zm/latex
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

初始

Browse Source
This commit is contained in:
wangjinlei
2026-02-10 17:57:57 +08:00
commit 1b9bc87e73
97 changed files with 54788 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

BIN
11.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 963 KiB

4
2.aux Normal file
View File

@@ -0,0 +1,4 @@
\relax
\newlabel{tab-381}{{1}{3}{}{table.1}{}}
\@writefile{lot}{\contentsline {table}{\numberline {1}{1111111}}{3}{}\protected@file@percent }
\gdef \@abspage@last{12}

8007
2.log Normal file
View File

File diff suppressed because it is too large Load Diff

BIN
2.pdf Normal file
View File

Binary file not shown.

BIN
2.synctex.gz Normal file
View File

Binary file not shown.

130
2.tex Normal file
View File

@@ -0,0 +1,130 @@
% !TEX program = lualatex
\documentclass{article}
\usepackage[a4paper, margin=2cm]{geometry}
\usepackage{tabularray}
\usepackage{textcomp}
\usepackage{amsmath}
\usepackage{xcolor}
% 定义偶数行颜色（仅保留样式，无其他复杂配置）
\definecolor{evenRowColor}{RGB}{250,231,232}
\begin{document}
\begin{longtblr}[
caption={1111111},
label={tab-381}
]{
colspec={X[0.7] X[0.7] X[0.8] X[0.7] X[1] X[0.3]},
long,
pagebreaks=auto,
rowhead=1,
hline{1}={1.5pt},
hline{2}={0.75pt},
hline{Z}={1.5pt},
row{1}={font=\bfseries, bg=white},
row{even[2]}={bg=evenRowColor},
cell{*}{*}={halign=l, valign=t,bg=white},
rowsep=3pt,
colsep=3pt,
vlines=0pt,
width=\textwidth
}
\textbf{Category} & \textbf{Flavonoids} & \textbf{Models} & \textbf{Dose} & \textbf{Mechanism} & \textbf{Ref.} \\
\SetCell[r=9]{l,bg=white} Chalcones & \SetCell[r=5]{l,bg=white} Phloretin & BALB/c mice were subjected to exposure to CS for 2 h twice/day, 6 days/week for 4 weeks. & 10, 20 mg/kg & Inflammatory cell\textdownarrow in BALF; IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow in BALF and lung; MUC5AC\textdownarrow in lung; p-EGFR\textdownarrow, p-ERK\textdownarrow, p-38\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{55} \\
& & CSE-induced NCI-H292 cells. & 1, 2.5, 5, 10 $\mu$M & IL-1$\beta$\textdownarrow, MUC5AC\textdownarrow; p-EGFR\textdownarrow, p-ERK\textdownarrow, p-38\textdownarrow & \\
& & 10${{6}}$ CFU non-typeable \textit{Haemophilus influenzae}, 10${{5}}$ CFU \textit{Moraxella catarrhalis}, 5 10${{5}}$ CFU \textit{Streptococcus pneumoniae}, and 10${{5}}$ CFU \textit{Pseudomonas aeruginosa} strain PAO1 were grown with phloretin. & 0.1-1 mM & Bacterial growth\textdownarrow, pathogen biofilm formation\textdownarrow & \SetCell[r=3]{l,bg=white} \parencite{56} \\
& & 310${{4}}$ NTHi infected NCI-H292 cells. & 0.1 mM & NTHi adhesion\textdownarrow & \\
& & FVB/NJ mice were diet supplemented with phloretin (1 week) and were exposed intratracheally to NTHi (24 h). & 0.157\% & NTHi burden\textdownarrow, CXCL1\textdownarrow & \\
& Isoliquiritigenin & C57BL/6N male mice were subjected to exposure to CS for 2 h twice/day for 4 weeks. & 10, 20, 30 mg/kg & Wet/Dry ratio\textdownarrow, inflammatory cell\textdownarrow; MPO\textdownarrow, TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow in BALF; MDA\textdownarrow in lung; p-p65\textdownarrow, p-I$\kappa$B\textdownarrow in lung; Nrf2\textuparrow, HO-1\textuparrow in lung & \parencite{57} \\
& \SetCell[r=3]{l,bg=white} Hydroxysafflor yellow A & MaleWistar rats were subjected to exposure to CS for 1 h daily for 4 weeks, except received intratracheal instillation of LPS on the 1${{st}}$ and 14${{th}}$ day. & 30, 48, 76.8 mg/kg & Wat/Pbm\textdownarrow TGF-$\beta$1\textdownarrow, $\alpha$-SMA\textdownarrow, CollagenI\textdownarrow in lung; TGF-$\beta$1\textdownarrow inplasma; p-p38\textdownarrow in lung & \parencite{58} \\
& & PAF-stimulated HSAECs. & 9, 27, 81 $\mu$M & IL-6\textdownarrow, IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow; PKC\textdownarrow, p-ERK\textdownarrow, p-JNK\textdownarrow, p-p38, p-I$\kappa$B\textdownarrow & \parencite{59} \\
& & MaleWistar rats were subjected to exposure to CS for 1 h/day for 4 weeks, except received intratracheal instillation of LPS on the 1${{st}}$ and 14${{th}}$ day. & 30, 48, 76.8 mg/kg & IL-6\textdownarrow, IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow in lung; ICAM-1\textdownarrow, VCAM-1\textdownarrow in lung; p-p38\textdownarrow, p65\textdownarrow in lung & \parencite{60} \\
\SetCell[r=16]{l,bg=white} Flavones & Apigenin & H$_{{2}}$O$_{{2}}$-induced senescence of WI-38 cell. & 10, 20 $\mu$M & Senescence\textdownarrow & \parencite{61} \\
& \SetCell[r=2]{l,bg=white} Oroxylin A & Male C57BL/6 were subjected to exposure to CS for 1 h/day for 4 days. & 6, 12, and 25 mg/kg & Inflammatory cell\textdownarrow in BALF; TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, MCP-1\textdownarrow in BALF; 3-NT\textdownarrow, 8-OHdG\textdownarrow, 8-isoprostane\textdownarrow; GSH\textuparrow, Nrf2\textuparrow, HO-1\textuparrow, GR\textuparrow, GPX-2\textuparrow & \SetCell[r=2]{l,bg=white} \parencite{62} \\
& & CSE-induced RAW264.7. & 50, 100, 150 $\mu$M & TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, Nrf2\textuparrow, HO-1\textuparrow & \\
& \SetCell[r=2]{l,bg=white} Scutellarein & RSL-3 or Erastin-induced human BEAS-2B cells. & 1, 2, 5 $\mu$M & LDH\textdownarrow, HMGB\textdownarrow; lipid ROS\textdownarrow, MDA\textdownarrow, GSH\textuparrow, GPX4\textuparrow, HMOX1\textdownarrow, ATF3\textdownarrow,\textit{ }Keap1\textuparrow, Nrf2\textdownarrow, HO-1\textdownarrow,\textit{ }p-JNK\textdownarrow, p-p38\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{63} \\
& & Male C57BL/6 mice were subjected to exposure to CS for 1 h/day for 6 weeks, except received intratracheal instillation of LPS on the 1${{st}}$ and 14${{th}}$ day. & 5, 10, 20 mg/kg & TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, IL-6\textdownarrow in serum; HMGB1\textdownarrow in BALF; MDA\textdownarrow, GPX4\textuparrow in lung tissues; HMOX1\textdownarrow, HO-1\textdownarrow & \\
& \SetCell[r=8]{l,bg=white} Quercetin & C57BL/6 mice were exposed to elastase and LPS on day four of the week for four weeks. & 10 mg/kg & Lung function\textuparrow; TBARS, Hmox-1, KC/CXCL-1\textdownarrow, MIP-2/CXCL-2\textdownarrow, MCP-1/CCL2\textdownarrow, IL-1$\beta$\textdownarrow, IL-12p40\textdownarrow, MIP-1$\beta$\textdownarrow, MUC5AC\textdownarrow, MMP-9\textdownarrow, MMP-12\textdownarrow, SIRT1\textuparrow & \parencite{64} \\
& & CSE-induced in U937 cells peripheral blood mononuclear cells (PBMC) collected from patients. & 10 $\mu$M & TNF$\alpha$\textdownarrow, CXCL8\textdownarrow\textit{, }p-AMPK\textuparrow, Nrf2\textuparrow, corticosteroid insensitivity\textuparrow & \parencite{65} \\
& & Female mice were subjected to whole body CS for 2h/day, 5 days/week for 8 weeks, and received intratracheal instillation of NTHi on the 7${{th}}$ and 21${{st}}$ day, and infected with RV. & 100 mg/kg & CXCL-1\textdownarrow, CXCL-10\textdownarrow, TNF-$\alpha$\textdownarrow, IFN-\textdownarrow and IL-17A\textdownarrow CD11b${{+}}$CD11c${{+}}$ macrophages\textdownarrow, neutrophils\textdownarrow, CD8${{+}}$T cells\textdownarrow & \parencite{66} \\
& & Male SD rats were subjected to exposure to CS for 1 h/day for 3 months. & 50 mg /kg & Lung function\textuparrow; Caspase-3\textdownarrow, Caspase-9\textdownarrow in lung; IL-6\textdownarrow, TNF-$\alpha$\textdownarrow, IL-10\textuparrow in serum; TGF-$\beta$1\textdownarrow, $\alpha$-SMA\textdownarrow in lung & \parencite{67} \\
& & COPD patients & 2,000 mg/day & Lung function\textuparrow & \parencite{68} \\
& & SD rats received intratracheal injections of LPS. & 50, 100, 200 mg/kg & MUC5AC\textdownarrow, EGFR\textdownarrow, PKC\textdownarrow, NF-$\kappa$B\textdownarrow\textit{, }p-PI3K\textdownarrow, p-AKT\textdownarrow, p-PKC\textdownarrow & \parencite{69} \\
& & C57BL/6 mice were subjected to CS 3 times/day for 60 days. & 10 mg/kg & Inflammatory cell\textdownarrow in BALF IL-10\textdownarrow, IL-13\textdownarrow, IL-17 \textuparrow, IL-22\textdownarrow in lung; SOD\textuparrow, CAT\textuparrow, GSH/ GSSG\textuparrow, MPO\textdownarrow, TBARS\textdownarrow in lung & \parencite{70} \\
& & Airway basal cells from COPD patients. & 1 $\mu$M & Occludin\textuparrow and E-cadherin\textuparrow IL-8\textdownarrow, IL-6\textdownarrow & \parencite{71} \\
& \SetCell[r=2]{l,bg=white} Fisetin & TNF-$\alpha$ induced HEK293T and NCI-H292 cells. & 2.5, 5, 10 $\mu$M & IL-8\textdownarrow & \parencite{72} \\
& & Male Wistar albino rats were subjected to exposure to CS for 1 h/day for 60 days. & 5, 25, 50 mg/kg & Macrophages and neutrophils\textdownarrow in BALF; MDA\textdownarrow, 3NT\textdownarrow, 8IP\textdownarrow, GSH\textuparrow, NO\textuparrow, SOD\textuparrow in lung; TNF$\alpha$\textdownarrow, GMCSF\textdownarrow, IL1$\beta$\textdownarrow, IL4\textdownarrow, IL10\textdownarrow in lung; HO1\textuparrow, Gpx2\textuparrow, Nrf2\textuparrow in lung & \parencite{73} \\
& Myricetin & TNF-$\alpha$-induced A549 cells. & 20, 40, 60 $\mu$M & IL-6\textdownarrow, IL-8\textdownarrow\textit{, }p-p65\textdownarrow, I$\kappa$B-$\alpha$\textdownarrow\textit{, }acetyl-p65, SIRT1\textuparrow & \parencite{74} \\
\SetCell[r=16]{l,bg=white} Flavones & \SetCell[r=4]{l,bg=white} Casticin & Female C57BL/6 mice were subjected to exposure to CS for 30 min four times a day for 14 d. & 1, 2, 10 mg/kg & Inflammatory cell\textdownarrow in BALF TNF-$\alpha$\textdownarrow, IL-6\textdownarrow, IL-1$\beta$\textdownarrow, KC\textdownarrow, MCP-1\textdownarrow in BALF & \parencite{75} \\
& & Male Wistar rats were subjected to exposure to CS twice/day for 85 days. & 10, 20, 30 mg/kg & Lung function\textuparrow; inflammatory cell\textdownarrow in plasma; leptin\textuparrow C-reactive protein\textdownarrow in plasma; MDA\textdownarrow, GSH\textuparrow, SOD\textuparrow\textit{, }TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, IL-6\textdownarrow\textit{, }TLR4\textdownarrow, p-NF-$\kappa$B\textdownarrow, p-I$\kappa$B$\alpha$\textuparrow in lung & \parencite{76} \\
& & LPS-induced BEAS-2B cells. & 0.5, 1, 2, 5, 10 $\mu$M & IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow, IL-6\textdownarrow, IFN-\textdownarrow\textit{, }apoptosis cell\textdownarrow, p-p65\textdownarrow, Nrf2\textuparrow, keap-1\textdownarrow & \parencite{77} \\
& & H$_{{2}}$O$_{{2}}$ induced BEAS-2B cells. & 0.5, 1, 2, 5, 10 $\mu$M & ROS\textdownarrow, SOD\textuparrow, MDA\textdownarrow\textit{, }Nrf2\textuparrow, HO-1\textuparrow, Keap-1\textdownarrow & \parencite{78} \\
& \SetCell[r=4]{l,bg=white} Luteolin & Male BALB/c mice were exposed first to 100 mg/m${{3}}$ CS for 15 days and then to 250 mg/m${{3}}$ CS for 5 days/week for 75 consecutive days (6 h/day). & 20, 40 mg/kg & Body weight, SOD\textuparrow, CAT\textuparrow, and MDA\textdownarrow in serum and BALF; IL6\textdownarrow, IL1$\beta$\textdownarrow, IL8\textdownarrow, TNF$\alpha$\textdownarrow in serum and BALF; NQO1\textuparrow, HO1\textuparrow; pp65\textdownarrow pI$\kappa$B\textdownarrow, NOX4\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{79} \\
& & CSE-treated A549 cells. & 100, 200 $\mu$M & SOD\textuparrow, CAT\textuparrow, MDA\textdownarrow, IL6\textdownarrow, IL1$\beta$\textdownarrow, IL8\textdownarrow, TNF$\alpha$\textdownarrow NQO1\textuparrow, HO1\textuparrow, pp65\textdownarrow pI$\kappa$B\textdownarrow, NOX4\textdownarrow & \\
& & CSE-treated A549 cells. & 10, 30 $\mu$M & SOD\textuparrow, LDH\textdownarrow, and MDA\textdownarrow, ROS\textdownarrow, mitochondrial ROS\textuparrow, Ca${{2+}}$ influx\textdownarrow, TRPV1\textdownarrow, SIRT6\textuparrow, CYP2A13\textdownarrow, NRF2\textuparrow, PGC1$\alpha$\textuparrow, SOD1\textuparrow, SOD2\textuparrow & \SetCell[r=2]{l,bg=white} \parencite{80} \\
& & C57BL/6J mice intratracheal injections of LPS on the 1${{st}}$ and 14${{th}}$ days of the trial. Mice were passively exposed for 15 min, twice/day, six days/week, for eight weeks. & 50, 100 mg/kg & Body weight\textuparrow, lung index\textdownarrow, SOD\textuparrow, LDH\textdownarrow, MDA\textdownarrow, CAT\textuparrow, GSH\textuparrow, TRPV1\textdownarrow, SIRT6\textuparrow, CYP2A13\textdownarrow, NRF2\textuparrow, PGC1$\alpha$\textuparrow, SOD1\textuparrow, SOD2\textuparrow & \\
& \SetCell[r=6]{l,bg=white} Naringenin & Hartley strain guinea pigs were subjected to exposure to CS 1 h/day, 6 days/week, for 8 weeks. & 9.2, 18.4, 36.8 mg/kg & Inflammatory cell\textdownarrow in BALF; SOD in lung, MPO in lung and BALF & \parencite{81} \\
& & BALB/c mice were subjected to exposure to CS for 1 h/day for 90 days. & 20, 40, 80 mg/kg & Lung function\textuparrow; TNF-$\alpha$\textdownarrow, IL-8\textdownarrow, MMP-9\textdownarrow in lung and BALF; p-p65\textdownarrow, I$\kappa$B-$\alpha$\textdownarrow, GR & \SetCell[r=2]{l,bg=white} \parencite{82} \\
& & CSE-induced A549. & 2, 20, 50 mM & TNF-$\alpha$\textdownarrow, IL-8\textdownarrow, GR & \\
& & RAW264.7 was treated by BEAS-2B-derived Evs. & 5.7 10${{8}}$ to 6.8 10${{8}}$ particles/m & M1 macrophage polarization\textdownarrow; IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow, IL-6\textdownarrow, iNOS\textdownarrow, IL-12\textdownarrow & \SetCell[r=3]{l,bg=white} \parencite{83} \\
& & MH-S macrophages were treated by BEAS-2B-derived Evs. & / & M1 macrophage polarization\textdownarrow & \\
& & THP-1 macrophages were treated by BEAS-2B-derived Evs. & / & M1 macrophage polarization\textdownarrow; IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow, IL-6\textdownarrow, iNOS\textdownarrow, IL-12\textdownarrow, miR-21-3p\textdownarrow, PTEN\textuparrow, p-AKT\textdownarrow & \\
& \SetCell[r=2]{l,bg=white} Hesperetin & C57BL/6 mice were intraperitoneally injected with CSE on days 1, 8, and 15. & 25, 50 mg/kg & SOD\textuparrow and CAT\textuparrow, IL-6\textdownarrow, IL-8\textdownarrow in BALF MPO\textdownarrow in lung; p-p65\textdownarrow, PGC-1a\textuparrow, SIRT1\textuparrow & \parencite{84} \\
& & Female ICR mice were subjected to exposure to CS 1 h twice/day and 6 days/week for 8 weeks. & 25, 50, 100 mg/kg & Lung function\textuparrow; AKT1\textdownarrow, IL-6\textdownarrow, VEGFA\textdownarrow, MMP-9\textdownarrow, TP53\textuparrow & \parencite{85} \\
\SetCell[r=2]{l,bg=white} Flavones & \SetCell[r=2]{l,bg=white} Taxifolin (dihydroquercetin) & Mice were subjected to exposure to CS 1 h twice/day, 6 days/week for 4 weeks with Days 0, 11, and 22; the mice were intraperitoneally injected with 0.3 mL/20 g 100\% CSE. & 50, 100 mg/kg & SLC7A11\textuparrow, GPx4\textuparrow, MDA\textdownarrow, SOD\textuparrow & \parencite{86} \\
& & CSE-treated HBE cells. & 40, 80 $\mu$M & MDA\textdownarrow, SOD\textuparrow, SLC7A11\textuparrow, GPx4\textuparrow, Nrf2\textuparrow, lipid peroxidation\textdownarrow & \parencite{87} \\
\SetCell[r=14]{l,bg=white} Flavone glycosides & \SetCell[r=14]{l,bg=white} Baicalin & Male SD rats were subjected to exposure to CS for 40 min, twice/day, 5 days/week, for 5 weeks. & 20, 40, 80 mg/kg & Lung function\textuparrow; inflammatory cell\textdownarrow in BALF; IL-6\textdownarrow, IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow in serum and BALF; p65\textdownarrow in lung & \SetCell[r=2]{l,bg=white} \parencite{88} \\
& & CSEtreated human type II pneumocytes. & 5, 10, 20 mM & IL-6\textdownarrow, IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow\textit{, }p-p65\textdownarrow, I$\kappa$B-$\alpha$\textuparrow & \\
& & Male BALB/c mice exposed CS for 1 h/day, 6 days/week for 3 months. & 25, 50, 100 mg/kg & Lung function\textuparrow; IL-8\textdownarrow, MMP-8\textdownarrow, TNF-$\alpha$\textdownarrow in serum and BALF; HDAC2\textuparrow, p-HDAC2\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{89} \\
& & CSE-treated A549 cells. & 10, 50, 100 $\mu$M & IL-8\textdownarrow, HDAC2\textuparrow & \\
& & SD rats were subjected to exposure to CS for 1 h, 3 times/day, 6 days/week for 36 months. & 40, 80, 160 mg/kg & Lung function\textuparrow; IL-1$\beta$\textdownarrow, IL-6\textdownarrow, and IL-10\textuparrow\textit{, }TNF-$\alpha$\textdownarrow, IL-17, MMP-2\textdownarrow, MMP-9\textdownarrow, TIMP-1\textuparrow\textit{, }MDA\textdownarrow, T-AOC\textuparrow, SOD\textuparrow, HO-1\textdownarrow\textit{, }blood pH and PaO2 & \parencite{90} \\
& & Male C57BL/6N mice were subjected to exposure to CS of 1 h for 7 days and administered LPS via nasal instillation. & 25, 50, 100 mg/kg & MUC5AC\textdownarrow, IL-6\textdownarrow, IL-8\textdownarrow, TNF-$\alpha$\textdownarrow in the BALF; JNK\textdownarrow but HSP72 inhibitor revise all & \SetCell[r=3]{l,bg=white} \parencite{91} \\
& & CSE-treated MLE-12 cells. & 5, 10, 20 $\mu$M & HSP72\textdownarrow, apoptosis cell\textdownarrow & \\
& & Si-HSP72 treated MLE-12 cells. & / & Apoptosis cell\textuparrow, IL-6\textuparrow, IL-8\textuparrow, TNF-$\alpha$\textuparrow & \\
& & SD rats exposed CS for 1 h/day, 6 days/week, for 24 weeks. & 40, 80,160 mg/kg & Lung function\textuparrow TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow in lung and serum; p-p65\textdownarrow p-I$\kappa$B-$\alpha$\textuparrow\textit{, }HDAC2\textuparrow PAI-1\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{92} \\
& & CSEtreated HBE cells. & 10, 20, 40 $\mu$M & Cells viability\textuparrow; TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, p-p65\textdownarrow, p-I$\kappa$B-$\alpha$\textuparrow\textit{, }HDAC2\textuparrow, PAI-1\textdownarrow & \\
& & LPS-treated HBE cells. & 10, 50, 100 $\mu$M & Cell apoptosis\textdownarrow; TNF-$\alpha$\textdownarrow, IL-6\textdownarrow\textit{, }P21\textdownarrow, Bax\textdownarrow, cyclinD1\textuparrow, Bcl2\textuparrow\textit{, }miR4451\textuparrow & \parencite{93} \\
& & IL-1$\beta$treated NCI-H292 cells. & 200 $\mu$g/mL & IL-8\textdownarrow, TNF-$\alpha$\textdownarrow, p65\textdownarrow, I$\kappa$B\textuparrow, MUC5AC\textdownarrow, CFTR\textuparrow & \parencite{94} \\
& & Male SD rats were subjected to exposure to CS for 1 h/day for 4 weeks, except received intratracheal instillation of LPS on the 1${{st}}$ and 14${{th}}$ day. & 40, 80, 160 mg/kg & Lung function\textuparrow; TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, IL-6\textdownarrow, IL-8\textdownarrow, IL-10\textdownarrow in serum; GSH\textuparrow, SOD\textuparrow, MDA\textdownarrow in serum; TLR2\textdownarrow, MYD88\textdownarrow, TNF-$\alpha$\textdownarrow, and IL-1$\beta$\textdownarrow in BALF cells; NF-$\kappa$B\textdownarrow, TLR2\textdownarrow, MYD88\textdownarrow in lung; MYD88\textdownarrow, p-p65\textdownarrow, p-I$\kappa$Ba\textuparrow, TLR2\textdownarrow, TLR4\textdownarrow in lung & \parencite{95} \\
& & CSE-treated 16HBE cells. & 10 $\mu$M & Cells proliferation\textuparrow; Cell apoptosis\textdownarrow; TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, IL-6\textdownarrow, IL-8\textdownarrow; miR-125a\textdownarrow & \parencite{96} \\
\SetCell[r=13]{l,bg=white} Flavone glycosides & \SetCell[r=2]{l,bg=white} Astragalin & Male BALB/c mice were subjected to exposure to CS for 30 min once a day for 8 weeks. & 10, 20 mg/kg & Inflammatory cell\textdownarrow in BALF; PAR-1\textdownarrow, PAR-2\textdownarrow, Tpa\textuparrow, PAI-1\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{97} \\
& & Thrombin-exposed A549 cells. & 1, 10, 20 $\mu$M & PAR-1\textdownarrow and PAR-2\textdownarrow, Tpa\textuparrow, PAI-1\textdownarrow Upa\textdownarrow\textit{, }ICAM-1\textdownarrow, COX-2\textdownarrow, iNOS\textdownarrow\textit{, }p-P38\textdownarrow, p-JNK\textdownarrow, p-ERK\textdownarrow & \\
& \SetCell[r=4]{l,bg=white} Icariin & CSE-treated BEAS2B cells. & 20, 40, 80 $\mu$M & IL8\textdownarrow, TNF$\alpha$\textdownarrow, IL-10\textuparrow; ROS\textdownarrow, MMP-9\textdownarrow, TIMP1\textuparrow\textit{, }GR\textuparrow, HDAC2\textuparrow, NF-$\kappa$B\textdownarrow, Nrf2\textuparrow & \parencite{98} \\
& & SD rats were subjected to exposure to CS for 40 min, 5 days/week, 8 weeks. & 2.12 mg/kg (icariin to nobiletin 12.5:1) & Lung function\textuparrow; IL-6\textdownarrow, IL-1$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow in lung; PI3K\textdownarrow, p-AKT\textdownarrow, p-p38\textdownarrow in lung & \parencite{99} \\
& & CSE-treated NCI-H292 cells and BEAS-2B cells. & 25, 50, 75 $\mu$M & PPAR\textuparrow\textit{, }NF-$\kappa$B p65/lamin-B\textdownarrow, NF-$\kappa$B p65\textuparrow, I$\kappa$B-$\alpha$\textuparrow,\textit{ }IL-6\textdownarrow, TNF-$\alpha$\textdownarrow in BALF or serum & \SetCell[r=2]{l,bg=white} \parencite{100} \\
& & Male SD rats were subjected to exposure to CS. & 40 mg/kg & Lung function\textuparrow\textit{, }PPAR\textuparrow\textit{, }p65\textdownarrow, I$\kappa$B-$\alpha$\textdownarrow & \\
& \SetCell[r=2]{l,bg=white} Liquiritin apioside & Female ICR mice were subjected to exposure to CS for four days. & 3, 10, 30 mg/kg & Inflammatory cell\textdownarrow in BALF; MPO\textdownarrow in lung; TNF-$\alpha$\textdownarrow, TGF-$\beta$\textdownarrow, SOD\textuparrow & \SetCell[r=2]{l,bg=white} \parencite{101} \\
& & CSE-induced A549 cell. & 0.1, 1 $\mu$M & TGF-$\beta$\textdownarrow, TNF-$\alpha$\textdownarrow, apoptosis cell\textdownarrow & \\
& \SetCell[r=4]{l,bg=white} Tilianin & EGF-simulated NCI-H292 cell. & 5, 10, 25, 50 $\mu$M & P-AKT\textdownarrow, p-p38\textdownarrow, p-ERK\textdownarrow, p-MEK\textdownarrow, MUC5AC\textdownarrow, p-Sp1\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{102} \\
& & EGF-simulated A549 cell. & 5, 10, 25, 50 $\mu$M & P-AKT, p-p38, p-ERK, p-MEK, p-Sp1\textdownarrow & \\
& & Male BALB/c mice were administered 100 $\mu$L of CFD in saline by intratracheal instillation thrice at 3-day intervals for 12 days using bronchial tubes. & 10 mg/kg & CXCL2\textdownarrow, IL-17A\textdownarrow, CXCL1\textdownarrow, and TNF-$\alpha$\textdownarrow in BALF; SDMA\textdownarrow in serum; CXCL2\textdownarrow, IL-17A\textdownarrow, CXCL1\textdownarrow, MUC5AC\textdownarrow, IL-6\textdownarrow, TNF-$\alpha$\textdownarrow NOS-II\textdownarrow, TRPV1\textdownarrow, STAT3\textdownarrow in lung; neutrophil infiltration\textdownarrow in lung and BALF & \SetCell[r=2]{l,bg=white} \parencite{103} \\
& & CFA-induced the mouse epithelial cell line LA-4 and human mast-cell line HMC-1. & 50 $\mu$g/mL & CXCL2\textdownarrow, IL-17A\textdownarrow & \\
& Naringin & Hartley strain guinea pigs were subjected to exposure to CS 1 h/day, 6 days/week, for 8 weeks. & 18.4 mg/kg & Cough\textdownarrow, pause\textdownarrow; SP\textdownarrow, NEP activity \textuparrow, NK-1 receptor\textdownarrow & \parencite{104} \\
\SetCell[r=3]{l,bg=white} Isoflavones & Biochanin A & Male SD rats received intratracheal instillation of PM$_{{2.5}}$ once every five days for five times. & 25, 50, 100 mg/kg & SOD\textuparrow, CAT\textuparrow in lung; GSH-Px\textuparrow, ALB\textdownarrow in serum; TNF-$\alpha$\textdownarrow, IL-2\textdownarrow, IL-6\textdownarrow, IL-8\textdownarrow in lung; MDA\textdownarrow in serum; AKP\textdownarrow, LDH\textdownarrow in BALF; XRCC1\textdownarrow, MAP2K5\textdownarrow, IGJ\textdownarrow & \parencite{105} \\
& \SetCell[r=2]{l,bg=white} Puerarin & CSE-induced HBE cell. & 50, 100, 200 $\mu$g/mL & Apoptosis\textdownarrow, cleaved caspase3\textdownarrow, Bax\textdownarrow; MMP level\textuparrow mitochondrial ROS\textdownarrow\textit{, }ATP\textuparrow\textit{, }PINK1\textdownarrow, Parkin\textdownarrow, DRP1\textdownarrow, FUNDC1\textdownarrow, p-FUNDC1\textuparrow & \parencite{106} \\
& & Male Wistar rats were subjected to exposure to CS for 1 h/day for 4 weeks, except received intratracheal instillation of LPS on the 1${{st}}$ and 14${{th}}$ day. & 50, 100, 200 mg/kg & Lung function\textuparrow\textit{, }TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, IL-6\textdownarrow in BALF; apoptosis\textdownarrow in lung; BECN1\textdownarrow, LC3B\textdownarrow in lung; PINK1\textdownarrow, parkin\textdownarrow, Bax\textdownarrow, Bcl-2\textuparrow in lung & \parencite{107} \\
\SetCell[r=2]{l,bg=white} Isoflavones & \SetCell[r=2]{l,bg=white} Formononetin & Male C57BL/6J mice were subjected to exposure to CS of 9 cigarettes/h, 4 h/day, 6 days/week for 24 weeks. & 50 mg/kg & Total cells\textdownarrow, macrophages\textdownarrow, and neutrophils\textdownarrow in BALF; TNF-$\alpha$\textdownarrow, CXCL1\textdownarrow in BALF; IL-10\textdownarrow, CCL22\textdownarrow in lung; Apoptosis: Bcl-2\textuparrow, Bax\textdownarrow, cleaved caspase 3\textdownarrow in lung; Endoplasmic reticulum stress: GRP78\textdownarrow, CHOP\textdownarrow, ATF6\textdownarrow, p-ERK\textdownarrow, p-EIF2$\alpha$\textdownarrow, AhR\textdownarrow, CYP1A1 \textdownarrow, p-AKT\textdownarrow, p-mTOR\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{108} \\
& & CSE-exposed BEAS-2B. & 50 $\mu$M FMN & Inflammation: TNF-$\alpha$\textdownarrow, IL-1$\beta$\textdownarrow, IL-8\textdownarrow Apoptosis: apoptotic cells\textdownarrow, Bax\textdownarrow, cleaved caspase\textdownarrow ER stress: GRP78\textdownarrow, CHOP\textdownarrow, ATF6\textdownarrow, p-EIF2$\alpha$\textdownarrow, p-ERK\textdownarrow, AhR\textdownarrow, CYP1A1\textdownarrow, p-AKT\textdownarrow, p-mTOR\textdownarrow & \\
\SetCell[r=3]{l,bg=white} Flavanols & \SetCell[r=2]{l,bg=white} -Epicatechin & Wistar male rats were subjected to exposure to CS for 1 h twice/day for 12 weeks. & 5, 15, 45 mg/kg & MDA\textdownarrow, IL-1$\beta$\textdownarrow in BALF; HO-1\textuparrow, NQO1\textuparrow, SOD1\textuparrow, SOD2\textuparrow, SOD3\textuparrow\textit{, }NLRP3\textdownarrow, GSDMD-N\textdownarrow, caspase-1\textdownarrow,\textit{ }IL-18, IL-1$\beta$ & \SetCell[r=2]{l,bg=white} \parencite{109} \\
& & CSE-induced BEAS-2B cells. & 10, 20, 50 $\mu$M & MDA\textdownarrow\textit{, }Keap1\textdownarrow, Nrf2\textuparrow, HO-1\textuparrow, NQO1\textuparrow\textit{, }NLRP3, GSDMD-N, caspase-1, IL-18, IL-1$\beta$ & \\
& Epigallocatechin gallate & CSE-induced NHBE cell. & 5, 10, 20 $\mu$M & ROS\textdownarrow, lipid peroxidation\textdownarrow\textit{, }4-HNE-protein adduct\textdownarrow\textit{, }p65\textdownarrow\textit{, }COX-2\textdownarrow, NOX4\textdownarrow, NOS2\textdownarrow, IL-6\textdownarrow, IL-8\textdownarrow & \parencite{110} \\
\SetCell[r=2]{l,bg=white} Bisflavonoid & \SetCell[r=2]{l,bg=white} Ginkgetin & C57 mice were subjected to exposure to CS of 0.5 h twice/day for 10 weeks, and LPS airway instillation on days 1, 14, 28, and 42 d. & 10, 40 mg/kg & IL-6\textdownarrow, IL-17A \textdownarrow, TNF-$\alpha$\textdownarrow, in BALF; FEV0.1/FVC\textuparrow & \SetCell[r=2]{l,bg=white} \parencite{115} \\
& & CSE-induced A549 cell. & 50, 100, and 250 $\mu$g/mL & C/EBP$\beta$\textdownarrow, CCL2\textdownarrow & \\
\SetCell[r=5]{l,bg=white} Other Flavonoids & \SetCell[r=3]{l,bg=white} Silibinin & Male C57BL/6N mice were subjected to exposure to CS of 1 h/day for 7 days. & 25, 40\textit{ }mg/kg & Inflammatory cell\textdownarrow; IL1$\beta$\textdownarrow, IL6\textdownarrow, MUC5AC\textdownarrow in BALF; p-ERK\textdownarrow, SP1\textdownarrow & \SetCell[r=2]{l,bg=white} \parencite{111} \\
& & CSE-induced NCI-H292 cells. & 6.25, 12.5, 25, 50 $\mu$M & IL6\textdownarrow, MUC5AC\textdownarrow, p-ERK\textdownarrow, SP1\textdownarrow & \\
& & Male C57BL/6N mice were subjected to exposure to CS of 1 h/day for 4 weeks. & 10, 20 mg/kg & Inflammatory cell\textdownarrow,\textit{ }IL1$\beta$\textdownarrow, IL6\textdownarrow, TNF-$\alpha$\textdownarrow in BALF; TGF-$\beta$1\textdownarrow, collagen\textdownarrow, p-Smad 2/3\textdownarrow & \parencite{112} \\
& \SetCell[r=2]{l,bg=white} Silymarin & BALB/c mice were subjected to exposure to CS of 2 h twice/day, 6 days/week for 4 weeks. & 25, 50 mg/kg & Inflammatory cell\textdownarrow; IL1$\beta$\textdownarrow, IL6\textdownarrow, TNF-$\alpha$\textdownarrow in BALF; p-p38\textdownarrow, p-ERK\textdownarrow\textit{, }MDA\textdownarrow, SOD\textdownarrow & \parencite{113} \\
& & CSE-exposed BEAS-2B. & 10, 20 $\mu$M & P-p38\textdownarrow, p-ERK\textdownarrow\textit{, }LC3II/I\textdownarrow & \parencite{114} \\
\SetCell[r=3]{l,bg=white} Total flavonoids & Total flavonoids of \textit{Nigella glandulifera }Freyn et Sint. & Papain nebulization inhalation for 4 weeks, and LPS airway instillation on the 1${{st}}$ and 15${{th}}$ days. & 71.5, 286 mg/kg & Inflammatory cell\textdownarrow, TNF-$\alpha$\textdownarrow, IL-8\textdownarrow, NF-$\kappa$B\textdownarrow in BALF & \parencite{116} \\
& \SetCell[r=2]{l,bg=white} Total flavonoids of sea buckthorn & Male ICR mice were subjected to exposure to CS for 1 h/day for 30 days and received intratracheal instillation of LPS on the 1${{st}}$ and 14${{th}}$ day. & 100, 200, 500 mg/kg & Inflammatory cell\textdownarrow in BALF; IL1$\beta$\textdownarrow, IL6\textdownarrow, COX2\textdownarrow, MUC5AC\textdownarrow, CXCL1\textdownarrow in lung & \SetCell[r=2]{l,bg=white} \parencite{117} \\
& & LPS/CSEactivated HBE16 cells. & 10, 20, and 50 $\mu$g/mL & IL1$\beta$\textdownarrow, IL6\textdownarrow, CXCL1\textdownarrow\textit{, }MUC5AC\textdownarrow, PGE2\textdownarrow\textit{, }p-ERK\textdownarrow, p-Akt\textdownarrow, p-PKC\textdownarrow & \\
\SetCell[r=5]{l,bg=white} Total flavonoids & Total flavonoids from \textit{Scutellaria Baicalensis }Georgi & Male ICR mice were subjected to exposure to CS for 1 h/day for 28 days and received intratracheal instillation of LPS on the 1${{st}}$ and 14${{th}}$ day. & 25, 50 mg/kg & Inflammatory cell\textdownarrow, IL-6\textdownarrow, IL-8\textdownarrowin BALF; MDA\textdownarrow, SOD\textuparrow, SIRT1\textuparrow, PGC-1$\alpha$\textuparrow in lung & \parencite{118} \\
& Total flavonoids of Loquat leaves & Male C57BL/6 mice were subjected to exposure to CS four times/day, 5 days/week for 12 weeks. & 50, 100 mg/kg & Body weight\textuparrow and lung index\textdownarrow; TRPV1\textdownarrow in lung; IL-1$\beta$\textdownarrow, IL-6\textdownarrow, TNF-$\alpha$\textdownarrow, NO\textdownarrow, SOD\textuparrow, MDA\textdownarrow in the serum; p-IKK\textdownarrow, p-I$\kappa$B\textdownarrow, p-p65\textdownarrow, p-Akt\textuparrow, iNOS\textdownarrow\textit{, }CYP2E1\textdownarrow, p-JNK\textdownarrow SOD-2\textuparrow & \parencite{119} \\
& Total flavonoids of \textit{Trollius altaicus} & Male Wistar rats were subjected to exposure to CS for 1 h/day for 45 days and received intratracheal instillation of LPS on the 1${{st}}$ and 15${{th}}$ day. & 125, 250, 500 mg/kg & Lung function\textuparrow, W/D\textdownarrow; inflammatory cell\textdownarrow in blood; IL-1$\beta$\textdownarrow, IL-6\textdownarrow, IL-8\textdownarrow, IL-10\textuparrow, TNF-$\alpha$\textdownarrow, TGF-1$\beta$\textdownarrow in BALF; TLR4\textdownarrow, IKK$\alpha$\textdownarrow, p65\textdownarrow, IL-1$\beta$\textdownarrow in lung; IRAK-1\textdownarrow, IKK$\alpha$\textdownarrow, p65\textdownarrow, IL-1$\beta$\textdownarrowin lung & \parencite{120} \\
& Total flavonoids of \textit{Trollius altaicus} & Old male C57BL/6 mice were subjected to exposure to CS for 40 min twice/day for 4 weeks and received intraperitoneal injections of CSE on the 1${{st}}$, 12${{th}}$, and 23${{rd}}$ days. & 125, 250, 500 mg/kg & P-p38 MAPK\textdownarrow, p-ERK1/2\textdownarrow in lung & \parencite{121} \\
& Total flavonoids of Dandelion & Female BALB/c mice were subjected to exposure to CS for 1 h/day for 12 weeks. & 2, 25, 4.5 g/kg & Lung function\textuparrow; SOD\textuparrow, GSH\textuparrow, MDA\textdownarrow\textit{, }Nrf2\textuparrow, SOD1\textuparrow, HO-1\textuparrow & \parencite{122} \\
\end{longtblr}
\vspace{0.5em}
\par\small\noindent{2222222}
\end{document}

BIN
Desktop.ini Normal file
View File

Binary file not shown.

1
HEAD Normal file
View File

@@ -0,0 +1 @@
ref: refs/heads/master

745
article_3113.aux Normal file
View File

@@ -0,0 +1,745 @@
\relax
\providecommand\zref@newlabel[2]{}
\providecommand\hyper@newdestlabel[2]{}
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\abx@aux@refcontext{none/global//global/global/global}
\abx@aux@cite{0}{ref_132721}
\abx@aux@segm{0}{0}{ref_132721}
\abx@aux@cite{0}{ref_132722}
\abx@aux@segm{0}{0}{ref_132722}
\abx@aux@cite{0}{ref_132735}
\abx@aux@segm{0}{0}{ref_132735}
\abx@aux@cite{0}{ref_132735}
\abx@aux@segm{0}{0}{ref_132735}
\abx@aux@cite{0}{ref_132736}
\abx@aux@segm{0}{0}{ref_132736}
\abx@aux@cite{0}{ref_132737}
\abx@aux@segm{0}{0}{ref_132737}
\abx@aux@cite{0}{ref_132747}
\abx@aux@segm{0}{0}{ref_132747}
\abx@aux@cite{0}{ref_132748}
\abx@aux@segm{0}{0}{ref_132748}
\abx@aux@cite{0}{ref_132749}
\abx@aux@segm{0}{0}{ref_132749}
\abx@aux@cite{0}{ref_132750}
\abx@aux@segm{0}{0}{ref_132750}
\abx@aux@cite{0}{ref_132751}
\abx@aux@segm{0}{0}{ref_132751}
\abx@aux@cite{0}{ref_132752}
\abx@aux@segm{0}{0}{ref_132752}
\abx@aux@cite{0}{ref_132753}
\abx@aux@segm{0}{0}{ref_132753}
\abx@aux@cite{0}{ref_132756}
\abx@aux@segm{0}{0}{ref_132756}
\abx@aux@cite{0}{ref_132757}
\abx@aux@segm{0}{0}{ref_132757}
\abx@aux@cite{0}{ref_132758}
\abx@aux@segm{0}{0}{ref_132758}
\abx@aux@cite{0}{ref_132759}
\abx@aux@segm{0}{0}{ref_132759}
\abx@aux@cite{0}{ref_132760}
\abx@aux@segm{0}{0}{ref_132760}
\abx@aux@cite{0}{ref_132761}
\abx@aux@segm{0}{0}{ref_132761}
\abx@aux@cite{0}{ref_132762}
\abx@aux@segm{0}{0}{ref_132762}
\abx@aux@cite{0}{ref_132763}
\abx@aux@segm{0}{0}{ref_132763}
\zref@newlabel{mdf@pagelabel-1}{\default{}\page{2}\abspage{2}\mdf@pagevalue{2}}
\@writefile{toc}{\contentsline {section}{\numberline {1}\textit {\textbf {Introduction}}}{2}{section.1}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {2}\textit {\textbf {Pathogenesis of COPD}}}{2}{section.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}\textbf {Oxidative stress}}{2}{subsection.2.1}\protected@file@percent }
\abx@aux@cite{0}{ref_132764}
\abx@aux@segm{0}{0}{ref_132764}
\abx@aux@cite{0}{ref_132765}
\abx@aux@segm{0}{0}{ref_132765}
\abx@aux@cite{0}{ref_132768}
\abx@aux@segm{0}{0}{ref_132768}
\abx@aux@cite{0}{ref_132769}
\abx@aux@segm{0}{0}{ref_132769}
\abx@aux@cite{0}{ref_132754}
\abx@aux@segm{0}{0}{ref_132754}
\abx@aux@cite{0}{ref_132755}
\abx@aux@segm{0}{0}{ref_132755}
\abx@aux@cite{0}{ref_132770}
\abx@aux@segm{0}{0}{ref_132770}
\abx@aux@cite{0}{ref_132771}
\abx@aux@segm{0}{0}{ref_132771}
\abx@aux@cite{0}{ref_132774}
\abx@aux@segm{0}{0}{ref_132774}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}\textbf {Inflammation and inflammatory cells}}{3}{subsection.2.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}\textbf {Fibrosis}}{3}{subsection.2.3}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {3}\textit {\textbf {Flavonoids in COPD regulation}}}{3}{section.3}\protected@file@percent }
\abx@aux@cite{0}{ref_132775}
\abx@aux@segm{0}{0}{ref_132775}
\abx@aux@cite{0}{ref_132776}
\abx@aux@segm{0}{0}{ref_132776}
\abx@aux@cite{0}{ref_132777}
\abx@aux@segm{0}{0}{ref_132777}
\abx@aux@cite{0}{ref_132778}
\abx@aux@segm{0}{0}{ref_132778}
\abx@aux@cite{0}{ref_132779}
\abx@aux@segm{0}{0}{ref_132779}
\abx@aux@cite{0}{ref_132780}
\abx@aux@segm{0}{0}{ref_132780}
\abx@aux@cite{0}{ref_132781}
\abx@aux@segm{0}{0}{ref_132781}
\abx@aux@cite{0}{ref_132782}
\abx@aux@segm{0}{0}{ref_132782}
\abx@aux@cite{0}{ref_132783}
\abx@aux@segm{0}{0}{ref_132783}
\abx@aux@cite{0}{ref_132784}
\abx@aux@segm{0}{0}{ref_132784}
\abx@aux@cite{0}{ref_132785}
\abx@aux@segm{0}{0}{ref_132785}
\abx@aux@cite{0}{ref_132786}
\abx@aux@segm{0}{0}{ref_132786}
\abx@aux@cite{0}{ref_132787}
\abx@aux@segm{0}{0}{ref_132787}
\abx@aux@cite{0}{ref_132788}
\abx@aux@segm{0}{0}{ref_132788}
\abx@aux@cite{0}{ref_132789}
\abx@aux@segm{0}{0}{ref_132789}
\abx@aux@cite{0}{ref_132791}
\abx@aux@segm{0}{0}{ref_132791}
\abx@aux@cite{0}{ref_132792}
\abx@aux@segm{0}{0}{ref_132792}
\abx@aux@cite{0}{ref_132793}
\abx@aux@segm{0}{0}{ref_132793}
\abx@aux@cite{0}{ref_132794}
\abx@aux@segm{0}{0}{ref_132794}
\abx@aux@cite{0}{ref_132795}
\abx@aux@segm{0}{0}{ref_132795}
\abx@aux@cite{0}{ref_132796}
\abx@aux@segm{0}{0}{ref_132796}
\abx@aux@cite{0}{ref_132797}
\abx@aux@segm{0}{0}{ref_132797}
\abx@aux@cite{0}{ref_132799}
\abx@aux@segm{0}{0}{ref_132799}
\abx@aux@cite{0}{ref_132801}
\abx@aux@segm{0}{0}{ref_132801}
\abx@aux@cite{0}{ref_132802}
\abx@aux@segm{0}{0}{ref_132802}
\abx@aux@cite{0}{ref_132803}
\abx@aux@segm{0}{0}{ref_132803}
\abx@aux@cite{0}{ref_132804}
\abx@aux@segm{0}{0}{ref_132804}
\abx@aux@cite{0}{ref_132805}
\abx@aux@segm{0}{0}{ref_132805}
\abx@aux@cite{0}{ref_132806}
\abx@aux@segm{0}{0}{ref_132806}
\abx@aux@cite{0}{ref_132807}
\abx@aux@segm{0}{0}{ref_132807}
\abx@aux@cite{0}{ref_132808}
\abx@aux@segm{0}{0}{ref_132808}
\abx@aux@cite{0}{ref_132809}
\abx@aux@segm{0}{0}{ref_132809}
\abx@aux@cite{0}{ref_132810}
\abx@aux@segm{0}{0}{ref_132810}
\abx@aux@cite{0}{ref_132811}
\abx@aux@segm{0}{0}{ref_132811}
\abx@aux@cite{0}{ref_132812}
\abx@aux@segm{0}{0}{ref_132812}
\abx@aux@cite{0}{ref_132813}
\abx@aux@segm{0}{0}{ref_132813}
\abx@aux@cite{0}{ref_132814}
\abx@aux@segm{0}{0}{ref_132814}
\abx@aux@cite{0}{ref_132815}
\abx@aux@segm{0}{0}{ref_132815}
\abx@aux@cite{0}{ref_132816}
\abx@aux@segm{0}{0}{ref_132816}
\abx@aux@cite{0}{ref_132818}
\abx@aux@segm{0}{0}{ref_132818}
\abx@aux@cite{0}{ref_132819}
\abx@aux@segm{0}{0}{ref_132819}
\abx@aux@cite{0}{ref_132820}
\abx@aux@segm{0}{0}{ref_132820}
\abx@aux@cite{0}{ref_132821}
\abx@aux@segm{0}{0}{ref_132821}
\abx@aux@cite{0}{ref_132822}
\abx@aux@segm{0}{0}{ref_132822}
\abx@aux@cite{0}{ref_132823}
\abx@aux@segm{0}{0}{ref_132823}
\abx@aux@cite{0}{ref_132824}
\abx@aux@segm{0}{0}{ref_132824}
\abx@aux@cite{0}{ref_132825}
\abx@aux@segm{0}{0}{ref_132825}
\abx@aux@cite{0}{ref_132826}
\abx@aux@segm{0}{0}{ref_132826}
\abx@aux@cite{0}{ref_132827}
\abx@aux@segm{0}{0}{ref_132827}
\abx@aux@cite{0}{ref_132828}
\abx@aux@segm{0}{0}{ref_132828}
\abx@aux@cite{0}{ref_132829}
\abx@aux@segm{0}{0}{ref_132829}
\abx@aux@cite{0}{ref_132830}
\abx@aux@segm{0}{0}{ref_132830}
\abx@aux@cite{0}{ref_132831}
\abx@aux@segm{0}{0}{ref_132831}
\abx@aux@cite{0}{ref_132832}
\abx@aux@segm{0}{0}{ref_132832}
\abx@aux@cite{0}{ref_132833}
\abx@aux@segm{0}{0}{ref_132833}
\abx@aux@cite{0}{ref_132834}
\abx@aux@segm{0}{0}{ref_132834}
\abx@aux@cite{0}{ref_132835}
\abx@aux@segm{0}{0}{ref_132835}
\abx@aux@cite{0}{ref_132836}
\abx@aux@segm{0}{0}{ref_132836}
\abx@aux@cite{0}{ref_132837}
\abx@aux@segm{0}{0}{ref_132837}
\abx@aux@cite{0}{ref_132838}
\abx@aux@segm{0}{0}{ref_132838}
\abx@aux@cite{0}{ref_132839}
\abx@aux@segm{0}{0}{ref_132839}
\abx@aux@cite{0}{ref_132840}
\abx@aux@segm{0}{0}{ref_132840}
\abx@aux@cite{0}{ref_132841}
\abx@aux@segm{0}{0}{ref_132841}
\abx@aux@cite{0}{ref_132842}
\abx@aux@segm{0}{0}{ref_132842}
\abx@aux@cite{0}{ref_132843}
\abx@aux@segm{0}{0}{ref_132843}
\abx@aux@cite{0}{ref_132844}
\abx@aux@segm{0}{0}{ref_132844}
\abx@aux@cite{0}{ref_132846}
\abx@aux@segm{0}{0}{ref_132846}
\abx@aux@cite{0}{ref_132848}
\abx@aux@segm{0}{0}{ref_132848}
\abx@aux@cite{0}{ref_132775}
\abx@aux@segm{0}{0}{ref_132775}
\abx@aux@cite{0}{ref_132776}
\abx@aux@segm{0}{0}{ref_132776}
\abx@aux@cite{0}{ref_132777}
\abx@aux@segm{0}{0}{ref_132777}
\abx@aux@cite{0}{ref_132778}
\abx@aux@segm{0}{0}{ref_132778}
\abx@aux@cite{0}{ref_132779}
\abx@aux@segm{0}{0}{ref_132779}
\abx@aux@cite{0}{ref_132780}
\abx@aux@segm{0}{0}{ref_132780}
\abx@aux@cite{0}{ref_132781}
\abx@aux@segm{0}{0}{ref_132781}
\abx@aux@cite{0}{ref_132782}
\abx@aux@segm{0}{0}{ref_132782}
\abx@aux@cite{0}{ref_132783}
\abx@aux@segm{0}{0}{ref_132783}
\abx@aux@cite{0}{ref_132784}
\abx@aux@segm{0}{0}{ref_132784}
\abx@aux@cite{0}{ref_132785}
\abx@aux@segm{0}{0}{ref_132785}
\abx@aux@cite{0}{ref_132786}
\abx@aux@segm{0}{0}{ref_132786}
\abx@aux@cite{0}{ref_132787}
\abx@aux@segm{0}{0}{ref_132787}
\abx@aux@cite{0}{ref_132788}
\abx@aux@segm{0}{0}{ref_132788}
\abx@aux@cite{0}{ref_132789}
\abx@aux@segm{0}{0}{ref_132789}
\abx@aux@cite{0}{ref_132791}
\abx@aux@segm{0}{0}{ref_132791}
\abx@aux@cite{0}{ref_132792}
\abx@aux@segm{0}{0}{ref_132792}
\abx@aux@cite{0}{ref_132793}
\abx@aux@segm{0}{0}{ref_132793}
\abx@aux@cite{0}{ref_132794}
\abx@aux@segm{0}{0}{ref_132794}
\abx@aux@cite{0}{ref_132795}
\abx@aux@segm{0}{0}{ref_132795}
\abx@aux@cite{0}{ref_132796}
\abx@aux@segm{0}{0}{ref_132796}
\abx@aux@cite{0}{ref_132797}
\abx@aux@segm{0}{0}{ref_132797}
\abx@aux@cite{0}{ref_132799}
\abx@aux@segm{0}{0}{ref_132799}
\abx@aux@cite{0}{ref_132801}
\abx@aux@segm{0}{0}{ref_132801}
\abx@aux@cite{0}{ref_132802}
\abx@aux@segm{0}{0}{ref_132802}
\abx@aux@cite{0}{ref_132803}
\abx@aux@segm{0}{0}{ref_132803}
\abx@aux@cite{0}{ref_132804}
\abx@aux@segm{0}{0}{ref_132804}
\abx@aux@cite{0}{ref_132805}
\abx@aux@segm{0}{0}{ref_132805}
\abx@aux@cite{0}{ref_132806}
\abx@aux@segm{0}{0}{ref_132806}
\abx@aux@cite{0}{ref_132807}
\abx@aux@segm{0}{0}{ref_132807}
\abx@aux@cite{0}{ref_132808}
\abx@aux@segm{0}{0}{ref_132808}
\abx@aux@cite{0}{ref_132809}
\abx@aux@segm{0}{0}{ref_132809}
\abx@aux@cite{0}{ref_132810}
\abx@aux@segm{0}{0}{ref_132810}
\abx@aux@cite{0}{ref_132811}
\abx@aux@segm{0}{0}{ref_132811}
\abx@aux@cite{0}{ref_132812}
\abx@aux@segm{0}{0}{ref_132812}
\abx@aux@cite{0}{ref_132813}
\abx@aux@segm{0}{0}{ref_132813}
\abx@aux@cite{0}{ref_132814}
\abx@aux@segm{0}{0}{ref_132814}
\abx@aux@cite{0}{ref_132815}
\abx@aux@segm{0}{0}{ref_132815}
\abx@aux@cite{0}{ref_132816}
\abx@aux@segm{0}{0}{ref_132816}
\abx@aux@cite{0}{ref_132818}
\abx@aux@segm{0}{0}{ref_132818}
\abx@aux@cite{0}{ref_132819}
\abx@aux@segm{0}{0}{ref_132819}
\abx@aux@cite{0}{ref_132820}
\abx@aux@segm{0}{0}{ref_132820}
\abx@aux@cite{0}{ref_132821}
\abx@aux@segm{0}{0}{ref_132821}
\abx@aux@cite{0}{ref_132822}
\abx@aux@segm{0}{0}{ref_132822}
\abx@aux@cite{0}{ref_132823}
\abx@aux@segm{0}{0}{ref_132823}
\abx@aux@cite{0}{ref_132824}
\abx@aux@segm{0}{0}{ref_132824}
\abx@aux@cite{0}{ref_132825}
\abx@aux@segm{0}{0}{ref_132825}
\abx@aux@cite{0}{ref_132826}
\abx@aux@segm{0}{0}{ref_132826}
\abx@aux@cite{0}{ref_132827}
\abx@aux@segm{0}{0}{ref_132827}
\abx@aux@cite{0}{ref_132828}
\abx@aux@segm{0}{0}{ref_132828}
\abx@aux@cite{0}{ref_132829}
\abx@aux@segm{0}{0}{ref_132829}
\abx@aux@cite{0}{ref_132830}
\abx@aux@segm{0}{0}{ref_132830}
\abx@aux@cite{0}{ref_132831}
\abx@aux@segm{0}{0}{ref_132831}
\abx@aux@cite{0}{ref_132832}
\abx@aux@segm{0}{0}{ref_132832}
\abx@aux@cite{0}{ref_132833}
\abx@aux@segm{0}{0}{ref_132833}
\abx@aux@cite{0}{ref_132834}
\abx@aux@segm{0}{0}{ref_132834}
\abx@aux@cite{0}{ref_132835}
\abx@aux@segm{0}{0}{ref_132835}
\abx@aux@cite{0}{ref_132836}
\abx@aux@segm{0}{0}{ref_132836}
\abx@aux@cite{0}{ref_132837}
\abx@aux@segm{0}{0}{ref_132837}
\abx@aux@cite{0}{ref_132838}
\abx@aux@segm{0}{0}{ref_132838}
\abx@aux@cite{0}{ref_132839}
\abx@aux@segm{0}{0}{ref_132839}
\abx@aux@cite{0}{ref_132840}
\abx@aux@segm{0}{0}{ref_132840}
\abx@aux@cite{0}{ref_132841}
\abx@aux@segm{0}{0}{ref_132841}
\abx@aux@cite{0}{ref_132842}
\abx@aux@segm{0}{0}{ref_132842}
\abx@aux@cite{0}{ref_132843}
\abx@aux@segm{0}{0}{ref_132843}
\abx@aux@cite{0}{ref_132844}
\abx@aux@segm{0}{0}{ref_132844}
\abx@aux@cite{0}{ref_132846}
\abx@aux@segm{0}{0}{ref_132846}
\abx@aux@cite{0}{ref_132848}
\abx@aux@segm{0}{0}{ref_132848}
\abx@aux@cite{0}{ref_132849}
\abx@aux@segm{0}{0}{ref_132849}
\abx@aux@cite{0}{ref_132850}
\abx@aux@segm{0}{0}{ref_132850}
\abx@aux@cite{0}{ref_132830}
\abx@aux@segm{0}{0}{ref_132830}
\abx@aux@cite{0}{ref_132813}
\abx@aux@segm{0}{0}{ref_132813}
\abx@aux@cite{0}{ref_132825}
\abx@aux@segm{0}{0}{ref_132825}
\abx@aux@cite{0}{ref_132829}
\abx@aux@segm{0}{0}{ref_132829}
\abx@aux@cite{0}{ref_132781}
\abx@aux@segm{0}{0}{ref_132781}
\abx@aux@cite{0}{ref_132853}
\abx@aux@segm{0}{0}{ref_132853}
\abx@aux@cite{0}{ref_132801}
\abx@aux@segm{0}{0}{ref_132801}
\abx@aux@cite{0}{ref_132782}
\abx@aux@segm{0}{0}{ref_132782}
\abx@aux@cite{0}{ref_132803}
\abx@aux@segm{0}{0}{ref_132803}
\abx@aux@cite{0}{ref_132782}
\abx@aux@segm{0}{0}{ref_132782}
\abx@aux@cite{0}{ref_132777}
\abx@aux@segm{0}{0}{ref_132777}
\abx@aux@cite{0}{ref_132794}
\abx@aux@segm{0}{0}{ref_132794}
\abx@aux@cite{0}{ref_132833}
\abx@aux@segm{0}{0}{ref_132833}
\abx@aux@cite{0}{ref_132803}
\abx@aux@segm{0}{0}{ref_132803}
\abx@aux@cite{0}{ref_132785}
\abx@aux@segm{0}{0}{ref_132785}
\abx@aux@cite{0}{ref_132848}
\abx@aux@segm{0}{0}{ref_132848}
\abx@aux@cite{0}{ref_132856}
\abx@aux@segm{0}{0}{ref_132856}
\abx@aux@cite{0}{ref_132857}
\abx@aux@segm{0}{0}{ref_132857}
\abx@aux@cite{0}{ref_132858}
\abx@aux@segm{0}{0}{ref_132858}
\abx@aux@cite{0}{ref_132859}
\abx@aux@segm{0}{0}{ref_132859}
\abx@aux@cite{0}{ref_132860}
\abx@aux@segm{0}{0}{ref_132860}
\abx@aux@cite{0}{ref_132799}
\abx@aux@segm{0}{0}{ref_132799}
\abx@aux@cite{0}{ref_132834}
\abx@aux@segm{0}{0}{ref_132834}
\abx@aux@cite{0}{ref_132818}
\abx@aux@segm{0}{0}{ref_132818}
\abx@aux@cite{0}{ref_132811}
\abx@aux@segm{0}{0}{ref_132811}
\abx@aux@cite{0}{ref_132777}
\abx@aux@segm{0}{0}{ref_132777}
\abx@aux@cite{0}{ref_132797}
\abx@aux@segm{0}{0}{ref_132797}
\abx@aux@cite{0}{ref_132810}
\abx@aux@segm{0}{0}{ref_132810}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}\textbf {Flavonoids targeting oxidative stress }}{4}{subsection.3.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}\textbf {Flavonoids targeting inflammation}}{4}{subsection.3.2}\protected@file@percent }
\abx@aux@cite{0}{ref_132822}
\abx@aux@segm{0}{0}{ref_132822}
\abx@aux@cite{0}{ref_132861}
\abx@aux@segm{0}{0}{ref_132861}
\abx@aux@cite{0}{ref_132862}
\abx@aux@segm{0}{0}{ref_132862}
\abx@aux@cite{0}{ref_132819}
\abx@aux@segm{0}{0}{ref_132819}
\abx@aux@cite{0}{ref_132844}
\abx@aux@segm{0}{0}{ref_132844}
\abx@aux@cite{0}{ref_132797}
\abx@aux@segm{0}{0}{ref_132797}
\abx@aux@cite{0}{ref_132863}
\abx@aux@segm{0}{0}{ref_132863}
\abx@aux@cite{0}{ref_132864}
\abx@aux@segm{0}{0}{ref_132864}
\abx@aux@cite{0}{ref_132784}
\abx@aux@segm{0}{0}{ref_132784}
\abx@aux@cite{0}{ref_132795}
\abx@aux@segm{0}{0}{ref_132795}
\abx@aux@cite{0}{ref_132807}
\abx@aux@segm{0}{0}{ref_132807}
\abx@aux@cite{0}{ref_132842}
\abx@aux@segm{0}{0}{ref_132842}
\abx@aux@cite{0}{ref_132793}
\abx@aux@segm{0}{0}{ref_132793}
\abx@aux@cite{0}{ref_132824}
\abx@aux@segm{0}{0}{ref_132824}
\abx@aux@cite{0}{ref_132802}
\abx@aux@segm{0}{0}{ref_132802}
\abx@aux@cite{0}{ref_132840}
\abx@aux@segm{0}{0}{ref_132840}
\abx@aux@cite{0}{ref_132843}
\abx@aux@segm{0}{0}{ref_132843}
\abx@aux@cite{0}{ref_132865}
\abx@aux@segm{0}{0}{ref_132865}
\abx@aux@cite{0}{ref_132866}
\abx@aux@segm{0}{0}{ref_132866}
\abx@aux@cite{0}{ref_132867}
\abx@aux@segm{0}{0}{ref_132867}
\abx@aux@cite{0}{ref_132821}
\abx@aux@segm{0}{0}{ref_132821}
\abx@aux@cite{0}{ref_132846}
\abx@aux@segm{0}{0}{ref_132846}
\abx@aux@cite{0}{ref_132868}
\abx@aux@segm{0}{0}{ref_132868}
\abx@aux@cite{0}{ref_132814}
\abx@aux@segm{0}{0}{ref_132814}
\abx@aux@cite{0}{ref_132835}
\abx@aux@segm{0}{0}{ref_132835}
\abx@aux@cite{0}{ref_132841}
\abx@aux@segm{0}{0}{ref_132841}
\abx@aux@cite{0}{ref_132869}
\abx@aux@segm{0}{0}{ref_132869}
\abx@aux@cite{0}{ref_132754}
\abx@aux@segm{0}{0}{ref_132754}
\abx@aux@cite{0}{ref_132870}
\abx@aux@segm{0}{0}{ref_132870}
\abx@aux@cite{0}{ref_132775}
\abx@aux@segm{0}{0}{ref_132775}
\abx@aux@cite{0}{ref_132789}
\abx@aux@segm{0}{0}{ref_132789}
\abx@aux@cite{0}{ref_132826}
\abx@aux@segm{0}{0}{ref_132826}
\abx@aux@cite{0}{ref_132823}
\abx@aux@segm{0}{0}{ref_132823}
\abx@aux@cite{0}{ref_132796}
\abx@aux@segm{0}{0}{ref_132796}
\abx@aux@cite{0}{ref_132776}
\abx@aux@segm{0}{0}{ref_132776}
\abx@aux@cite{0}{ref_132792}
\abx@aux@segm{0}{0}{ref_132792}
\abx@aux@cite{0}{ref_132827}
\abx@aux@segm{0}{0}{ref_132827}
\abx@aux@cite{0}{ref_132839}
\abx@aux@segm{0}{0}{ref_132839}
\abx@aux@cite{0}{ref_132828}
\abx@aux@segm{0}{0}{ref_132828}
\abx@aux@cite{0}{ref_132768}
\abx@aux@segm{0}{0}{ref_132768}
\abx@aux@cite{0}{ref_132874}
\abx@aux@segm{0}{0}{ref_132874}
\abx@aux@cite{0}{ref_132875}
\abx@aux@segm{0}{0}{ref_132875}
\abx@aux@cite{0}{ref_132784}
\abx@aux@segm{0}{0}{ref_132784}
\abx@aux@cite{0}{ref_132805}
\abx@aux@segm{0}{0}{ref_132805}
\abx@aux@cite{0}{ref_132808}
\abx@aux@segm{0}{0}{ref_132808}
\abx@aux@cite{0}{ref_132813}
\abx@aux@segm{0}{0}{ref_132813}
\abx@aux@cite{0}{ref_132812}
\abx@aux@segm{0}{0}{ref_132812}
\abx@aux@cite{0}{ref_132822}
\abx@aux@segm{0}{0}{ref_132822}
\abx@aux@cite{0}{ref_132876}
\abx@aux@segm{0}{0}{ref_132876}
\abx@aux@cite{0}{ref_132877}
\abx@aux@segm{0}{0}{ref_132877}
\abx@aux@cite{0}{ref_132778}
\abx@aux@segm{0}{0}{ref_132778}
\abx@aux@cite{0}{ref_132825}
\abx@aux@segm{0}{0}{ref_132825}
\abx@aux@cite{0}{ref_132787}
\abx@aux@segm{0}{0}{ref_132787}
\abx@aux@cite{0}{ref_132836}
\abx@aux@segm{0}{0}{ref_132836}
\abx@aux@cite{0}{ref_132844}
\abx@aux@segm{0}{0}{ref_132844}
\abx@aux@cite{0}{ref_132878}
\abx@aux@segm{0}{0}{ref_132878}
\abx@aux@cite{0}{ref_132879}
\abx@aux@segm{0}{0}{ref_132879}
\abx@aux@cite{0}{ref_132814}
\abx@aux@segm{0}{0}{ref_132814}
\abx@aux@cite{0}{ref_132810}
\abx@aux@segm{0}{0}{ref_132810}
\abx@aux@cite{0}{ref_132808}
\abx@aux@segm{0}{0}{ref_132808}
\abx@aux@cite{0}{ref_132830}
\abx@aux@segm{0}{0}{ref_132830}
\abx@aux@cite{0}{ref_132831}
\abx@aux@segm{0}{0}{ref_132831}
\abx@aux@cite{0}{ref_132832}
\abx@aux@segm{0}{0}{ref_132832}
\abx@aux@cite{0}{ref_132829}
\abx@aux@segm{0}{0}{ref_132829}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}\textbf {Flavonoids targeting airway remodelling and fibrosis}}{8}{subsection.3.3}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}\textbf {Flavonoids targeting programmed cell death}}{8}{subsection.3.4}\protected@file@percent }
\abx@aux@cite{0}{ref_132880}
\abx@aux@segm{0}{0}{ref_132880}
\abx@aux@cite{0}{ref_132881}
\abx@aux@segm{0}{0}{ref_132881}
\abx@aux@cite{0}{ref_132882}
\abx@aux@segm{0}{0}{ref_132882}
\abx@aux@cite{0}{ref_132883}
\abx@aux@segm{0}{0}{ref_132883}
\abx@aux@cite{0}{ref_132783}
\abx@aux@segm{0}{0}{ref_132783}
\abx@aux@cite{0}{ref_132809}
\abx@aux@segm{0}{0}{ref_132809}
\abx@aux@cite{0}{ref_132806}
\abx@aux@segm{0}{0}{ref_132806}
\abx@aux@cite{0}{ref_132884}
\abx@aux@segm{0}{0}{ref_132884}
\abx@aux@cite{0}{ref_132885}
\abx@aux@segm{0}{0}{ref_132885}
\abx@aux@cite{0}{ref_132886}
\abx@aux@segm{0}{0}{ref_132886}
\abx@aux@cite{0}{ref_132887}
\abx@aux@segm{0}{0}{ref_132887}
\abx@aux@cite{0}{ref_132888}
\abx@aux@segm{0}{0}{ref_132888}
\abx@aux@cite{0}{ref_132889}
\abx@aux@segm{0}{0}{ref_132889}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.5}\textbf {Flavonoids’ safety}}{9}{subsection.3.5}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {4}\textit {\textbf {Conclusion and prospect}}}{9}{section.4}\protected@file@percent }
\abx@aux@cite{0}{ref_132788}
\abx@aux@segm{0}{0}{ref_132788}
\abx@aux@nociteall
\@writefile{toc}{\contentsline {section}{References}{10}{section*.6}\protected@file@percent }
\newlabel{LastPage}{{4}{16}{\protect \textit {\protect \textbf {Conclusion and prospect}}}{page.16}{}}
\gdef\lastpage@lastpage{16}
\gdef\lastpage@lastpageHy{16}
\abx@aux@read@bbl@mdfivesum{D3186D0484B0EA29C8FA121A4BA16204}
\abx@aux@defaultrefcontext{0}{ref_132721}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132722}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132735}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132736}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132737}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132747}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132748}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132749}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132750}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132751}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132752}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132753}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132756}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132757}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132758}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132759}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132760}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132761}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132762}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132763}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132764}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132765}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132768}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132769}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132754}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132755}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132770}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132771}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132774}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132775}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132776}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132777}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132778}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132779}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132780}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132781}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132782}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132783}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132784}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132785}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132786}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132787}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132788}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132789}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132791}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132792}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132793}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132794}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132795}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132796}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132797}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132799}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132801}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132802}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132803}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132804}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132805}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132806}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132807}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132808}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132809}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132810}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132811}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132812}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132813}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132814}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132815}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132816}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132818}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132819}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132820}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132821}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132822}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132823}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132824}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132825}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132826}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132827}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132828}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132829}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132830}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132831}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132832}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132833}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132834}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132835}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132836}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132837}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132838}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132839}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132840}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132841}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132842}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132843}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132844}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132846}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132848}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132849}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132850}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132853}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132856}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132857}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132858}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132859}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132860}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132861}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132862}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132863}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132864}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132865}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132866}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132867}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132868}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132869}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132870}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132874}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132875}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132876}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132877}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132878}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132879}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132880}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132881}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132882}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132883}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132884}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132885}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132886}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132887}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132888}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132889}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132723}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132724}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132725}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132726}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132727}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132728}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132729}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132730}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132731}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132732}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132733}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132734}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132738}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132739}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132740}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132741}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132742}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132743}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132744}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132745}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132746}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132766}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132767}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132772}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132773}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132851}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132852}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132854}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132855}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132871}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132872}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132873}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132890}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132891}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132892}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132893}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132895}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132896}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132897}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_132898}{none/global//global/global/global}
\gdef \@abspage@last{16}

7756
article_3113.bbl Normal file
View File

File diff suppressed because it is too large Load Diff

2675
article_3113.bcf Normal file
View File

File diff suppressed because it is too large Load Diff

15
article_3113.blg Normal file
View File

@@ -0,0 +1,15 @@
[0] Config.pm:328> INFO - This is Biber 2.21
[0] Config.pm:331> INFO - Logfile is 'article_3113.blg'
[93] biber-MSWIN64:342> INFO - ===
[132] Biber.pm:421> INFO - Reading 'article_3113.bcf'
[259] Biber.pm:999> INFO - Using all citekeys in bib section 0
[268] Biber.pm:4487> INFO - Processing section 0
[277] Biber.pm:4678> INFO - Looking for bibtex file 'references_3113.bib' for section 0
[334] bibtex.pm:1713> INFO - LaTeX decoding ...
[463] bibtex.pm:1519> INFO - Found BibTeX data source 'references_3113.bib'
[1139] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
[1139] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
[1139] Biber.pm:4307> INFO - Sorting list 'none/global//global/global/global' of type 'entry' with template 'none' and locale 'en-US'
[1139] Biber.pm:4313> INFO - No sort tailoring available for locale 'en-US'
[1212] bbl.pm:676> INFO - Writing 'article_3113.bbl' with encoding 'UTF-8'
[1305] bbl.pm:779> INFO - Output to article_3113.bbl

4170
article_3113.log Normal file
View File

File diff suppressed because it is too large Load Diff

13
article_3113.out Normal file
View File

@@ -0,0 +1,13 @@
\BOOKMARK [1][-]{section.1}{\376\377\000I\000n\000t\000r\000o\000d\000u\000c\000t\000i\000o\000n}{}% 1
\BOOKMARK [1][-]{section.2}{\376\377\000P\000a\000t\000h\000o\000g\000e\000n\000e\000s\000i\000s\000\040\000o\000f\000\040\000C\000O\000P\000D}{}% 2
\BOOKMARK [2][-]{subsection.2.1}{\376\377\000O\000x\000i\000d\000a\000t\000i\000v\000e\000\040\000s\000t\000r\000e\000s\000s}{section.2}% 3
\BOOKMARK [2][-]{subsection.2.2}{\376\377\000I\000n\000f\000l\000a\000m\000m\000a\000t\000i\000o\000n\000\040\000a\000n\000d\000\040\000i\000n\000f\000l\000a\000m\000m\000a\000t\000o\000r\000y\000\040\000c\000e\000l\000l\000s}{section.2}% 4
\BOOKMARK [2][-]{subsection.2.3}{\376\377\000F\000i\000b\000r\000o\000s\000i\000s}{section.2}% 5
\BOOKMARK [1][-]{section.3}{\376\377\000F\000l\000a\000v\000o\000n\000o\000i\000d\000s\000\040\000i\000n\000\040\000C\000O\000P\000D\000\040\000r\000e\000g\000u\000l\000a\000t\000i\000o\000n}{}% 6
\BOOKMARK [2][-]{subsection.3.1}{\376\377\000F\000l\000a\000v\000o\000n\000o\000i\000d\000s\000\040\000t\000a\000r\000g\000e\000t\000i\000n\000g\000\040\000o\000x\000i\000d\000a\000t\000i\000v\000e\000\040\000s\000t\000r\000e\000s\000s\000\240}{section.3}% 7
\BOOKMARK [2][-]{subsection.3.2}{\376\377\000F\000l\000a\000v\000o\000n\000o\000i\000d\000s\000\040\000t\000a\000r\000g\000e\000t\000i\000n\000g\000\040\000i\000n\000f\000l\000a\000m\000m\000a\000t\000i\000o\000n}{section.3}% 8
\BOOKMARK [2][-]{subsection.3.3}{\376\377\000F\000l\000a\000v\000o\000n\000o\000i\000d\000s\000\040\000t\000a\000r\000g\000e\000t\000i\000n\000g\000\040\000a\000i\000r\000w\000a\000y\000\040\000r\000e\000m\000o\000d\000e\000l\000l\000i\000n\000g\000\040\000a\000n\000d\000\040\000f\000i\000b\000r\000o\000s\000i\000s}{section.3}% 9
\BOOKMARK [2][-]{subsection.3.4}{\376\377\000F\000l\000a\000v\000o\000n\000o\000i\000d\000s\000\040\000t\000a\000r\000g\000e\000t\000i\000n\000g\000\040\000p\000r\000o\000g\000r\000a\000m\000m\000e\000d\000\040\000c\000e\000l\000l\000\040\000d\000e\000a\000t\000h}{section.3}% 10
\BOOKMARK [2][-]{subsection.3.5}{\376\377\000F\000l\000a\000v\000o\000n\000o\000i\000d\000s\040\031\000\040\000s\000a\000f\000e\000t\000y}{section.3}% 11
\BOOKMARK [1][-]{section.4}{\376\377\000C\000o\000n\000c\000l\000u\000s\000i\000o\000n\000\040\000a\000n\000d\000\040\000p\000r\000o\000s\000p\000e\000c\000t}{}% 12
\BOOKMARK [1][-]{section*.6}{\376\377\000R\000e\000f\000e\000r\000e\000n\000c\000e\000s}{}% 13

BIN
article_3113.pdf Normal file
View File

Binary file not shown.

87
article_3113.run.xml Normal file
View File

@@ -0,0 +1,87 @@
<?xml version="1.0" standalone="yes"?>
<!-- logreq request file -->
<!-- logreq version 1.0 / dtd version 1.0 -->
<!-- Do not edit this file! -->
<!DOCTYPE requests [
<!ELEMENT requests (internal | external)*>
<!ELEMENT internal (generic, (provides | requires)*)>
<!ELEMENT external (generic, cmdline?, input?, output?, (provides | requires)*)>
<!ELEMENT cmdline (binary, (option | infile | outfile)*)>
<!ELEMENT input (file)+>
<!ELEMENT output (file)+>
<!ELEMENT provides (file)+>
<!ELEMENT requires (file)+>
<!ELEMENT generic (#PCDATA)>
<!ELEMENT binary (#PCDATA)>
<!ELEMENT option (#PCDATA)>
<!ELEMENT infile (#PCDATA)>
<!ELEMENT outfile (#PCDATA)>
<!ELEMENT file (#PCDATA)>
<!ATTLIST requests
version CDATA #REQUIRED
>
<!ATTLIST internal
package CDATA #REQUIRED
priority (9) #REQUIRED
active (0 | 1) #REQUIRED
>
<!ATTLIST external
package CDATA #REQUIRED
priority (1 | 2 | 3 | 4 | 5 | 6 | 7 | 8) #REQUIRED
active (0 | 1) #REQUIRED
>
<!ATTLIST provides
type (static | dynamic | editable) #REQUIRED
>
<!ATTLIST requires
type (static | dynamic | editable) #REQUIRED
>
<!ATTLIST file
type CDATA #IMPLIED
>
]>
<requests version="1.0">
<internal package="biblatex" priority="9" active="0">
<generic>latex</generic>
<provides type="dynamic">
<file>article_3113.bcf</file>
</provides>
<requires type="dynamic">
<file>article_3113.bbl</file>
</requires>
<requires type="static">
<file>blx-dm.def</file>
<file>blx-unicode.def</file>
<file>blx-compat.def</file>
<file>biblatex.def</file>
<file>blx-natbib.def</file>
<file>standard.bbx</file>
<file>numeric.bbx</file>
<file>numeric-comp.cbx</file>
<file>biblatex.cfg</file>
<file>english.lbx</file>
</requires>
</internal>
<external package="biblatex" priority="5" active="0">
<generic>biber</generic>
<cmdline>
<binary>biber</binary>
<infile>article_3113</infile>
</cmdline>
<input>
<file>article_3113.bcf</file>
</input>
<output>
<file>article_3113.bbl</file>
</output>
<provides type="dynamic">
<file>article_3113.bbl</file>
</provides>
<requires type="dynamic">
<file>article_3113.bcf</file>
</requires>
<requires type="editable">
<file>references_3113.bib</file>
</requires>
</external>
</requests>

BIN
article_3113.synctex.gz Normal file
View File

Binary file not shown.

209
article_3113.tex Normal file
View File

@@ -0,0 +1,209 @@
\documentclass[
journal=tmr,
journalname={{Traditional Medicine Research}},
layout=largetwo,
year=2025,%年
volume=10,%卷
articletype=REVIWER,
no=9,%期
page=57,%号
]{tmr-tex}
\doi{10.53388/TMR20241121001}
\journalweb{https://www.tmrjournals.com/tmr}
\usepackage{amsmath}
\usepackage[nopatch]{microtype}
\usepackage{booktabs}
\usepackage[backend=biber]{biblatex}
\usepackage{xcolor}
\usepackage{tabularray}
\addbibresource{references_3113.bib}
\definecolor{evenRowColor}{RGB}{250,231,232}
\title{Natural flavonoids for the treatment of chronic obstructive pulmonary disease: An overview}
\author{Peng-Liang Shi}
\affiliation{School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.}
\firstauthor
\author{Guo-Xuan Zhang}
\affiliation{School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.}
\orcid{0000-0002-0472-0377}
\author{Pei-Yi Wang}
\affiliation{Department of Pharmacy, The Second Affiliated Hospital of Shandong First Medical University, Tai’an 271000, China.}
\author{Zi-Qi Liu}
\affiliation{School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.}
\author{Bing-Qing Zheng}
\email{Zhengbq880223@126.com}
\affiliation{School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.}
%\orcid{0000-0002-0472-0377}
\Correspondence{Bing-Qing Zheng,School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.E-mail: Zhengbq880223@126.com.}
\keywords{keyword entry 1, keyword entry 2, keyword entry 3} %% First letter not capped
\authorcontributions{Shi PL collected documents and wrote the manuscript; Zhang GX, Wang PY, and Liu ZQ helped with information collection and manuscript editing; Zheng BQ revised the manuscript for important content and the manuscript preparation and editing.}
\competinginterests{The authors declare no conflicts of interest.}
\Acknowledgments{This work was supported by the Shandong Provincial Traditional Chinese Medicine Science and Technology Project “Study on the mechanism of Astragalus polysaccharide inhibiting the occurrence of liver fibrosis through PD-1 regulating NK cell function (2020Q004)”; Shandong Provincial Medical and Health Science and Technology Development Project “Mechanism of STAT3 inhibition of liver fibrosis by regulating PD-1+NK cells (202002070991)”. Natural Science Foundation of Shandong Province “Astragalus polysaccharides inhibit liver fibrosis by regulating CD49a+NK cells (ZR2022QH111).”}
\Peerreviewinformation{Traditional Medicine Research thanks all anonymous reviewers for their contribution to the peer review of this paper}
\Abbreviations{COPD, chronic obstructive pulmonary disease; CS, cigarette smoke; ROS, reactive oxygen species; ECM, extracellular matrix; EGFR, epidermal growth factor receptor; NF-κB, nuclear factor kappa-B; TNF-α, tumor necrosis factor-alpha; TGF-β, transforming growth factor-β; IL-6, interleukin-6; IL-1β, interleukin-1 beta; MMP9, matrix metalloproteinase 9; HDAC2, histone deacetylase-2; PI3K, phosphoinositide-3- kinase; Nrf2, nuclear factor erythroid 2-related factor 2; CXCL, C-X-C motif chemokine ligand; NE, neutrophil elastase; MAPK, mitogen-activated protein kinase; EGF, epidermal growth factor; LPS, lipopolysaccharide; TLR4, toll-like receptor 4; JNK, c-Jun N-terminal kinase; SOD, superoxide dismutase; GSH, glutathione; GSH-Px, glutathione peroxidase; HO-1, haem oxygenase 1; MDA, malondialdehyde; SIRT1, silent information regulator 1; NAD+, nicotinamide adenine dinucleotide; GPX4, glutathione peroxidase 4; NADPH, nicotinamide adenine dinucleotide phosphate; GR, glucocorticoid receptor; CSE, cigarette smoke extract; IκB, inhibitor kappa B; HBECs, human bronchial epithelial cells; TLRs, toll-like receptors; IRAK, interleukin-1 receptor-associated kinase; PKC, protein kinase C; NOX4, NADPH oxidase 4; TIMP, tissue inhibitors of metalloproteinase; EMT, epithelial-mesenchymal transition; α-SMA, alpha smooth muscle actin; ERK, extracellular regulated protein kinases; AKT, protein kinase B.}
\Citation{Shi PL, Zhang GX, Wang PY, et al. Natural flavonoids for the treatment of chronic obstructive pulmonary disease: An overview. \textit{Tradit Med Res}. 2025;10(9):57. doi:10.53388/TMR20241121001}
\received{22 November 2024}
\revised{28 November 2024}
\accepted{27 February 2025}
\Availableonline{3 March 2025}
\EditorialAdvisoryBoard{lixiang}
\Executiveeditor{liuna}
\tmrabstract{<p>Chronic obstructive pulmonary disease (COPD) has been a major global public health issue due to its high prevalence, disability, and mortality rates. The pathogenesis of COPD is complex and remains incompletely understood, compounded by a lack of specific and effective clinical therapies. Key pathophysiological mechanisms include oxidative stress, inflammation, programmed cell death, and fibrosis, influenced by external risk factors such as cigarette smoke and internal factors like immune deficiency. Natural flavonoids emerge as promising adjuvant treatments or potential drug candidates for COPD, attributed to their multi-target properties and low toxicity. This article provides an overview of various types and sources of natural flavonoids that exhibit therapeutic effects on COPD, their specific pharmacological actions and detailed mechanisms of action. This review aims to serve as a reference for adjuvant treatment strategies in daily dietary practices and to inspire novel drug candidates for COPD.</p>}
\keywords{natural flavonoids; COPD; oxidative stress; inflammation; programmed cell death; fibrosis}
\KeywordImage{./image/c83075d3cbe5dd333419e6ecf1028e70.png}
\begin{document}
\twocolumn
\begin{highlight}
\highlightitem{Highlights}{<p>1. The effectiveness and diversity of traditional Chinese medicine in treating COPD promote the exploration of effective natural substances.</p><p>2. The structural diversity of natural flavonoids contributes to their ability to engage multiple pathways and mechanisms in the treatment of COPD.</p><p>3. The high safety and easy availability of natural flavonoids are significant advantages in both the prevention and treatment of COPD.</p>}
\highlightitem{Medical history of objective}{<p>Extensive research on flavonoids in the treatment of COPD highlights the effectiveness of TCM for long-term management of lung diseases. For instance, the flower buds of \emph{Tussilago farfara} L. were first documented in “\emph{Shennong’s Classic of Materia Medica}” (written during the Eastern Han Dynasty (25-220 C.E.)), which noted their ability to dissolve phlegm and relieve cough. Similarly, other ancient Chinese texts, such as “\emph{Newly Revised Materia Medica}” (Jing Zhang et al. wrote in Tang Dynasty (659 C.E.)) and “\emph{Compendium of Materia Medica}” (Shi-Zhen Li wrote in 1552-1578 C.E.), also recognized the same effects. Current pharmacological studies indicate that the flavonoids present in \emph{Tussilago farfara} L. can combat COPD through various mechanisms, such as anti-inflammatory and anti-oxidative stress.</p>}
\end{highlight}
\section{\textit{\textbf{Introduction}}}
\par
Chronic obstructive pulmonary disease (COPD) is a progressive condition defined by persistent airflow obstruction and debilitating respiratory symptoms, presenting a considerable public health challenge across the globe. This illness leads to significant health issues and increased death rates, highlighting its effect on healthcare systems and individuals’ quality of life. A report published by the World Health Organization indicated that COPD has escalated to the status of the third cause of death worldwide \parencite{ref_132721}. Furthermore, COPD will continue to increase in tandem with the aging global population \parencite{ref_132722}.\par
The main pathological features of COPD include airway inflammation, airflow limitation, excessive mucus production, and damage to lung tissue. These factors result in symptoms such as wheezing, coughing, and shortness of breath, often leading to acute exacerbations or even death. The lungs are damaged by exposure to cigarette smoke (CS), dust, biofuel exhaust, and pathogens \textcolor[HTML]{0082AA}{[3, 4]}. COPD often overlap with asthma and is often complicated by pulmonary hypertension and cardiovascular diseases, including systemic hypertension and atherosclerosis \textcolor[HTML]{0082AA}{[5, 6]}. Multiple theories have been used to elucidate the pathogenesis of COPD, including the regulation of local oxidative stress in the lungs, the degree of inflammation, the proteolytic homeostasis of the extracellular matrix (ECM), and the imbalance of autophagy and apoptosis [7-10]. Although the pathogenesis of COPD remains unclear, there is currently no specific treatment available. Palliative regimens focused on improving airflow limitation represent the primary clinical approaches. Thus, new strategies and drugs for blocking COPD development should be developed, and multiple pathological factors and targets should be considered.\par
Natural products are still a crucial source for drug screening and discovery because of their structural and functional diversity. Flavonoids are vital bioactive components found in natural plants, playing an essential role in maintaining health and alleviating diseases. Natural flavonoids are categorized based on their structural features and demonstrate various biological activities, including anti-inflammatory, antioxidative, cell-protective, antimicrobial, and ECM degradation \textcolor[HTML]{0082AA}{[11, 12]}. Furthermore, flavonoid intakes can not only alleviate COPD in experimental models but are also inversely associated with COPD incidence in smokers \textcolor[HTML]{0082AA}{[13, 14]}. Natural flavonoids, with the advantages of multi-target and low toxicity, have great potential in developing COPD drugs, but there are few reviews on the relevant content. The familiar sources of natural flavonoids and the mechanism of action in treating COPD are shown in Figure 1. This article mainly summarises the pharmacological effects and specific mechanisms of natural flavonoids on COPD and provides a reference for the adjuvant treatment of COPD in daily dietary life and opinions for identifying new prospective candidate drugs for COPD.\par
\par
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./image/file_682161fb0fcfd.png}
\end{figure*}\par
\par
\section{\textit{\textbf{Pathogenesis of COPD}}}
\par
COPD is a progressive respiratory condition defined by significant airflow limitation, mucus overproduction, chronic bronchitis, emphysema, or damage to the alveolar septal walls. These manifestations lead to symptoms such as wheezing, coughing, and dyspnea, which can result in acute exacerbations, hospitalisation, and even death \parencite{ref_132735}.\par
CS, pathogens, and various environmental exposures are significant external risk factors contributing to the development of COPD; notably, the removal of these risk factors has the potential to halt the pathological progression of the disease \textcolor[HTML]{0082AA}{[4, 15]}. Other factors, including genetics, gender, occupation, airway hyperresponsiveness, lung growth and development, and infections, also significantly contribute to the pathogenesis of COPD \parencite{ref_132735,ref_132736,ref_132737}. COPD is characterised by persistent inflammation and fibrosis of the small airways and the deterioration of lung parenchyma, also known as emphysema \textcolor[HTML]{0082AA}{[18, 19]}. COPD is complicatedly regulated by various biological mechanisms and their crosstalk, including oxidative stress, inflammation, ECM homeostasis, apoptosis, pyroptosis, and mucous cell hyperplasia \textcolor[HTML]{0082AA}{[7, 20-24]}. Among these, the regulation mechanism of oxidative stress and inflammation has attracted much attention in the field of COPD research and drug discovery, and airway remodelling and fibrosis are critical pathological features of late-stage COPD.\par
\par
\subsection{\textbf{Oxidative stress}}
Increased oxidative stress in the lungs significantly drives the disease through various molecular mechanisms\textcolor[HTML]{0082AA}{ [25, 26]}. The anatomical structure of the lungs makes them highly vulnerable to damage from environmental oxidative stress. Reactive oxygen species (ROS) from mitochondrial respiration and responses to lung infections also play a regulatory role. In individuals diagnosed with COPD, there is a notable increase in both the quantity and activation of alveolar macrophages, leading to higher levels of ROS, such as superoxide anions and H$_{{2}}$O$_{{2}}$ \parencite{ref_132747}. The same outcome can also be triggered by an increase in neutrophils, particularly during exacerbations \parencite{ref_132748}. In addition, lung epithelial cells also produce oxidative stress due to mitochondrial respiration \parencite{ref_132749}. Excessive ROS production is induced by both exogenous stimulation and internal inflammatory conditions.\par
Increased oxidative stress can activate several signalling pathways to drive the pathophysiology of COPD. Oxidative stress releases a large amount of oxidants, leading to the inactivation of anti-proteases and disrupting the protease-antiprotease balance in lung homeostasis, particularly affecting α1-antitrypsin \parencite{ref_132750}. The oxidative system is linked to the secretion of airway epithelial mucus, lead to the accumulation of ROS and induce high levels of mucus genes like mucin 5AC (\textit{MUC5AC}) and mucin 5B (\textit{MUC5B}) \parencite{ref_132751,ref_132752,ref_132753}. Epidermal growth factor receptor (EGFR) resided in oxidants-activated airway cells, which up-regulated the transcription of mucus genes during COPD development \textcolor[HTML]{0082AA}{[34, 35]}. Oxidative stress also triggers an inflammatory response in the lungs by influencing activator protein 1, an oxidant-sensitive transcription factor, and nuclear factor kappa-B (NF-κB). These factors promote the expression of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1 beta (IL-1β) \parencite{ref_132756}. In addition, oxidative stress activates transforming growth factor-β (TGF-β) signalling pathways, leading to small airway fibrosis and increased expression of matrix metalloproteinase 9 (MMP9) \parencite{ref_132757}, which also enhances elastolysis by inactivating α1-antitrypsin and secretory leukoprotease inhibitor, resulting in heightened neutrophil elastase activity and emphysema \parencite{ref_132758}. Moreover, corticosteroids play a significant role in suppressing pro-inflammatory gene expression \parencite{ref_132759}. However, oxidative stress can directly promote the phosphorylation and ubiquitination of histone deacetylase-2 (HDAC2) by activating phosphoinositide-3-kinase (PI3K)-δ \parencite{ref_132760}, resulting in amplified inflammation and corticosteroid resistance \parencite{ref_132761}.\par
Notably, nuclear factor erythroid 2-related factor 2 (Nrf2) will dissociate in oxidative stress response and transport to the nucleus to activate antioxidant gene transcription. Research indicates that the reduced self-protective mechanism in COPD patients is linked to decreased levels of Nrf2, leading to lower production of endogenous antioxidants. So, oxidative stress is a crucial mechanism of CS that initiates lung injury and inflammation and provides us with several targets, such as ROS and Nrf2, for drug discovery \parencite{ref_132762}.\par
\par
\subsection{\textbf{Inflammation and inflammatory cells}}
Inflammation drives the progression and exacerbations of COPD. In the airway lumen of COPD patients, inflammation is characteristic of increased numbers of macrophages, neutrophils, eosinophils, T cells, B cells, epithelial cells, endothelial cells, and fibroblasts \parencite{ref_132763}. The cells with different functions release different cytokines that work together to stimulate and maintain inflammatory levels and promote the progression and exacerbation of COPD.\par
CS stimulates both airway epithelial cells and macrophages to produce a range of chemotactic cytokines and chemokines, including IL-6, IL-8, C-X-C motif chemokine ligand (CXCL) 10, and CXCL9. These factors recruit neutrophils and CD8$^{{+}}$ T cells to the airway \parencite{ref_132764}. CD8$^{{+}}$ T cells are known to release perforins and granzymes, which contribute to tissue destruction and apoptosis. This process results in the production of pro-inflammatory proteases and cytokines, including IFN-γ and TNF-α, which may play a significant role in the pathogenesis of emphysema \parencite{ref_132765}. IFN-γ secreted by Th1, CD8$^{{+}}$ cells, and B cells triggers a series of signalling cascades that induce macrophage polarized to M1 phenotype, which indeed produces pro-inflammatory cytokines. Such as, TNF-α promotes leukocyte accumulation by modulating endothelial adhesion molecules, which release elastase and ROS to destroy alveolar epithelium \textcolor[HTML]{0082AA}{[46, 47]}. Proteases such as MMP and neutrophil elastase (NE), released by neutrophils and macrophages, degrade connective tissue and elastin in the alveoli, leading to both localized (centrilobular) and generalized (panlobular) emphysema. MMP levels were significantly increased in COPD patients with emphysema, contributing to ECM degradation and initiating airway tissue remodelling \parencite{ref_132768}. While CS-induced protease-antiprotease imbalance provokes airway inflammation for COPD pathogenesis, MMP and NE play crucial roles in the progression of emphysema and COPD \parencite{ref_132769}. Additionally, EGFRs can be activated by EGF released from neutrophils, or they may be indirectly activated through the mechanisms of oxidative stress \parencite{ref_132754}. Then, EGFRs activate mitogen-activated protein kinases (MAPK), which upregulate the expression of MUC5AC and MUC5B and lead to hyperplasia of goblet cells and submucosal glands, resulting in mucus hypersecretion and hyperplasia \parencite{ref_132755}. Moreover, macrophages and epithelials tend to secret TGF-β to activate the fibrosis progress by proliferating epithelium, smooth muscle and fibroblasts. Thus, both the immune cells and airway epithelial cells are overly activated during the inflammation response and maintaining the stability of the pulmonary immune environment is a crucial problem in COPD treatment.\par
\par
\subsection{\textbf{Fibrosis}}
Most types of chronic lung injury, primarily COPD, can induce fibrosis in the lungs, which occurs during the airway remodelling phase induced by persistent inflammatory stimulation and irreversible lung injury. Initially targeted at susceptible lung cells, recurring injuries caused by viruses, cigarette smoke, and others provoke epithelial cell death. Repairing the injuries increased vascular permeability, allowing fibrinogen and fibronectin to format a provisional matrix. Meanwhile, recurring injuries promote bronchiolar and alveolar epithelial cell migration and proliferation, including the aberrant-activated epithelial cells producing diverse epidermal growth factors (EGF) and chemokines to encourage the response. MMP1 and MMP7 are significant contributors to the migration of epithelial cells \parencite{ref_132770}. Following the migration of fibroblasts and fibrocytes to the sites of injury, these cells release MMP2 and MMP9, which activate TGFβ1. This activation subsequently facilitates epithelial-mesenchymal transition and promotes the differentiation of fibroblasts into myofibroblasts \parencite{ref_132771}. Myofibroblasts secrete ECM accumulation in the foci, mainly fibrillar collagens and alpha smooth muscle actin (α-SMA), and can provoke additional epithelial apoptosis through different signalling ways \textcolor[HTML]{0082AA}{[52, 53]}.\par
In addition, lipopolysaccharide (LPS) and smoke stimulation can also promote the occurrence of epithelial-mesenchymal transition (EMT) by up-regulating NF-κB signalling, toll-like receptor 4 (TLR4)/c-Jun N-terminal kinase (JNK) signalling, and forkhead box O signalling \parencite{ref_132774}. Pulmonary fibrosis, a final stage of COPD, is a critical phase to prevent COPD from developing into a pulmonary malignant disease.\par
\par
\section{\textit{\textbf{Flavonoids in COPD regulation}}}
\par
Traditional Chinese medicine possesses distinctive advantages in the treatment of COPD due to its inherent natural properties and demonstrated efficacy. Noteworthy, flavonoids, as the principal constituents of natural ingredients, offer outstanding anti-inflammatory and antioxidant properties, prompting significant interest and attention in scientific and industrial domains. Flavonoids can be categorized into 7 subgroups based on their structural variances, which include chalcones, flavones, flavonoid glycosides, Isoflavones, catechins (flavanols), biflavones and others. We have summarized 28 flavonoids that have been used in the treatment of COPD and divided them into 6 categories, which can be visualized in \textcolor[HTML]{0082AA}{Figure 2}.\par
The different biological activities of flavonoids depend on the three rings C6-C3-C6 essential backbone and different substitution groups. In addition, we have made a detailed summary of the natural sources of flavonoids in order to provide a reference for the public’s daily diet. The leading information can be seen in \parencite{ref_132775,ref_132776,ref_132777,ref_132778,ref_132779,ref_132780,ref_132781,ref_132782,ref_132783,ref_132784,ref_132785,ref_132786,ref_132787,ref_132788,ref_132789,ref_132791,ref_132792,ref_132793,ref_132794,ref_132795,ref_132796,ref_132797,ref_132799,ref_132801,ref_132802,ref_132803,ref_132804,ref_132805,ref_132806,ref_132807,ref_132808,ref_132809,ref_132810,ref_132811,ref_132812,ref_132813,ref_132814,ref_132815,ref_132816,ref_132818,ref_132819,ref_132820,ref_132821,ref_132822,ref_132823,ref_132824,ref_132825,ref_132826,ref_132827,ref_132828,ref_132829,ref_132830,ref_132831,ref_132832,ref_132833,ref_132834,ref_132835,ref_132836,ref_132837,ref_132838,ref_132839,ref_132840,ref_132841,ref_132842,ref_132843,ref_132844,ref_132846,ref_132848}.\par
\par
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./image/image-60721.png}
\end{figure*}\par
\par
执行附表程序\par
\par
The use of natural compounds and traditional Chinese medicine for managing COPD has been increasingly recognised. Patients with COPD should consider integrating these treatments into their overall care plan. Specifically, flavonoids have the potential to regulate imbalances in protease-antiprotease activity, immune function, lung function changes, blood flow, and serum markers by addressing airway and lung inflammation, improving airway remodelling, reducing airway reactivity, and modulating oxidative-antioxidant processes. Below, we summarise the potential efficacy of flavonoids in the treatment of COPD by discussing the specific mechanisms of action of these compounds. The detailed experimental verification can be found in \parencite{ref_132775,ref_132776,ref_132777,ref_132778,ref_132779,ref_132780,ref_132781,ref_132782,ref_132783,ref_132784,ref_132785,ref_132786,ref_132787,ref_132788,ref_132789,ref_132791,ref_132792,ref_132793,ref_132794,ref_132795,ref_132796,ref_132797,ref_132799,ref_132801,ref_132802,ref_132803,ref_132804,ref_132805,ref_132806,ref_132807,ref_132808,ref_132809,ref_132810,ref_132811,ref_132812,ref_132813,ref_132814,ref_132815,ref_132816,ref_132818,ref_132819,ref_132820,ref_132821,ref_132822,ref_132823,ref_132824,ref_132825,ref_132826,ref_132827,ref_132828,ref_132829,ref_132830,ref_132831,ref_132832,ref_132833,ref_132834,ref_132835,ref_132836,ref_132837,ref_132838,ref_132839,ref_132840,ref_132841,ref_132842,ref_132843,ref_132844,ref_132846,ref_132848}.\par
\par
执行附表程序\par
\par
\subsection{\textbf{Flavonoids targeting oxidative stress }}
\textbf{Targeting ROS and antioxidative enzymes. }Oxidative stress accelerates the pathogenesis of COPD \parencite{ref_132849}. Harmful substances such as CS can cause inflammatory cells such as neutrophils and macrophages to accumulate in the lungs, producing large amounts of ROS \parencite{ref_132850}. In turn, ROS stimulates the expression of various inflammatory mediators in the lungs and results in hyper-mucus secretion by airway epithelial cells \textcolor[HTML]{0082AA}{[125, 126]}, aggravating the development of COPD.\par
Flavonoids have been reported to prevent and inhibit lung injury by scavenging free radicals. Flavonoids enhance intrinsic antioxidant capacity by increasing the levels and activities of antioxidant enzymes such as catalase, superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px), while also inhibiting ROS-generating enzymes like xanthine oxidase. For example, puerarin reversed mitochondrial membrane potential levels and ATP levels and decreased ROS content in cigarette smoke extract (CSE)-stimulated human bronchial epithelial cells (HBECs) \parencite{ref_132830}. Baicalin exerted an antioxidant role in CS-induced COPD mice by enhancing SOD, haem oxygenase 1 (HO-1) and reducing malondialdehyde (MDA) levels \parencite{ref_132813}. Liquiritin inhibited oxidant stress by increasing the SOD levels of the lung in CS-induced ICR mice \parencite{ref_132825}. Biochanin A increased the levels of antioxidant enzymes such as GSH-Px and decreased MDA, lactate dehydrogenase and alkaline phosphatase in a rat model of PM$_{{2.5}}$ exposure \parencite{ref_132829}. Apigenin enhanced the activation of silent information regulator 1 (SIRT1), nicotinamide adenine dinucleotide (NAD+), and the NAD+/NADH ratio in H$_{{2}}$O$_{{2}}$-induced WI-38 cells \parencite{ref_132781}. By regulating the balance between the antioxidant and ROS-generating enzymes, the flavonoids above can alleviate lung tissue lesions in COPD.\par
\textbf{Targeting Nrf2/HO-1.} Nrf2 serves as a pivotal transcription factor that governs the expression of genes associated with antioxidant activity. In patients with COPD, low levels of Nrf2 can diminish the body’s antioxidant capacity \parencite{ref_132853}. Under normal conditions, Kelch-like ECH-associated protein 1 sequesters Nrf2 in the cytoplasm. However, under oxidative stress, Nrf2 is released and translocated to the nucleus, where it initiates the transcription of various antioxidant genes, including glutathione (GSH), glutathione peroxidase 4 (GPX4), nicotinamide adenine dinucleotide phosphate (NADPH), NADPH quinone dehydrogenase 1, HO-1, 6-phosphogluconate dehydrogenase, thioredoxin, and the glucocorticoid receptor (GR) \textcolor[HTML]{0082AA}{[128, 129]}.\par
Various flavonoid monomers have been shown to have protective effects on airway epithelial cells and inflammatory cells. In summary, flavonoids were found to inhibit oxidative stress and inflammatory responses by targeting Nrf2 in different cell types, including epithelial cells, macrophages, and lung cancer cells, The mechanism of action can be visually illustrated in \textcolor[HTML]{0082AA}{Figure 3}. Casticin enhanced SOD activity but decreased ROS and MDA by regulating Keap1-Nrf2/antioxidant response element signalling way in H$_{{2}}$O$_{{2}}$-induced BEAS-2B cells \parencite{ref_132801}. Oroxylin A reduced the inflammatory response by increasing Nrf2 levels in CS-damaged RAW 264.7 cells \parencite{ref_132782}. Luteolin decreased total ROS and mitochondrial ROS concentrations by inhibiting CYP2A13 expression while simultaneously increasing Nrf2 levels in CSE-treated A549 cells \parencite{ref_132803}.\par
\par
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./image/file_67aee377039e2.jpg}
\end{figure*}\par
\par
In mice and rat models of COPD, various flavonoids have likewise been proved to inhibit the development of COPD by modulating Nrf2-related oxidative stress to attenuate lung damage and inflammatory responses. In a four-days CS-induced mouse model, Oroxylin A increased GSH, GR, GPx-2 and HO-1 levels by promoting Nrf2 binding to antioxidant response element \parencite{ref_132782}. In a CS-induced mouse model, isoliquiritigenin reduced MDA levels by enhancing the expression of Nrf2 and HO-1 \parencite{ref_132777}. Fisetin was demonstrated to prevent lung damage and attenuate oxidative stress and inflammation via Nrf2-mediated antioxidant factors in CS-induced rat model, including decreasing inflammatory cytokines such as IL-4, IL-1β, TNF-α and increasing antioxidant enzymes such as SOD, GSH \parencite{ref_132794}. Epicatechin repressed the production of ROS by enhancing the Nrf2-induced anti-oxidant enzyme and then reduced IL-18 and IL-1β levels by inhibiting the NLRP3-Caspase-1 pathway in the CS-induced rat \parencite{ref_132833}. Luteolin inhibited oxidative stress by decreasing the TRPV1 and CYP2A13 proteins while increasing SIRT6 and Nrf2 levels in CSE-treated A549 cells and CS- and LPS-induced COPD mice \parencite{ref_132803}. Additionally, flavonoids could target Nrf2 and reverse dexamethasone tolerance during COPD treatment. Quercetin was reported to reverse the dexamethasone-tolerance in CSE-induced U937 cells by activating adenosine 5’-monophosphate-activated protein kinase (AMPK) and Nrf2 signalling way \parencite{ref_132785}.\par
Total flavonoids of natural plants have also been reported to inhibit oxidative stress in COPD by targeting Nrf2. Dandelion total flavonoids improved lung function and reduced oxidative stress-induced lung damage by upregulating antioxidant enzymes such as SOD and GSH in mice exposed to CS, linked to the activation of the Nrf2 signaling pathway \parencite{ref_132848}.\par
\par
\subsection{\textbf{Flavonoids targeting inflammation}}
Inflammation is a vital component in the initiation and advancement of COPD. The infiltration of neutrophils demonstrates a positive correlation with the severity of airflow obstruction. Harmful substances induce the respiratory tract epithelium to secrete inflammatory cytokines and chemokines. Likely, TNF-α facilitates leukocyte migration and accumulation by stimulating pulmonary endothelial cells, releasing lysosomal enzymes, elastase, and ROS, all detrimental to endothelial cells and the alveolar epithelium \parencite{ref_132856}. IL-6 activates neutrophils, releasing NE and ROS, which damage alveolar surfactants, increase pulmonary vascular permeability, and induce pulmonary edema \parencite{ref_132857}. Additionally, monocyte chemotactic protein-1 and macrophage inflammatory protein-1α serve as chemoattractants, recruiting inflammatory cells such as macrophages and lymphocytes into inflamed tissues.\par
Flavonoids possess strong anti-inflammatory properties in various inflammatory diseases, effectively inhibiting the onset and progression of COPD. Additionally, flavonoids play a significant role in addressing respiratory diseases closely linked to inflammation. We will focus on the anti-COPD effect of flavonoids and their related mechanisms of action, including their relationship with NF-κB signalling pathway, MAPK signalling pathway, EGFR signalling pathway, and immune cells migration. The different mechanisms of action that flavonoids protect COPD can be seen intuitively in \textcolor[HTML]{0082AA}{Figure 4}.\par
\textbf{Targeting NF-κ}\textbf{B pathway.} The activation of the NF-κB pathway is believed to play a pivotal role in inflammatory diseases, including airway and pulmonary inflammation. Under normal conditions, NF-κB remains inactive in the cytosol, as it is bound to the inhibitor kappa B (IκB). Upon stimulation of airway epithelial receptors, IκB kinase is activated, leading to the phosphorylation of IκBα or IκBβ. This phosphorylation results in the dissociation of IκBα or IκBβ from NF-κB. Subsequently, NF-κB (comprising p50 and p65 subunits) translocates to the nucleus, where it activates the expression of genes encoding inflammatory cytokines and chemokines, such as \textit{TNF-α}, \textit{IL-1β}, and \textit{IL-6} \parencite{ref_132858}. Notably, activated NF-κB in the lung tissue of COPD patients were significantly elevated, while IκB levels were notably reduced \parencite{ref_132859}. Furthermore, the inhibition of the NF-κB pathway led to a decrease in the presence of respiratory mucus, specifically MUC5AC and MUC5B \parencite{ref_132860}.\par
\par
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./image/file_68999e3c72e62.png}
\end{figure*}\par
\par
Researchers have identified a range of natural products that modulate NF-κB signalling in COPD, acknowledging NF-κB’s significance in chronic inflammatory conditions. Casticin was reported to inhibit the apoptosis and pro-inflammatory cytokines (TNF-α, IL-6, IL-1β, and IFN-γ) production by down-regulating p-p65 but increasing Nrf2 in LPS-induced BEAS-2B cell \parencite{ref_132799}. Epigallocatechin gallate diminished the lipid peroxidation, ROS production and inflammation mediator levels (COX-2, NADPH oxidase 4 (NOX4), NOS2, IL-6 and IL-8) by suppressing the activation of NF-κB pathway in CSE-stimulated airway epithelial cells \parencite{ref_132834}. Baicalin inhibited the TNF-α, IL-8, and MUC5AC levels by decreasing NF-κB p65 phosphorylation in IL-1β-induced NCI-H292 cells \parencite{ref_132818}, in CSE-induced type II pneumocytes and in CS-exposed rat \parencite{ref_132811}. In a CS-induced mouse model, Isoliquiritigenin reduced the phosphorylation of p65 and IκB, attenuating the inflammatory response and reducing the inflammatory factors \parencite{ref_132777}. Casticin improved lung function and reduced the number of white blood cells, neutrophils, and macrophages and the level of leptin, C-reactive protein, and pro-inflammatory cytokines by inhibiting the NF-ĸB and iNOS pathway \parencite{ref_132797}. Taxifolin significantly suppressed elevated IL-1β, IL-6 and TNF-α levels in COPD mouse lung tissue and CSE-treated HBECs by inhibiting the phosphorylation p65 NF-κB \parencite{ref_132810}.\par
In addition, glucocorticoid drugs are often used to improve airflow limitation and attenuate the inflammation of COPD in clinical, which usually induces drug tolerance at the same time. Several kinds of flavonoids decreased the inflammation levels and reversed the glucocorticoid drug tolerance at the same time. In CS exposure mice and pig models, naringenin not only significantly improved the pulmonary function but decreased the accumulation of inflammatory cells and pro-inflammatory cytokines such as IL-8, TNF-α, and MMP9 by inhibiting phosphorylation of NF-κB but increasing GR, which indicate that naringenin enhances the corticosteroid sensibility \textcolor[HTML]{0082AA}{[48, 82]}. Icariin demonstrated a significant reduction in inflammation, airway remodelling, and ROS production that is induced by CSE. Furthermore, it alleviated glucocorticoid resistance through the down-regulation of the Nrf2 and NF-κB signalling pathways \parencite{ref_132822}.\par
Flavonoids can directly act on the NF-κB pathway to improve COPD inflammation. However, as a core pathway, its upstream regulators, including toll-like receptors (TLRs) and sirtuins, also regulate its activation.\par
TLRs are a family of receptors that detect pathogens and tissue damage. Among them, TLR2 recognizes lipoteichoic acid from gram-positive bacteria, and TLR4 is activated by LPS and other endogenous ligands \parencite{ref_132861}, which were reported to be up-regulated and activated during COPD initiation and development, especially in the COPD co-infection model. Upon stimulation, myeloid differentiation primary response gene 88 recruits the interleukin-1 receptor-associated kinase (IRAK) family member IRAK4 to TLRs and induces the phosphorylation of IRAK1, which subsequently activates the NF-κB pathway \parencite{ref_132862}. In CS and LPS co-induced COPD rat model, baicalin reduced TLR2 and TLR4, followed by lowering MYD88, phosphorylation of p65, and increasing phosphorylation of IκBa \parencite{ref_132819}, Total flavonoids of \textit{Trollius altaicus} downregulated TLR4 to reduce the activation of IRAK-1, thereby reducing the activation of the NF-κB and reducing IL-1β, IL-6, IL-8, TNF-α \parencite{ref_132844}. In long-term cs-exposed rats, casticin reduced TLR4 expression to inhibit the NF-κB phosphorylation and increase the IκBα phosphorylation level, thereby reducing inflammation \parencite{ref_132797}. TLRs are crucial targets of flavonoids in inhibiting COPD-associated inflammation, especially in infection-associated COPD development.\par
SIRT1, one of seven-number families with different subcellular localisations and functions, is the first sirtuin that regulates inflammatory, oxidative stress, autophagy, and apoptosis, which can inhibit the activation of NF-κB. The heightened inflammatory response may reduce SIRT1 levels in patients with COPD \parencite{ref_132863}, and a high level of SIRT1 inhibits inflammatory reactions in lung cells stimulated by CS \parencite{ref_132864}. Quercetin increased SIRT1 levels to reduce inflammatory responses and oxidative stress in mice with COPD induced by LPS and elastase \parencite{ref_132784}. Myricetin reduced IL-6 and IL-8 levels in TNF-α-induced A549 cells by regulating the SIRT1/NF-κB pathway \parencite{ref_132795}. Hesperetin alleviated inflammation and oxidative stress responses by SIRT1/PGC-1a/NF-κB signalling axis in CES-induced COPD mice \parencite{ref_132807}. Besides, total flavonoids of \textit{Scutellaria baicalensis }Georgi also inhibited inflammatory and oxidative stress by increasing SIRT1 and PGC-1a levels in CS- and LPS- co-induced mice models \parencite{ref_132842}.\par
There are also several other targets for flavonoids to regulate NF-κB during COPD inflammation, such as protein kinase C (PKC)δ, peroxisome proliferator-activated receptor γ, HDAC2 and NOX4. Fisetin has been reported to directly bind to PKCδ, preventing the activation of NF-κB and the expression of IL-8 in HEK293T and NCI-H292 cells induced by TNF-α \parencite{ref_132793}. Icariin promoted lung function and reduced TNF-α and IL-6 levels in bronchoalveolar lavage fluid and serum of COPD rat model, and indeed up-regulated the peroxisome proliferator-activated receptor γ expression and inhibited the activation of p65 of lung and epithelial cells \parencite{ref_132824}. Baicalin treatment markedly attenuated the inflammatory effects through down-regulating airway inflammatory infiltration and decreasing the inflammatory factors (the levels of TNF-α and IL-1β) in CS/CSE-exposed rats and cells, which attributed to the enhancement of HDAC2 protein expression and inhibition of NF-κB or even targeting its downstream effector PAI-1 \textcolor[HTML]{0082AA}{[89, 92]}. Luteolin reduces inflammation and oxidative stress via the NOX4-mediated NF-κB pathway in CS-induced cell and mice \parencite{ref_132802}.\par
Total flavonoid extract also has the effect of inhibiting the NF-κB pathway. For example, total flavonoids from \textit{Nigella glandulifera} relieved the pathological state, inhibited the infiltration of neutrophils and macrophages into the lung and decreased levels of TNF-α and IL-8 via the NF-κB pathway \parencite{ref_132840}. Total flavonoids from loquat (\textit{Eriobotrya }\textit{japonica}) leaves alleviated oxidative stress-induced lung injury, emphysema and inflammation by inhibiting NF-κB and JNK phosphorylation \parencite{ref_132843}.\par
\textbf{Targeting MAPK pathway. }MAPKs represent a diverse family of kinases that are critical components of various signal transduction pathways. The MAPK family comprises four subfamilies: extracellular regulated protein kinases (ERK)1/2 (extracellular signal-regulated kinases 1 and 2), ERK5, JNKs, and p38s (p38 MAPKs). Notably, p38 MAPK is activated in response to environmental stress and inflammatory stimuli. Of its subtypes, p38α is particularly abundant in inflammatory cells \parencite{ref_132865}. Moreover, elevated levels of p38 MAPK have been detected in the airways and sputum of COPD patients \parencite{ref_132866}. Furthermore, it has been demonstrated that pathogens can augment MUC5AC mucin secretion by activating the p38 MAPK pathway while simultaneously inhibiting the PI3K-Akt pathway \parencite{ref_132867}.\par
In mice exposed to CS, astragalin demonstrated an ability to attenuate inflammation and oxidative stress induced by pulmonary thrombus. This effect was mediated by a reduction of PAR-1 and PAR-2, which subsequently inhibited the production of ROS and the expression of COX-2, iNOS, and ICAM-1. The mechanism involved the inhibition of the ERK, p38, and JNK signalling pathways \parencite{ref_132821}. Silymarin significantly alleviated the thickening of the airway epithelium and infiltration of peribronchial inflammatory cells (including total cells, macrophages, and neutrophils) while also reducing levels of pro-inflammatory factors such as TNF-α, IL-1β, and IL-8 by attenuating the phosphorylation of ERK and p38 in CS-exposed mice, the results were also proved in CSE-induced Beas-2B cells \textcolor[HTML]{0082AA}{[113, 114]}. The total flavonoids of \textit{Trollius altaicus} protected lung function by inhibiting the activation of p38 and ERK in CS-induced mice \parencite{ref_132846}.\par
Bacterial infection stands as a primary culprit behind acute exacerbations of COPD. These exacerbations correlate with a notable rise in both airway and systemic inflammation \parencite{ref_132868}. Flavonoids alleviate the pathological inflammation associated with COPD combined with bacterial infection by inhibiting the activation of MAPKs. Appling LPS/CSE-induced acute exacerbations of COPD mouse models, researchers discovered that: Hydroxysafflor yellow A decreased levels of IL-6, IL-1β, and TNF-α by inhibiting the phosphorylation of ERK, p38, and JNK, and to inhibit inflammatory mediator by decreasing levels of p-p38 and p-p65, as well as in platelet-activating factor (PAF)-stimulated HSAECs \textcolor[HTML]{0082AA}{[59, 60]}; Baicalin reversed elevated levels of IL-8, IL-6, TNF-α and MUC5AC which is associated with the inhibition of JNK activation \parencite{ref_132814}; Silibinin inhibited the expression of MUC5AC, IL-6, and IL-1β by suppressing the activation of ERK and specificity protein 1, as well as in CS condensate-stimulated NCI-H292 cells \parencite{ref_132835}. Total flavonoids of sea buckthorn inhibited expression of IL-1β, IL-6, CXCL1, prostaglandin E2, cyclooxygenase-2, and MUC5AC through the inhibition of the ERK, PI3K/Akt, and PKCα pathways, as well as in LPS/CSE-induced HBE16 cells \parencite{ref_132841}.\par
\textbf{Targeting EGFR pathway.} The EGF and its receptor (EGFR) are essential for causing and promoting various cell growth, proliferation, and transformation processes. Patients with COPD have higher levels of EGFR in their lungs compared to smokers without COPD symptoms \parencite{ref_132869}. This suggests that EGFR plays a significant role in the development of COPD. The increased EGFR levels are linked to the growth of airway epithelial goblet cells and more mucus production \parencite{ref_132754}. EGFR activation initiates key signaling pathways, including PI3K/protein kinase B (AKT)/mTOR, RAS/MEK/ERK, and MAPK p38 \parencite{ref_132870}.\par
Flavonoids inhibit the activation of the EGFR signalling pathway, making them promising for treating lung diseases. In CS-exposed mice and CSE-induced NCI-H292 cells, phloretin decreased EGFR phosphorylation, reducing ERK phosphorylation and p38 expression and decreasing mucin expression and inflammation \parencite{ref_132775}. In the LPS-induced airway mucus hypersecretion rat model, quercetin reduced the expression level of MUC5AC by inhibiting the activation of the EGFR, as evidenced by decreased levels of p-EGFR/EGFR, p-PI3K/PI3K, p-PKC/PKC, p-AKT/AKT, and NF-κB \parencite{ref_132789}. Additionally, in EGF-stimulated H292 cells or A549 cells, tilianin also reduced MUC5AC expression by modulating the expression of AKT, ERK, and p38 in EGF-stimulated NCI-H292 human airway epithelial cells. However, it did not affect A549 cells \parencite{ref_132826}. Furthermore, icariin was shown to promote lung function and reduce pro-inflammatory cytokines by inhibiting the phosphorylation of PI3K, AKT, and p38 \parencite{ref_132823}.\par
\textbf{Immune cells migration. }The inflammatory process of COPD is characterized by the persistent migration of inflammatory cells from the blood vessels to the lungs. This includes neutrophils, macrophages, and T cells, which produce a plethora of cytokines that exacerbate inflammation.\par
Casticin was found to inhibit immune cell infiltration in the lungs, including neutrophils, macrophages, and lymphocytes. This effect was accompanied by a downregulation of TNF-α, IL-6, IL-1β, and monocyte chemotactic protein-1 levels in bronchoalveolar lavage fluid of CS-exposed mice \parencite{ref_132796}. Phloretin was shown to reduce the level of CXCL1, a neutrophil chemoattractant, which was induced by nontypeable \textit{Haemophilus influenzae} in a COPD model of infection \parencite{ref_132776}. Quercetin improved the lung function and alleviated goblet cells metaplasia and emphysematous by down-regulating the inflammation mediators levels (such as CXCL-1, CXCL-10, TNF-α, IFN-γ IL-13 and IL-17A) and infiltration of immune cells (such as leukocytes, total lymphocytes, CD11b$^{{+}}$CD11c$^{{+}}$ macrophages, neutrophils and CD8$^{{+}}$T cells) in both CS-induced and rhinovirus/CS-induced COPD mice \textcolor[HTML]{0082AA}{[66, 70]}. Quercetin also improved epithelial regeneration and reduced TGF-β, IL-6 and IL-8 airway basal cells of COPD patients \parencite{ref_132792}. Tilianin inhibited neutrophil infiltration in the lung by affecting CXCL2, IL-17/STAT3 signal pathways in the COPD mice model \parencite{ref_132827}. Ginkgetin decreased CCL2 levels by downregulating the c/EBPβ signalling pathway in CSE-induced A549 cells, which is associated with immune cell migration such as macrophages \parencite{ref_132839}. Additionally, naringenin inhibited the M1 polarization of macrophages induced by extracellular vesicles from CSE-induced epithelial cells and reduced inflammatory cells and myeloperoxidase in CS-exposed guinea pigs \textcolor[HTML]{0082AA}{[81, 83]}. Additionally, naringin has a protective effect on NEP activity by reducing SP and the expression of the NK-1 receptor, thereby relieving the cough symptoms in CS-exposed guinea pigs \parencite{ref_132828}.\par
\par
\subsection{\textbf{Flavonoids targeting airway remodelling and fibrosis}}
There is currently insufficient data on how flavonoids affect the anti-COPD fibrosis process. These studies primarily focus on the formation and degradation of ECM and the process by which fibrotic cells are formed. \textcolor[HTML]{0082AA}{Figure 5} offers more detailed insights into the specific mechanisms of action involved.\par
\par
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./image/zipimg67aea6fbb71406.png}
\end{figure*}\par
\par
\textbf{ECM degradation.} Proteases are key in remodelling tissue and promoting inflammation \parencite{ref_132768}. An imbalance between proteases and anti-proteases disrupts ECM, which is essential for maintaining the dynamic integrity of organs. MMPs, serine proteases, and caspases are the three main elastase types responsible for hydrolysis peptides and other proteins. MMP-2, MMP-8, MMP-9, and NE are most involved in emphysema and COPD \textcolor[HTML]{0082AA}{[49, 145-147]}. Among them, MMPs are a prominent and influential family. Tissue inhibitors of metalloproteinases (TIMPs) are endogenous inhibitors of MMPs; usually, TIMP-1 inhibits active MMPs, and TIMP-1 binds to pro-MMP-9 to prevent the activation of pro-MMP-9. However, under harmful conditions, the combination of TIMP-1 and pro-MMP-9 dissociates by NE action, and MMP-3 activates pro-MMP-9 to MMP-9 \parencite{ref_132874}. NE, cathepsin G, and proteinase-3 are three main types of serine proteases that are also destroyed by degrading ECM components of lung tissue \parencite{ref_132875}. ECM degradation releases collagen and elastin, which attract immune cells and cause ongoing airway inflammation in the lungs.\par
In CS-exposed mice, quercetin \parencite{ref_132784}, naringenin \parencite{ref_132805}, hesperidin \parencite{ref_132808}, and baicalein all downregulated MMP9 levels and reduced inflammation \parencite{ref_132813}. Baicalein also reduced TIMP-1, MMP2, MMP9, and MMP8 \parencite{ref_132812}. In CSE-induced BEAS-2B cells, icariin reduced the expression of MMP-9 and TIMP1 \parencite{ref_132822}. Thus, flavonoids inhibited ECM degradation and maintained alveolar integrity in the early phase of COPD development.\par
\textbf{Fibrosis.} In patients with COPD, airway epithelial cells show higher levels of TGF-β1 expression, which contributes to fibrotic airway remodelling and a decline in lung function \parencite{ref_132876}. TGF-β activates TGFBR, promoting the phosphorylation of Smad2/3. The phosphorylated Smad2/3 subsequently binds to Smad4, initiating the transcription of genes related to airway remodellings, such as \textit{α-SMA} and collagen. This process alters the EMT \parencite{ref_132877}. Additionally, TGF-β signalling upregulates MMPs, facilitating EMT and resulting in the degradation and destruction of lung tissue. Furthermore, TGF-β induces the EGF/EGFR signalling pathway, which synergistically promotes EMT-related phenotypic changes. TGF-β1 also stimulates the activity of neutrophils, macrophages, and mast cells.\par
Hydroxysafflor yellow A reduced the expression of TGF-β1 in a rat model of LPS-CS co-infection and alleviated pulmonary fibrosis by decreasing the accumulation of α-SMA and collagen I, thereby improving lung function \parencite{ref_132778}. In four-day CS-exposure mice, liquiritin apioside reduced inflammatory response and TGF-β1 level. The same results were performed in CSE-induced A549 \parencite{ref_132825}. In a three-month CS-exposed mouse model, quercetin also reduced TGF-β1 and α-SMA in the lungs \parencite{ref_132787}. In the CS-exposed mouse model, silibinin decreased collagen accumulation by modulating the TGF-β1/p-Smad 2/3 signalling axis and simultaneously attenuated the expression of inflammatory factors \parencite{ref_132836}. Besides, in the LPS-CS co-induced rat model, total flavonoids of \textit{Trollius altaicus} were shown to decrease TGF-β1 levels \parencite{ref_132844}. Both short-term and long-term exposure to smoke can result in lung inflammation and cellular damage, leading to the production of TGF-β1, implicating the initiation of pulmonary fibrosis. However, flavonoids, such as hydroxysafflor yellow A, liquiritin apioside, quercetin, silibinin, and total flavonoids of \textit{Trollius altaicus} may help prevent the progression of COPD to pulmonary fibrosis by reducing TGF-β. Compared with the anti-inflammatory and antioxidant studies of flavonoids, there are few studies on anti-pulmonary fibrosis, and most of them only reduce TGF-β, which suggests that more extensive and in-depth research is needed.\par
\par
\subsection{\textbf{Flavonoids targeting programmed cell death}}
\textbf{Targeting apoptosis.} Apoptosis is a physiological or pathological response of cells to various stimuli, including DNA damage, oxidative stress, and inflammation. This process is complex and tightly regulated by multiple genes, such as the \textit{Bcl-2} family, the caspase family, the oncogene \textit{C-myc}, and the tumour suppressor gene \textit{P53}, as well as by various molecular signals, including the death receptor pathway and the mitochondrial pathway \parencite{ref_132878}. Research shows that the apoptosis (cell death) of lung structural cells is a crucial factor in the development of COPD. In patients with COPD, there is an observed increase in the apoptosis of alveolar epithelial and endothelial cells. This increase cannot be compensated for by the proliferation of structural cells, leading to the destruction of lung tissue and the development of emphysema \parencite{ref_132879}.\par
Flavonoids have been reported to act as positive protectors against apoptosis in the pathogenesis of COPD. For instance, baicalin has been shown to inhibit the expression of HSP72 and alleviate apoptosis in CSE-treated MLE-12 cells, which are mouse lung type II epithelial cells \parencite{ref_132814}. Additionally, baicalin mitigates the apoptosis of LPS- or CSE-induced 16HBE cells by increasing Bcl-2 and CyclinD1 but increasing B-cell lymphoma 2-associated X (Bax) and P21 \textcolor[HTML]{0082AA}{[92, 93, 96]}. Taxifolin inhibited apoptosis by suppressing Bax and CCP3 levels and increasing Bcl-2 levels in COPD mouse lung tissue and CSE-treated HBECs \parencite{ref_132810}. Hesperetin is capable of suppressing the protein expression of AKT1, IL6, VEGFA, and MMP9 while up-regulating the protein expression of TP53, thereby impeding COPD to lung cancer \parencite{ref_132808}. Furthermore, flavonoids regulate autophagy-related apoptosis. Puerarin has been demonstrated to inhibit FUNDC1-mediated mitochondrial autophagy and CSE-induced apoptosis in human bronchial epithelial cells by activating the PI3K/AKT/mTOR signalling pathway \parencite{ref_132830}. Puerarin also protects lung function and inhibits apoptosis by down-regulating the Bax level and up-regulating Bcl-2 in the lung, a process that depends on the PINK1-parkin signalling pathway mediated mitophagy \parencite{ref_132831}. Moreover, formononetin can attenuate CS-induced inflammation, endoplasmic reticulum stress, and apoptosis in bronchial epithelial cells through the inhibition of AhR/CYP1A1 and AKT/mTOR signalling pathways \parencite{ref_132832}. In addition, flavonoids inhibit apoptosis associated with inflammation. Biochanin A has been shown to reduce PM2.5-induced apoptosis and the production of pro-inflammatory factors, such as TNF-α, IL-2, IL-6, and IL-8 \parencite{ref_132829}.\par
\textbf{Targeting ferroptosis.} Ferroptosis, a form of regulated cell death, is driven by an excessive accumulation of iron, ROS, and lipid peroxides. This process leads to a decrease in GSH and the inactivation of GPX4 \parencite{ref_132880}. Ferroptosis is increasingly recognised as a critical factor in COPD, with levels elevated in free iron, lipid peroxidation, and inflammatory responses in mice, which are reversed by the overexpression of the GPX4 \parencite{ref_132881}. GPX4, a negative regulator of ferroptosis, was significantly downregulated in HBECs, which correlates with exacerbations of airway obstruction. Additionally, acyl-CoA synthetase long-chain family member 4 (ACSL4) has been identified as a significant contributor to sensitivity to ferroptosis and was significantly upregulated \parencite{ref_132882}. Furthermore, Nrf2 plays a protective role by inhibiting ferroptosis through the scavenging of ROS and the promotion of GSH synthesis \parencite{ref_132883}.\par
Scutellarein may help treat COPD by inhibiting ferroptosis through iron chelation and interaction with the enzyme arachidonate 15-lipoxygenase, which is involved in fatty acid oxidation. Besides, scutellarein significantly inhibited Ras-selective lethal small molecule 3-induced ferroptosis and mitochondria injury \parencite{ref_132783}. Dihydroquercetin significantly inhibited the ferroptosis induced by CS through the Nrf2-dependent signalling pathway. It notably impeded the increasing of lipid peroxidation and morphological changes in the mitochondria by up-regulating SLC7A11, GPx4, and SOD by down-regulating MDA and ROS \parencite{ref_132809}. Naringenin increased the extracellular vesicles miR-23a-3p level of CSE-induced alveolar macrophages and inhibited lung epithelial ferroptosis by targeting ACSL4 \parencite{ref_132806}. Thus, flavonoids could alleviate the ferroptosis-induced injury and oxidative stress by chelating iron and activating the downstream anti-oxidant pathway during the progression of COPD.\par
\par
\subsection{\textbf{Flavonoids’ safety}}
Flavonoids have a long history of consumption in diets with high-rich flavonoids, and a high intake of flavonoids has not caused any side effects. As a result, it is unlikely that dietary sources could provide doses sufficient to cause mutations or cytotoxicity. However, pharmacological and clinical experiments have shown that a variety of flavonoid preparations have certain toxicity, consult \textcolor[HTML]{0082AA}{Table 3 [68, 158-171]} for additional details. For example, chalcones, such as isoliquiritigenin has mortality and malformationand \parencite{ref_132884}, hydroxysafflor yellow A has a slight nephrotoxicity in long term \parencite{ref_132885}; Flavone glycosidescertain, baicalin has certain renal toxicity \parencite{ref_132886}. Isoflavones, such as puerarin has the severity of adverse drug reactions depends on the choice of infusion solvent \parencite{ref_132887}, not the dosage of puerarin, formononetin has mortality at acute dose 300 mg/kg and LD50 at 103.6 mg/kg \parencite{ref_132888}. Bisflavonoid, such as ginkgetin has potential hepatic and renal toxicity \parencite{ref_132889}. There are tens of thousands of types of flavonoids, and the safety of each flavonoid compound is extensive, so it is of great significance to investigate the side effects of daily flavonoid supplementation.\par
\par
执行附表程序\par
\par
\section{\textit{\textbf{Conclusion and prospect}}}
\par
Natural flavonoids are a class of compounds commonly found in various foods, and herbs derives its properties from a wide range of plants, including apples, celery, bayberry, citrus, tea, and ginkgo, as well as astragalus and licorice. A significant body of research exists regarding the flavonoid content in different food sources. Among the flavonols and flavones, quercetin is the most prevalent compound which is particularly abundant in onions and tea. Other noteworthy flavonoids include kaempferol, myricetin, as well as the flavones apigenin and luteolin.\par
COPD is a prevalent and fatal chronic lung disease that poses a significant burden on global healthcare systems. Despite its high incidence, the mechanisms underlying the development of COPD remain elusive, and there are currently no drugs available to prevent or reverse its initiation and progression. As discussed in this review, flavonoids can target multiple pathways to inhibit the development of COPD, including regulating oxidative stress by targeting ROS and Nrf2/HO-1, reduce inflammation by targeting NF-κB, MAPK, and EGFR pathway, remodel airway by reducing ECM and fibrosis, and regulate programmed cell death by targeting apoptosis and ferroptosis. All of this indicates that flavonoids offer unique advantages in drug discovery and adjuvant therapy but also suggests that foods rich in flavonoids can be used to prevent the occurrence of COPD.\par
The structure of flavonoids is both complex and diverse. As discussed in this review, the structures of flavonoids exhibiting therapeutic effects on COPD vary significantly. Notably, the number and position of hydroxyl groups influence flavonoids’ activity and mechanism of action, with the ortho-hydroxyl group playing a particularly critical role. Additionally, glycosylation represents another important structural feature that impacts efficacy. However, research related to COPD has not thoroughly and specifically addressed the structure-activity relationships of flavonoids. Elucidating these relationships, along with conducting structural modifications and optimisations, is essential for guiding the development of anti-COPD drugs.\par
A variety of flavonoids have been developed into pharmaceuticals, cosmetics, and health foods, such as ginkgo flavonoids, tea polyphenols, and puerarin. Flavonoids as dietary supplements are not regulated as strictly as drugs, do not need to be reviewed by the FDA, and are not thoroughly evaluated for potential toxicity. When people use these drugs, cosmetics, or health foods, the recommended intake of flavonoids is much higher than in the daily diet, usually at the gram level. Therefore, people need to pay attention to the dosage when supplementing with additional flavonoid preparations to avoid adverse reactions.\par
Many flavonoid compounds, as well as flavonoid-rich diets and medications, are utilized in clinical treatments, demonstrating their relative safety for human health, only quercetin has explicitly been tested in clinical trials for treating COPD \parencite{ref_132788}. Consequently, which flavonoid is most suitable for COPD therapy remains unclear, underscoring the need for further preclinical and clinical research. Currently, there have been only two clinical trials involving quercetin and a flavonoid-rich diet for COPD \textcolor[HTML]{0082AA}{[13, 14]}. Nonetheless, numerous other clinical trials have established the safety of flavonoids, which can serve as a reference for conducting trials related to COPD. In addition, the combination of flavonoid compounds with other drugs is also an important strategy.\par
This article provides a comprehensive overview of flavonoids employed in the treatment of COPD. It delineates their primary sources, structural classifications, and the various mechanisms of action and therapeutic targets. This information serves as a crucial reference for the dietary management and treatment strategies for patients suffering from COPD. Furthermore, it offers valuable insights for researchers concerning structural attributes, pharmacological effects, and potential toxicities. Additionally, the article establishes a foundation for future clinical trials aimed at advancing the treatment methodologies for COPD.\par
\par
\textit{\textbf{Author contributions}}\par
Shi PL collected documents and wrote the manuscript; Zhang GX, Wang PY, and Liu ZQ helped with information collection and manuscript editing; Zheng BQ revised the manuscript for important content and the manuscript preparation and editing.\par
\textit{\textbf{Competing interests}}\par
The authors declare no conflicts of interest.\par
\textit{\textbf{Acknowledgments}}\par
This work was supported by the Shandong Provincial Traditional Chinese Medicine Science and Technology Project “Study on the mechanism of Astragalus polysaccharide inhibiting the occurrence of liver fibrosis through PD-1 regulating NK cell function (2020Q004)”; Shandong Provincial Medical and Health Science and Technology Development Project “Mechanism of STAT3 inhibition of liver fibrosis by regulating PD-1$^{{+}}$NK cells (202002070991)”. Natural Science Foundation of Shandong Province “Astragalus polysaccharides inhibit liver fibrosis by regulating CD49a$^{{+}}$NK cells (ZR2022QH111).”\par
\textit{\textbf{Peer rev}}\textit{\textbf{iew information}}\par
\textit{Traditional Medicine Research} thanks Hai-Jing Zhong, He Li and another anonymous reviewer for their contribution to the peer review of this paper.\par
\textit{\textbf{Abbreviations}}\par
COPD, chronic obstructive pulmonary disease; CS, cigarette smoke; ROS, reactive oxygen species; ECM, extracellular matrix; EGFR, epidermal growth factor receptor; NF-κB, nuclear factor kappa-B; TNF-α, tumor necrosis factor-alpha; TGF-β, transforming growth factor-β; IL-6, interleukin-6; IL-1β, interleukin-1 beta; MMP9, matrix metalloproteinase 9; HDAC2, histone deacetylase-2; PI3K, phosphoinositide-3- kinase; Nrf2, nuclear factor erythroid 2-related factor 2; CXCL, C-X-C motif chemokine ligand; NE, neutrophil elastase; MAPK, mitogen-activated protein kinase; EGF, epidermal growth factor; LPS, lipopolysaccharide; TLR4, toll-like receptor 4; JNK, c-Jun N-terminal kinase; SOD, superoxide dismutase; GSH, glutathione; GSH-Px, glutathione peroxidase; HO-1, haem oxygenase 1; MDA, malondialdehyde; SIRT1, silent information regulator 1; NAD+, nicotinamide adenine dinucleotide; GPX4, glutathione peroxidase 4; NADPH, nicotinamide adenine dinucleotide phosphate; GR, glucocorticoid receptor; CSE, cigarette smoke extract; IκB, inhibitor kappa B; HBECs, human bronchial epithelial cells; TLRs, toll-like receptors; IRAK, interleukin-1 receptor-associated kinase; PKC, protein kinase C; NOX4, NADPH oxidase 4; TIMP, tissue inhibitors of metalloproteinase; EMT, epithelial-mesenchymal transition; α-SMA, alpha smooth muscle actin; ERK, extracellular regulated protein kinases; AKT, protein kinase B.\par
\textit{\textbf{Citation}}\par
Shi PL, Zhang GX, Wang PY, Liu ZQ, Zheng BQ. Natural flavonoids for the treatment of chronic obstructive pulmonary disease: An overview. \textit{Tradit Med Res}. 2025;10(9):57. doi: 10.53388/TMR20241121001.\par
\par
\textbf{Executive editor: }Xin-Yue Zhang.\par
\textbf{Received: }22 November 2024; \textbf{Revised:} 01 January 2025; \textbf{Accepted: }27 February 2025; \textbf{Available online:} 03 March 2025.\par
© 2025 By Author(s). Published by TMR Publishing Group Limited. This is an open access article under the CC-BY license. (https://creativecommons.org/licenses/by/4.0/)\par
\nocite{*}
\printbibliography[title={References}]
\end{document}

125
article_6762.aux Normal file
View File

@@ -0,0 +1,125 @@
\relax
\providecommand\zref@newlabel[2]{}
\providecommand\hyper@newdestlabel[2]{}
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\abx@aux@refcontext{none/global//global/global/global}
\abx@aux@cite{0}{ref_199959}
\abx@aux@segm{0}{0}{ref_199959}
\abx@aux@cite{0}{ref_199962}
\abx@aux@segm{0}{0}{ref_199962}
\abx@aux@cite{0}{ref_199963}
\abx@aux@segm{0}{0}{ref_199963}
\abx@aux@cite{0}{ref_199964}
\abx@aux@segm{0}{0}{ref_199964}
\abx@aux@cite{0}{ref_199965}
\abx@aux@segm{0}{0}{ref_199965}
\abx@aux@cite{0}{ref_199961}
\abx@aux@segm{0}{0}{ref_199961}
\abx@aux@cite{0}{ref_199966}
\abx@aux@segm{0}{0}{ref_199966}
\abx@aux@cite{0}{ref_199967}
\abx@aux@segm{0}{0}{ref_199967}
\abx@aux@cite{0}{ref_199968}
\abx@aux@segm{0}{0}{ref_199968}
\abx@aux@cite{0}{ref_199969}
\abx@aux@segm{0}{0}{ref_199969}
\abx@aux@cite{0}{ref_199970}
\abx@aux@segm{0}{0}{ref_199970}
\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{2}{section.1}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {2}Comparative analysis of CNNs and ViTs in medical applications}{2}{section.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Radiology: X-ray, CT, and MRI}{2}{subsection.2.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}Oncological imaging and tumour detection}{2}{subsection.2.2}\protected@file@percent }
\abx@aux@cite{0}{ref_199975}
\abx@aux@segm{0}{0}{ref_199975}
\abx@aux@cite{0}{ref_199976}
\abx@aux@segm{0}{0}{ref_199976}
\abx@aux@cite{0}{ref_199977}
\abx@aux@segm{0}{0}{ref_199977}
\abx@aux@cite{0}{ref_199978}
\abx@aux@segm{0}{0}{ref_199978}
\abx@aux@cite{0}{ref_199979}
\abx@aux@segm{0}{0}{ref_199979}
\abx@aux@cite{0}{ref_199980}
\abx@aux@segm{0}{0}{ref_199980}
\abx@aux@cite{0}{ref_199981}
\abx@aux@segm{0}{0}{ref_199981}
\abx@aux@cite{0}{ref_199982}
\abx@aux@segm{0}{0}{ref_199982}
\abx@aux@cite{0}{ref_199983}
\abx@aux@segm{0}{0}{ref_199983}
\abx@aux@cite{0}{ref_199984}
\abx@aux@segm{0}{0}{ref_199984}
\abx@aux@cite{0}{ref_199985}
\abx@aux@segm{0}{0}{ref_199985}
\abx@aux@cite{0}{ref_199986}
\abx@aux@segm{0}{0}{ref_199986}
\abx@aux@cite{0}{ref_199987}
\abx@aux@segm{0}{0}{ref_199987}
\abx@aux@cite{0}{ref_199971}
\abx@aux@segm{0}{0}{ref_199971}
\abx@aux@cite{0}{ref_199972}
\abx@aux@segm{0}{0}{ref_199972}
\abx@aux@cite{0}{ref_199973}
\abx@aux@segm{0}{0}{ref_199973}
\abx@aux@cite{0}{ref_199990}
\abx@aux@segm{0}{0}{ref_199990}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}EEG and MEG imaging}{5}{subsection.2.3}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.4}Dermatology and histopathology}{5}{subsection.2.4}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.5}Ophthalmology and retinal imaging}{5}{subsection.2.5}\protected@file@percent }
\abx@aux@cite{0}{ref_199991}
\abx@aux@segm{0}{0}{ref_199991}
\abx@aux@cite{0}{ref_199992}
\abx@aux@segm{0}{0}{ref_199992}
\abx@aux@cite{0}{ref_199969}
\abx@aux@segm{0}{0}{ref_199969}
\abx@aux@cite{0}{ref_199995}
\abx@aux@segm{0}{0}{ref_199995}
\abx@aux@cite{0}{ref_199996}
\abx@aux@segm{0}{0}{ref_199996}
\abx@aux@cite{0}{ref_199997}
\abx@aux@segm{0}{0}{ref_199997}
\abx@aux@cite{0}{ref_199998}
\abx@aux@segm{0}{0}{ref_199998}
\abx@aux@cite{0}{ref_199999}
\abx@aux@segm{0}{0}{ref_199999}
\abx@aux@cite{0}{ref_200002}
\abx@aux@segm{0}{0}{ref_200002}
\abx@aux@cite{0}{ref_200004}
\abx@aux@segm{0}{0}{ref_200004}
\abx@aux@cite{0}{ref_200004}
\abx@aux@segm{0}{0}{ref_200004}
\abx@aux@cite{0}{ref_200009}
\abx@aux@segm{0}{0}{ref_200009}
\abx@aux@cite{0}{ref_199963}
\abx@aux@segm{0}{0}{ref_199963}
\abx@aux@cite{0}{ref_200008}
\abx@aux@segm{0}{0}{ref_200008}
\abx@aux@cite{0}{ref_199992}
\abx@aux@segm{0}{0}{ref_199992}
\abx@aux@cite{0}{ref_199962}
\abx@aux@segm{0}{0}{ref_199962}
\abx@aux@cite{0}{ref_200013}
\abx@aux@segm{0}{0}{ref_200013}
\abx@aux@cite{0}{ref_199989}
\abx@aux@segm{0}{0}{ref_199989}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.6}Musculoskeletal imaging and bone assessment}{7}{subsection.2.6}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {3}Factors for selection between ViT and CNN}{7}{section.3}\protected@file@percent }
\abx@aux@cite{0}{ref_199961}
\abx@aux@segm{0}{0}{ref_199961}
\abx@aux@cite{0}{ref_200014}
\abx@aux@segm{0}{0}{ref_200014}
\abx@aux@cite{0}{ref_200015}
\abx@aux@segm{0}{0}{ref_200015}
\abx@aux@cite{0}{ref_200007}
\abx@aux@segm{0}{0}{ref_200007}
\abx@aux@cite{0}{ref_199963}
\abx@aux@segm{0}{0}{ref_199963}
\abx@aux@nociteall
\@writefile{toc}{\contentsline {section}{\numberline {4}Concluding remarks}{8}{section.4}\protected@file@percent }
\newlabel{LastPage}{{4}{8}{Concluding remarks}{page.8}{}}
\gdef\lastpage@lastpage{8}
\gdef\lastpage@lastpageHy{8}
\abx@aux@read@bbl@mdfivesum{nobblfile}
\abx@aux@read@bblrerun
\gdef \@abspage@last{8}

2452
article_6762.bcf Normal file
View File

File diff suppressed because it is too large Load Diff

3151
article_6762.log Normal file
View File

File diff suppressed because it is too large Load Diff

10
article_6762.out Normal file
View File

@@ -0,0 +1,10 @@
\BOOKMARK [1][-]{section.1}{\376\377\000I\000n\000t\000r\000o\000d\000u\000c\000t\000i\000o\000n}{}% 1
\BOOKMARK [1][-]{section.2}{\376\377\000C\000o\000m\000p\000a\000r\000a\000t\000i\000v\000e\000\040\000a\000n\000a\000l\000y\000s\000i\000s\000\040\000o\000f\000\040\000C\000N\000N\000s\000\040\000a\000n\000d\000\040\000V\000i\000T\000s\000\040\000i\000n\000\040\000m\000e\000d\000i\000c\000a\000l\000\040\000a\000p\000p\000l\000i\000c\000a\000t\000i\000o\000n\000s}{}% 2
\BOOKMARK [2][-]{subsection.2.1}{\376\377\000R\000a\000d\000i\000o\000l\000o\000g\000y\000:\000\040\000X\000-\000r\000a\000y\000,\000\040\000C\000T\000,\000\040\000a\000n\000d\000\040\000M\000R\000I}{section.2}% 3
\BOOKMARK [2][-]{subsection.2.2}{\376\377\000O\000n\000c\000o\000l\000o\000g\000i\000c\000a\000l\000\040\000i\000m\000a\000g\000i\000n\000g\000\040\000a\000n\000d\000\040\000t\000u\000m\000o\000u\000r\000\040\000d\000e\000t\000e\000c\000t\000i\000o\000n}{section.2}% 4
\BOOKMARK [2][-]{subsection.2.3}{\376\377\000E\000E\000G\000\040\000a\000n\000d\000\040\000M\000E\000G\000\040\000i\000m\000a\000g\000i\000n\000g}{section.2}% 5
\BOOKMARK [2][-]{subsection.2.4}{\376\377\000D\000e\000r\000m\000a\000t\000o\000l\000o\000g\000y\000\040\000a\000n\000d\000\040\000h\000i\000s\000t\000o\000p\000a\000t\000h\000o\000l\000o\000g\000y}{section.2}% 6
\BOOKMARK [2][-]{subsection.2.5}{\376\377\000O\000p\000h\000t\000h\000a\000l\000m\000o\000l\000o\000g\000y\000\040\000a\000n\000d\000\040\000r\000e\000t\000i\000n\000a\000l\000\040\000i\000m\000a\000g\000i\000n\000g}{section.2}% 7
\BOOKMARK [2][-]{subsection.2.6}{\376\377\000M\000u\000s\000c\000u\000l\000o\000s\000k\000e\000l\000e\000t\000a\000l\000\040\000i\000m\000a\000g\000i\000n\000g\000\040\000a\000n\000d\000\040\000b\000o\000n\000e\000\040\000a\000s\000s\000e\000s\000s\000m\000e\000n\000t}{section.2}% 8
\BOOKMARK [1][-]{section.3}{\376\377\000F\000a\000c\000t\000o\000r\000s\000\040\000f\000o\000r\000\040\000s\000e\000l\000e\000c\000t\000i\000o\000n\000\040\000b\000e\000t\000w\000e\000e\000n\000\040\000V\000i\000T\000\040\000a\000n\000d\000\040\000C\000N\000N}{}% 9
\BOOKMARK [1][-]{section.4}{\376\377\000C\000o\000n\000c\000l\000u\000d\000i\000n\000g\000\040\000r\000e\000m\000a\000r\000k\000s}{}% 10

BIN
article_6762.pdf Normal file
View File

Binary file not shown.

87
article_6762.run.xml Normal file
View File

@@ -0,0 +1,87 @@
<?xml version="1.0" standalone="yes"?>
<!-- logreq request file -->
<!-- logreq version 1.0 / dtd version 1.0 -->
<!-- Do not edit this file! -->
<!DOCTYPE requests [
<!ELEMENT requests (internal | external)*>
<!ELEMENT internal (generic, (provides | requires)*)>
<!ELEMENT external (generic, cmdline?, input?, output?, (provides | requires)*)>
<!ELEMENT cmdline (binary, (option | infile | outfile)*)>
<!ELEMENT input (file)+>
<!ELEMENT output (file)+>
<!ELEMENT provides (file)+>
<!ELEMENT requires (file)+>
<!ELEMENT generic (#PCDATA)>
<!ELEMENT binary (#PCDATA)>
<!ELEMENT option (#PCDATA)>
<!ELEMENT infile (#PCDATA)>
<!ELEMENT outfile (#PCDATA)>
<!ELEMENT file (#PCDATA)>
<!ATTLIST requests
version CDATA #REQUIRED
>
<!ATTLIST internal
package CDATA #REQUIRED
priority (9) #REQUIRED
active (0 | 1) #REQUIRED
>
<!ATTLIST external
package CDATA #REQUIRED
priority (1 | 2 | 3 | 4 | 5 | 6 | 7 | 8) #REQUIRED
active (0 | 1) #REQUIRED
>
<!ATTLIST provides
type (static | dynamic | editable) #REQUIRED
>
<!ATTLIST requires
type (static | dynamic | editable) #REQUIRED
>
<!ATTLIST file
type CDATA #IMPLIED
>
]>
<requests version="1.0">
<internal package="biblatex" priority="9" active="1">
<generic>latex</generic>
<provides type="dynamic">
<file>article_6762.bcf</file>
</provides>
<requires type="dynamic">
<file>article_6762.bbl</file>
</requires>
<requires type="static">
<file>blx-dm.def</file>
<file>blx-unicode.def</file>
<file>blx-compat.def</file>
<file>biblatex.def</file>
<file>blx-natbib.def</file>
<file>standard.bbx</file>
<file>numeric.bbx</file>
<file>numeric-comp.cbx</file>
<file>biblatex.cfg</file>
<file>english.lbx</file>
</requires>
</internal>
<external package="biblatex" priority="5" active="1">
<generic>biber</generic>
<cmdline>
<binary>biber</binary>
<infile>article_6762</infile>
</cmdline>
<input>
<file>article_6762.bcf</file>
</input>
<output>
<file>article_6762.bbl</file>
</output>
<provides type="dynamic">
<file>article_6762.bbl</file>
</provides>
<requires type="dynamic">
<file>article_6762.bcf</file>
</requires>
<requires type="editable">
<file>references_6762.bib</file>
</requires>
</external>
</requests>

BIN
article_6762.synctex.gz Normal file
View File

Binary file not shown.

126
article_6762.tex Normal file
View File

@@ -0,0 +1,126 @@
\documentclass[
journal=tmr,
journalname={{Medical Data Mining}},
layout=largetwo,
year=2026,%年
volume=9,%卷
no=2,%期
page=10,%号
]{tmr-tex}
\doi{10.53388/MDM202609010}
\journalweb{https://www.tmrjournals.com/mdm}
\usepackage{amsmath}
\usepackage[nopatch]{microtype}
\usepackage{booktabs}
\usepackage[backend=biber]{biblatex}
\usepackage{xcolor}
\usepackage{tabularray}
\addbibresource{references_6762.bib}
\definecolor{evenRowColor}{RGB}{250,231,232}
\title{Brief application notes for vision transformer (ViT) and convolutional neural network (CNN) in medical imaging}
\keywords{keyword entry 1, keyword entry 2, keyword entry 3} %% First letter not capped
\authorcontributions{-}
\competinginterests{The authors declare no conflicts of interest.}
\Acknowledgments{-}
\Peerreviewinformation{Medical Data Mining thanks all anonymous reviewers for their contribution to the peer review of this paper}
\Citation{Wong WK, Melinda M, et al. Brief application notes for vision transformer (ViT) and convolutional neural network (CNN) in medical imaging. \textit{Med Data Min}. 2026;9(2):10. doi:10.53388/MDM202609010}
\received{20 September 2025}
\revised{17 November 2025}
\accepted{}
\Availableonline{11 February 2026}
\Executiveeditor{Ming-Hao Wang}
\tmrabstract{In contemporary computer vision, convolutional neural networks (CNNs) and vision transformers (ViTs) represent the two main approaches in image recognition. While both are used in various application in medical imaging, they work in fundamentally different ways. This report attempts to provide brief application notes on the ViTs and CNN in particular relating to situations to select one over the other. Generally, CNNs rely on convolutional kernels, local connections, and weight sharing. This gives them high efficiency and surprisingly strong performance in detecting features in local regions. ViTs, on the other hand, break images down into smaller sections (referred to as tokens) and use self-attention mechanisms to understand the relationships between all these sections, globally. In general, ViT achieves optimality when they are able to learn from incredibly large amounts of pre-training data. This report will examine briefly the structure, the underlying math, and the relative performance of CNNs and ViTs differ based on the lastest finding frmresearch work. Most importantly, the report serves as brief application note for implementation between the stated algorithms.}
\keywords{convolutional neural network; vision transformer; comparative study; medical imaging}
\begin{document}
\twocolumn
\section{Introduction}
Image processing has become an integral component in medical technology. The introduction of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) has ushered in an era where features are no longer human engineered, but self-generated within the framework of the model. While both CNNs and ViTs tackle similar problems specifically in medical imaging and object detection, they approach from dif ferent approach. CNNs rely on convolutional kernels, focusing on nearby connections and shared weights to create layered feature maps. ViTs, however, see an image as a string of patches, using self-attention to understand relationships across the whole picture. So, even though they both do well in the same areas, they need different amounts of data and computing power, and they have different built-in assumptions. As depicted in \textcolor[HTML]{0082AA}{Figure 1}, CNNs use a series of convolution and pooling steps, building up local features layer by layer, by implementing weight sharing. ViTs, by contrast, break the image down into patches, turn them into embeddings (adding positional info), and then use self-attention. This lets them “see” global connections right away. This difference in structure highlights why CNNs are great at picking up local textures, while ViTs are better at immediately understanding long-distance relationships.\par
In a vision transformer (ViT), an image is split into patches and each patch becomes a \textit{token} (a vector). An input image of size \textit{H×W ×C} (height, width, channels) is divided into fixed-size, non-overlapping patches of spatial size \textit{P × P}. Each patch is then flattened into a vector of length \textit{P}$^{{\textit{2}}}$\textit{C} and linearly projected into an embedding of dimension \textit{d}. This produces a sequence of tokens analogous to words in natural language processing.\par
In the original report introducing ViT \parencite{ref_199959}, a 224 × 224 RGB image is split into patches of size 16×16. This yields 14 × 14 = 196 patches. Each patch has 16 × 16 × 3 = 768 raw values, which are projected to an embedding of dimension d = 768. The resulting 196 patch embeddings, along with an added classification token, form the input sequence to the Transformer encoder.\par
It is important to emphasize that patching works very differently as compared to convolution. Convolutions use overlapping sliding kernels with weight sharing, producing hierarchical local features. Patching, in contrast, is a rigid partitioning of theimage into non-overlapping tiles, each treated as a token. This design removes built-in inductive biases such as translation invariance, forcing the model to learn them from data.\par
As illustrated in \textcolor[HTML]{0082AA}{Figure 2}, the self-attention mechanism enables each image patch (token) to selectively integrate information from all other patches in the image. For an input token <wmath data-id="wmath-wej9a12cy" data-latex="\$\$\{\{\textbackslashmathbf\{x\}\}\_i\}\$\$" data-wrap="inline">xi</wmath>, three linear projections are computed: the query <wmath data-id="wmath-go97l0ywk" data-latex="\$\$\{\{\textbackslashmathbf\{Q\}\}\_i\}\textbackslashmathrm\{=\}\{\{\textbackslashmathbf\{x\}\}\_i\}\{\{\textbackslashmathbf\{W\}\}\_Q\}\$\$" data-wrap="inline">Qi=xiWQ</wmath>, the key <wmath data-id="wmath-7l8zdsl4q" data-latex="\$\$\{\{\textbackslashmathbf\{K\}\}\_i\}\textbackslashmathrm\{=\}\{\{\textbackslashmathbf\{x\}\}\_i\}\{\{\textbackslashmathbf\{W\}\}\_K\}\$\$" data-wrap="inline">Ki=xiWK</wmath>, and the value <wmath data-id="wmath-42ucwyr1p" data-latex="\$\$\{\{\textbackslashmathbf\{V\}\}\_i\}\textbackslashmathrm\{=\}\{\{\textbackslashmathbf\{x\}\}\_i\}\{\{\textbackslashmathbf\{W\}\}\_V\}\$\$" data-wrap="inline">Vi=xiWV</wmath>. The relevance between token\textit{ i }and any other token \textit{j} is quantified using a scaled dot-product similarity as expressed in \textcolor[HTML]{0082AA}{Equation 1}.\par
<wmath data-id="wmath-w1qb4f5fc" data-latex="\$\$\{s\_\{ij\}\}\textbackslashmathrm\{=\}\textbackslashfrac\{\{\{\textbackslashmathbf\{Q\}\}\_i\}\textbackslashmathrm\{⋅\}\{\{\textbackslashmathbf\{K\}\}\_j\}\}\{\textbackslashsqrt\{d\}\}\textbackslashmathrm\{,\}\$\$" data-wrap="inline">sij=Qi⋅Kjd,</wmath> (1)\par
where \textit{d} denotes the dimensionality of the key vectors. These similarity scores are normalised with a softmax function to produce attention weights as expressed in \textcolor[HTML]{0082AA}{Equation 2}.\par
<wmath data-id="wmath-9o2itynkx" data-latex="\$\$\{α\_\{ij\}\}\textbackslashmathrm\{=\}\textbackslashfrac\{exp\textbackslashleft (\{\{s\_\{ij\}\}\}\textbackslashright )\}\{\textbackslashsum\_\{k\}\textasciicircum\{​\}\{exp\}\textbackslashleft (\{\{s\_\{ik\}\}\}\textbackslashright )\}\textbackslashmathrm\{,\}\$\$" data-wrap="inline">αij=exp(sij)∑k​exp(sik),</wmath> (2)\par
which represent the contribution of token\textit{ j} when updating token\textit{ i}. The output representation of token \textit{i} is then obtained as a weighted aggregation of all value vectors as expressed in \textcolor[HTML]{0082AA}{Equation 3}.\par
<wmath data-id="wmath-h3jzjks1m" data-latex="\$\$\{\{\textbackslashmathbf\{z\}\}\_i\}\textbackslashmathrm\{=\}\textbackslashsum\_\{j\}\textasciicircum\{​\}\{\{α\_\{ij\}\}\}\{\{\textbackslashmathbf\{V\}\}\_j\}\textbackslashmathrm\{.\}\$\$" data-wrap="inline">zi=∑j​αijVj.</wmath> (3)<wmath data-id="wmath-wh4qgoviz" data-latex="" data-wrap="block"></wmath>\par
This formulation allows global information exchange across the entire image, enabling Vision Transformers to model long-range spatial dependencies more effectively than convolution-based architectures.\par
Deep learning has become central to medical image analysis, with CNNs and ViTs emerging as the two dominant architectural paradigms. While both have demonstrated strong performance across a wide range of clinical applications, their relative strengths, limitations, and suitability for real-world medical deployment remain fragmented across the literature. This article presents a structured comparative analysis of CNNs and ViTs in medical imaging,focusing on key domains including radiology, ophthalmology, musculoskeletal imaging, and oncology. We examine how architectural inductive biases, data availability,pretraining strategies, and computational constraints influence model performance in tasks such as classification and segmentation.\par
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./image/file_697dc012dbc4b.png}
\end{figure*}\par
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./image/file_697dbf7d614e0.png}
\end{figure*}\par
\section{Comparative analysis of CNNs and ViTs in medical applications}
With the architectural foundations of CNNs and ViTs established, this section provides an analysis of their use across major medical imaging application domains. The section will briefly cover implementation in Radiology, Oncology, Dermatology/Histopathology, Ophthalmology, andMusculoskeletal Imaging. Most of these application involves either image segmentation or image classification. While the previous section explains with regard to image classification, slight modification enables both CNN and ViT to be used for pixel classification thereby creating a segmented region of interest.\par
\subsection{Radiology: X-ray, CT, and MRI}
Radiological imaging remains as one of the most extensively studied domains for comparing CNNs and ViTs. Across chest X-ray, CT, and MRI modalities, CNN-based approaches have demonstrated strong and reliable performance, particularly when annotated datasets are limited and diagnostically relevant cues are spatially localized. In COVID-19 detection from chest X-rays, CNNs trained either from scratch or via transfer learning achieved competitive accuracy, faster convergence, and greater training stability under label scarcity \textcolor[HTML]{0082AA}{[2, 3]}. Similar observations have been reported in brain MRI classification tasks, where CNNs maintained stable optimisation behaviour and efficient learning on moderately sized datasets \parencite{ref_199962}.\par
However, radiological tasks often involve spatially distributed or diffuse patterns, such as lung opacities or infiltrative tumour growth, where purely local feature extraction may be insufficient. Vision transformers address this limitation through self-attention mechanisms that model long-range spatial dependencies. Multiple studies report that ViTs outperform CNNs in radiological tasks when adequate pretraining and fine-tuning are employed, particularly for complex MRI and CT analyses \parencite{ref_199963,ref_199964,ref_199965}. Murphy et al. further showed that pretrained ViTs exhibit improved transferability across radiology datasets, although their advantage diminishes in small-data regimes \parencite{ref_199961}.\par
Hybrid CNN-ViT architectures have emerged as an effective compromise in radiology, particularly for segmentation. Ghribi et al. proposed a 3D U-Net-ViT hybrid for brain tumour segmentation \parencite{ref_199966}, achieving a global accuracy of 99.56\% and an average Dice similarity coefficient of 77.43\%. These results highlight a recurring pattern: convolutional encoders provide precise local boundary and texture information, while transformer layers enhance global coherence across anatomically complex regions.\par
\subsection{Oncological imaging and tumour detection}
Oncological imaging deals with the detection, characterization and monitoring of tumours using medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) and histopathological whole-slide images. Imaging is an essential component throughout the cancer continuum of care, facilitating early diagnosis, treatment development, response evaluation and disease surveillance. Tumour morphology in medical images is highly heterogeneous and different in size, shape, texture and spatial arrangement between patients and types of cancer. Accordingly, automated image analysis is a growing area of study to ensure diagnostic rigor, lessen clinician load, and increase sensitivity to minor or precursory cancer. Oncological imaging is one of the most commonly used application domains to compare CNNs and ViTs, it can be implemented in classification or segmentation tasks. CNNs have been largely used for tumour detection, attributed to the high capacity of CNN to represent lesion texture, edge area and regional intensity variation. However, results from different studies are not consistent. Some works show superiority of CNN in the fine-grained lesion analysis, while some work presented better performance of ViTs or hybrid architectures especially when tumours had heterogeneous or spatially distributed properties \parencite{ref_199967,ref_199968,ref_199969}. \textcolor[HTML]{0082AA}{Figure 3} shows a representative MRI-based classification task, separating between tumour and non-tumour cases, as reported by Srinivasan et al. \parencite{ref_199970}. They present a hybrid CNN-ViT framework which merges convolutional feature extraction (CNN) and transformer-based attention, supporting the larger point that in oncological situations the ability to combine local and global representations is helpful. In the literature, the advantages of ViT in oncology are most readily observed when relying on pretraining and when the accuracy of segmentation relies on contextual relations between the tumour subregions rather than on purely local appearance.\par
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./image/file_697dc0984afb1.jpg}
\end{figure*}\par
\subsection{EEG and MEG imaging}
Electroencephalography (EEG) and magnetoencephalography (MEG) provide direct measurements of neural activity with millisecond temporal resolution, but their representation as multichannel time-series signals presents unique challenges for deep learning. Consequently, research in this domain has evolved from conventional CNN pipelines toward increasingly sophisticated hybrid and transformer-based architectures. Early and influential work demonstrated that CNNs can learn meaningful electrophysiological representations directly from raw or minimally processed EEG. For example, the compact EEGNet architecture established that carefully designed temporal and spatial convolutions can generalize across paradigms and subjects while remaining computationally efficient, explaining why CNNs remain dominant in low-data clinical settings \textcolor[HTML]{0082AA}{[13, 14]}. Subsequent work reinforced this finding, showing that CNNs can extract physiologically meaningful oscillatory and spatiotemporal features without handcrafted signal engineering \textcolor[HTML]{0082AA}{[15, 16]}.\par
More recent studies increasingly report limitations of purely convolutional inductive bias, particularly for tasks where discriminative information is distributed across long temporal contexts or complex inter-channel relationships. Liu et al. proposed a hybrid CNN-Transformer architecture in which convolutional layers first learn stable local temporal-spectral features before transformer blocks model long-range dependencies across the signal, demonstrating improved classification accuracy compared with standalone CNNs on multiple EEG benchmarks \parencite{ref_199975}. Zhao et al. further extended this hybrid paradigm through the CNNViT-MILF framework, integrating CNNs, vision transformers, and multi-instance learning to explicitly address subject variability and weak labeling, two persistent challenges in real-world EEG analysis \parencite{ref_199976}. Their results suggest that attention mechanisms offer clear advantages in heterogeneous datasets where global relational modeling is essential.\par
Several recent ViT-centric studies provide direct evidence that attention-based architectures can outperform convolutional baselines when EEG is encoded as structured time-frequency or connectivity representations. Chakladar demonstrated that when EEG-derived brain connectivity matrices are treated as visual patterns, a vision transformer can more effectively capture global relational structure than CNNs, resulting in improved discrimination of neurological states \parencite{ref_199977}. Afzal et al. applied vision transformers to large-scale clinical seizure datasets and showed that transformerbased models generalize more robustly across patients than conventional convolutional approaches, emphasizing the importance of long-range temporal modeling for realistic clinical deployment \parencite{ref_199978}. Related work further supports this conclusion by demonstrating the benefits of multi-stream and multimodal ViT-based architectures for complex EEG conditions such as epilepsy and cognitive disorders \parencite{ref_199979,ref_199980,ref_199981,ref_199982}.\par
MEG research exhibits a parallel evolution but with additional emphasis on spatial modeling and source inference. Wang et al. demonstrated that deep learning models, including CNNs and vision transformers, can approximate the traditionally ill-posed MEG inverse problem, enabling fast and accurate source localization directly from sensor data \parencite{ref_199983}. This result is significant because it suggests that data-driven models can replace computationally expensive biophysical solvers in time-critical MEG applications. Earlier work by Seeliger et al. already showed that CNNs can successfully decode and localize MEG activity patterns, reinforcing the suitability of convolutional architectures for structured spatiotemporal neural data \parencite{ref_199984}.\par
More recent transformer-based approaches in MEG focus less on classification and more on foundational signal modeling tasks. Tibermacine et al. proposed attention-based models for MEG denoising, showing that transformer architectures can enhance event-related fields under low signal-to-noise conditions, thereby reducing the need for extensive trial averaging \parencite{ref_199985}. Khadka et al. further explored transformer-style architectures for long-sequence modeling of MEG time-series, demonstrating that attention mechanisms are well suited to capturing long-term temporal structure in continuous neural recordings \parencite{ref_199986}. Afzal et al. additionally argued for the role of large-scale transformer models as emerging foundation architectures for electrophysiological signals, positioning MEG and EEG within the broader trend toward pretraining and transferable neural representations \parencite{ref_199987}.\par
Taken together, the literature consistently indicates that CNNs remain highly effective when data are limited and discriminative features are local, which explains their continued dominance in clinical EEG and MEG pipelines \parencite{ref_199971,ref_199972,ref_199973}. However, an increasing number of studies demonstrate that vision transformers and hybrid CNN-ViT architectures offer tangible advantages when tasks require modeling long-range temporal dependencies, distributed spatial interactions, cross-subject variability, or multimodal fusion \textcolor[HTML]{0082AA}{[17-21, 25]}. This trajectory mirrors broader trends in medical AI, where convolutional architectures provide strong baselines, but attention-based models increasingly define the frontier for large-scale, generalizable, and foundation-style learning.\par
\subsection{Dermatology and histopathology}
Dermatology and histopathology focus on the visual assessment of skin surfaces and microscopic tissue structures to diagnose inflammatory, infectious, and neoplastic diseases. Imaging modalities such as dermoscopic images and digitized histopathology slides are central to clinical decision-making, as diagnosis often relies on subtle textural patterns, boundary irregularities, and cellular morphology. These images are typically high-resolution and rich in fine-grained visual detail, making automated analysis particularly valuable for screening, workload reduction, and diagnostic standardization in both clinical and telemedicine settings.\par
In dermatology and histopathology, CNNs continue to provide strong baseline performance due to their locality bias, which aligns well with texture and edge dominated visual patterns such as skin lesions, cellular nuclei, and glandular structures. In dermoscopic image analysis, CNNs frequently achieve stable and competitive results even with limited annotations, making them suitable for screening and teledermatology applications \textcolor[HTML]{0082AA}{[30, 31]}.\par
Vision transformers have also demonstrated the potential to match or exceed CNN performance in these domains, but predominantly under conditions of substantial pretraining and careful fine-tuning. Takahashi et al. showed that ViTs can achieve comparable classification accuracy in histopathological tasks \parencite{ref_199990}, although at the cost of increased training complexity and computational demand. In digital pathology, where whole slide images contain both localized cellular features and broader tissue architecture, transformer-based models benefit from their ability to model long-range dependencies. Nevertheless, computational cost and sensitivity to patch selection strategies remain key barriers, reinforcing the appeal of hybrid CNN-ViT designs.\par
\par
\subsection{Ophthalmology and retinal imaging}
Ophthalmology focuses on the diagnosis and management of diseases affecting the eye, many of which manifest as structural and vascular changes in the retina. Retinal imaging plays a central role in this field, as the retina provides a non-invasive window into ocular and systemic health. Imaging modalities such as fundus photography and optical coherence tomography (OCT) are routinely used to screen, diagnose, and monitor conditions including diabetic retinopathy, age-related macular degeneration, glaucoma, and hypertensive retinopathy. These diseases often progress silently in early stages, making automated retinal analysis particularly valuable for large-scale screening, early intervention, and resource-limited clinical settings, Ophthalmology is one of the earliest medical domains where deep learning systems have achieved regulatory-approved deployment. CNN-based models have been widely adopted for fundus photography and optical coherence tomography (OCT), excelling at capturing vascular structures, optic disc morphology, and localized retinal lesions. Under limited labelled data, CNNs remain highly effective and computationally efficient.\par
Generally both ViT and CNN were often evaluated particularly for distinguishing between the various stages of Retinopathy or Glaukoma. The myriad of datasets (such as APTOS, Messidor ) encourages the trend of utlising various deep learning approaches, \parencite{ref_199991} evaluated enhancing optimality in CNN transfer learning by using metaheuristic approaches and reported state of the art improvements. Vision transformers have shown advantages in retinal disease diagnosis when pathological patterns are spatially distributed across the field of view. Hwang et al. demonstrated that ViT-based models outperform CNNs after pretraining \parencite{ref_199992}, particularly in multi-disease classification settings. Systematic reviews further indicate that attention mechanisms improve integration of spatially dispersed retinal cues and enhance cross-dataset robustness \parencite{ref_199969}. As with other domains, hybrid CNN-ViT architectures often offer the most practical balance between performance and data efficiency. The challenge lies in the fact that most of the research exclusively used 1 dataset for evaluation. Cross dataset evaluation could further show generalisation capability of the reported results.\par
\par
\subsection{Musculoskeletal imaging and bone assessment}
Musculoskeletal imaging focuses on the assessment of bones, joints, and connective tissues for the diagnosis of degenerative, traumatic, and metabolic disorders. Conventional radiography remains the most widely used modality in this domain due to its low cost, accessibility, and suitability for population-level screening, particularly in the context of osteoporosis, osteoarthritis, fractures, and spinal deformities. Clinical interpretation commonly relies on subtle radiographic cues such as variations in bone density, cortical thickness, joint space narrowing, and overall skeletal morphology, which are inherently subjective and prone to inter-observer variability. Consequently, automated image analysis using deep learning has attracted substantial interest as a means of supporting early diagnosis, improving reproducibility, and enabling large-scale screening in ageing populations.\par
A substantial body of literature demonstrates the effectiveness of convolutional neural networks (CNNs) for musculoskeletal image analysis. Early studies showed that deep CNN models can reliably detect abnormalities in musculoskeletal radiographs and improve diagnostic efficiency in routine clinical workflows \textcolor[HTML]{0082AA}{[35, 36]}. Comprehensive reviews further confirm that CNN-based systems have achieved strong performance across a wide range of musculoskeletal tasks, including fracture detection, joint abnormality classification, and tissue segmentation in MRI, largely due to their ability to exploit local texture and structural patterns \parencite{ref_199995,ref_199996,ref_199997}. More recent contributions highlight that CNN-driven pipelines are increasingly being integrated into clinical practice as decision-support tools, particularly in high-volume radiology environments where workload and variability remain critical challenges \parencite{ref_199998}. These findings collectively support the continued relevance of convolutional architectures in musculoskeletal imaging, especially in settings where training data are moderate in size and diagnostically relevant features are spatially localized.\par
However, emerging evidence also indicates that purely convolutional inductive bias may be insufficient for tasks where diagnosis depends on more global structural relationships. In population-level screening applications such as osteoporosis assessment, the discriminative cues are often distributed across the entire skeletal structure rather than confined to isolated local regions. Cross-analysis studies suggest that when sufficient data or pretrained representations are available, transformer-based architectures can provide state-of-the-art performance by more effectively capturing long-range dependencies and holistic anatomical context. Bi et al. proposed a hybrid CNN-transformer architecture for musculoskeletal image analysis and demonstrated that incorporating attention mechanisms improves robustness and generalization compared with CNN-only baselines \parencite{ref_199999}. Similarly, recent studies applying global modeling strategies to musculoskeletal radiographs emphasize that structural relationships across distant anatomical regions contribute meaningfully to diagnostic accuracy, supporting the suitability of transformer-style representations for this domain \textcolor[HTML]{0082AA}{[42, 43]}.\par
Within this context, Sarmadi et al. provide a direct comparative evaluation between CNN-based and vision transformer (ViT)-based models for bone assessment tasks \parencite{ref_200002}. Their results indicate that ViT-based architectures can significantly outperform convolutional baselines in specific musculoskeletal applications, particularly when diagnostic decisions depend on global anatomical structure rather than local texture alone. This finding aligns with broader trends observed across medical imaging, where CNNs remain highly effective under limited data regimes, but ViTs offer increasing advantages when tasks require holistic structural reasoning and when sufficient training data or transfer learning strategies are available.\par
A summary of the implementation discussion is shown in \textcolor[HTML]{0082AA}{Table 1}. It is observed that for most application, there are advocates for both ViT and CNN with justification.\par
\par
\section{Factors for selection between ViT and CNN}
Previous section have discussed some of the works pertaining to the use of ViT and CNN. This section will attempt to evaluate/formalize the factors that should be considered for implementation. As discussed in previous section, CNNs continue to be a popular option when dealing with smaller datasets, costly annotations. Their inherent strengths lies in local connectivity and the ability to recognize patterns regardless of location. For instance, some studies comparing COVID-19 X-ray detection and brain MRI classification; authors noted that CNNs not only trained more quickly, but were also more reliable when dealing with limited labeled data \textcolor[HTML]{0082AA}{[2, 4]}. Further supporting this, research in dermatology and chest radiograph classification indicates CNNs offer consistent performance even with limited annotations. ViTs, on the other hand, often needed pre-training to achieve similar results \textcolor[HTML]{0082AA}{[30, 45]}. CNN efficiency is also crucial in safety-critical systems, where fast response times are essential \parencite{ref_200004}. Image classification involving smaller datasets, CNNs demonstrated better convergence and reduced the risk of overfitting compared to ViTs \textcolor[HTML]{0082AA}{[47, 48]}. Their reduced computational demands make them well-suited for edge devices and real-time applications, as seen in areas like industrial crack segmentation and geological fault detection \textcolor[HTML]{0082AA}{[49, 50]}. Further supporting this, it is noteworthy that areas such as dermatology and chest X-ray analysis, CNNs tend to deliver consistent results with minimal annotation; ViTs, on the other hand, may need pre-training to compete \textcolor[HTML]{0082AA}{[30, 45]}. Efficiency is also key in safety-critical setups, and studies underscore CNNs’ low-latency advantages \parencite{ref_200004}.\par
Shallower CNN layers tend to pick up the localized features (edges, textures), which is optimal for domains such as dermatology and histopathology. ViTs, in comparison, use global self-attention across all patches, regardless of length. As such CNNs can still dominate in situations where fine-grained, localized structures are what really matter. Conversely, ViTs utilize self-attention globally across all image patches from the outset, lacking that intrinsic sense of locality. It’s this absence that likely explains CNNs’ continued superiority in fields where subtle, local details hold the most crucial information.\par
Vision Transformers (ViTs) perform optimally when a task demands understanding across an entire image or even through time. Human action recognition, for instance. CNNs, being more focused on local motion, seem to miss global context \parencite{ref_200009}. This advantage in ViT is important especially in identifying tumors, ViTs leveraged on these global perspective to outperform CNNs on similar task as demonstrated by the various research discussed, but only with training of more data training data \parencite{ref_199963}. Reinforcing this, authors in other domain cited that ViT they were much better at understanding satellite images, thanks to their ability to connect broader regions of the image. CNNs struggled with those wide-area relationships as \textcolor[HTML]{0082AA}{[52, 53]}. Fault detection in geology followed a similar pattern: ViTs were more accurate by making sense of the overall picture in high-resolution images \parencite{ref_200008}. This trend holds up in medical imaging. Research on eye images showed that ViTs captured those widespread features associated with glaucoma better than CNNs \parencite{ref_199992}. And a review of different architectures suggested that ViTs are great at combining multimodal information such as images, clinical data,because they’re flexible with that attention mechanism in the architecture \textcolor[HTML]{0082AA}{[11, 54]}.\par
It’s generally understood that Vision Transformers (ViTs) excells when there is large, annotated datasets or the opportunity for transfer learning. In these situations, ViTs have often proven themselves superior to Convolutional Neural Networks (CNNs). This is largely because their comparatively minimal inductive bias becomes relevant when working at larger scale, allowing for representations that are more adaptable. Some studies show ViTs achieving a slight improvement in accuracy over CNN baselines on benchmarks such as ImageNet and CIFAR \textcolor[HTML]{0082AA}{[4, 55]}. Report in(Tokens-to-Token ViT) reports that its T2T-ViT model achieves 1.4\%-2.7\% higher top-1 accuracy than ResNet-50/101/152 under comparable model size and computational cost on the ImageNet benchmark \parencite{ref_199962}, demonstrating that transformer-based architectures can outperform established CNN baselines when training protocols are carefully controlled. Similarly, report in \parencite{ref_200013} (Comparative Analysis between CNN and ViT using Brain MRI Dataset) demonstrates that a Vision Transformer achieved 88.5\% classification accuracy compared to 85.5\% for a CNN model on a brain tumor MRI dataset, corresponding to a 3\% absolute improvement in a real medical imaging task further Supporting this statement authors have shown through pigmented skin lesion classification. Here, pre-trained ViTs, after being fine-tuned on clinical images, actually surpassed the performance of CNNs \parencite{ref_199989}.\par
ViTs, consistently outpace CNNs on large-scale benchmarks when pretraining is part of the consideration. Studies suggest that pretrained ViTs create flexible representations that generalize well across different tasks. CNNs trained from the ground up, on the other hand, often need a lot more fine-tuning \textcolor[HTML]{0082AA}{[3, 6]}. Consider radiology, for instance, where some pretrained ViTs demonstrated better sample efficiency and were more robust to hidden stratification (unlabeled subgroups within a class) than their CNN counterparts \parencite{ref_199961}. Pretrained ViTs have also been successfully applied to dental radiology, delivering competitive baseline performance across various downstream classification tasks \parencite{ref_200014}.\par
While CNNs will continue to be relevant despite the advantages posed by ViT, particularly when training directly on smaller or medium-sized datasets, ViTs that have been trained on truly massive datasets and then fine-tuned tend to outperform CNNs. This is especially true in areas like medical imaging, security, and remote sensing. From the exiting reports, scale and pretraining enables the full potential of ViTs. \textcolor[HTML]{0082AA}{Table 2} summarises the considerations for consiering between ViT and CNN.\par
From a mathematical perspective, error appears to follow a power-law with dataset size: <wmath data-id="wmath-96pvynrms" data-latex="\$\$\textbackslashtext\{ \}\textbackslashmathcal\{\textbackslashmathrm\{ℰ\}\}\textbackslashleft (\{N\}\textbackslashright )​\textbackslashmathrm\{≈\}​a\{N\textasciicircum\{\textbackslashmathrm\{−\}α\}\}​\textbackslashmathrm\{+\}​b\$\$" data-wrap="inline"> ℰ(N)​≈​aN−α​+​b</wmath>. If pretrain , ViTs tend to show a larger exponent <wmath data-id="wmath-g2yd7bq3j" data-latex="\$\$\{α\_\{\textbackslashmathrm\{ViT\}\}\}\textbackslashmathrm\{>\}\{α\_\{\textbackslashmathrm\{CNN\}\}\}\$\$" data-wrap="inline">αViT>αCNN</wmath> and a lower asymptote b, which helps explain steeper performance improvements and higher limits when really scale up \parencite{ref_200015}.\par
In response to the weaknesses and strengths of both, it is important to highlight that hybrid of ViT and CNN were often considered. For instance, hybrid models that explain themselves well are being used for crack segmentation, offering improved interpretability and robustness compared to CNN-only setups \parencite{ref_200007}. In situations such as federated learning, where data is spread out, hybrids can strike a good balance between efficient communication and powerful representation, using the CNN to compress local features and the ViT to flexibly combine information on a global scale \parencite{ref_199963}.\par
Intuitively,in the hybrid architecture, CNNs can stabilize training and lessen the need for huge datasets, while transformer blocks add adaptability and scalability \textcolor[HTML]{0082AA}{[59, 60]}. All in all, hybrid architectures are generally seen as a “best of both worlds” solution. That makes them particularly attractive for real-world scenarios where you need both high accuracy and good efficiency.\par
\par
\section{Concluding remarks}
This study provides brief comparative study between CNNs and ViTs, though often pitted against each other, each should be selected based on the application. CNNs, with their ingrained understanding of local patterns and lean designs, continue to be workhorses in situations where resources are tight, datasets are modest, and local details reign supreme. On the other hand, ViTs reaches full potential when afforded the opportunity to absorb global context and leverage the power of extensive pre-training.\par
\par
\par
\nocite{*}
\printbibliography[title={References}]
\end{document}

6
config Normal file
View File

@@ -0,0 +1,6 @@
[core]
bare = true
repositoryformatversion = 0
filemode = false
symlinks = false
ignorecase = true

1
description Normal file
View File

@@ -0,0 +1 @@
Unnamed repository; edit this file 'description' to name the repository.

124
document.aux Normal file
View File

@@ -0,0 +1,124 @@
\relax
\providecommand\zref@newlabel[2]{}
\providecommand\hyper@newdestlabel[2]{}
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\abx@aux@refcontext{none/global//global/global/global}
\abx@aux@cite{0}{ref_194007}
\abx@aux@segm{0}{0}{ref_194007}
\abx@aux@cite{0}{ref_194008}
\abx@aux@segm{0}{0}{ref_194008}
\abx@aux@cite{0}{ref_194009}
\abx@aux@segm{0}{0}{ref_194009}
\abx@aux@cite{0}{ref_194010}
\abx@aux@segm{0}{0}{ref_194010}
\abx@aux@cite{0}{Bayer_etal_2013}
\abx@aux@segm{0}{0}{Bayer_etal_2013}
\abx@aux@cite{0}{Adade_etal_2007}
\abx@aux@segm{0}{0}{Adade_etal_2007}
\abx@aux@cite{0}{ref_186096}
\abx@aux@segm{0}{0}{ref_186096}
\abx@aux@cite{0}{ref_186093}
\abx@aux@segm{0}{0}{ref_186093}
\abx@aux@cite{0}{ref_186062}
\abx@aux@segm{0}{0}{ref_186062}
\abx@aux@cite{0}{ref_186063}
\abx@aux@segm{0}{0}{ref_186063}
\abx@aux@cite{0}{ref_186063}
\abx@aux@segm{0}{0}{ref_186063}
\abx@aux@cite{0}{ref_186064}
\abx@aux@segm{0}{0}{ref_186064}
\abx@aux@cite{0}{ref_186065}
\abx@aux@segm{0}{0}{ref_186065}
\abx@aux@cite{0}{ref_186066}
\abx@aux@segm{0}{0}{ref_186066}
\abx@aux@cite{0}{ref_186067}
\abx@aux@segm{0}{0}{ref_186067}
\abx@aux@cite{0}{ref_186068}
\abx@aux@segm{0}{0}{ref_186068}
\abx@aux@cite{0}{ref_186051}
\abx@aux@segm{0}{0}{ref_186051}
\abx@aux@cite{0}{ref_186069}
\abx@aux@segm{0}{0}{ref_186069}
\zref@newlabel{mdf@pagelabel-1}{\default{}\page{2}\abspage{2}\mdf@pagevalue{2}}
\@writefile{toc}{\contentsline {section}{\numberline {1}Insert A head here}{2}{section.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {1.1}Insert B head here}{2}{subsection.1.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsubsection}{\numberline {1.1.1}Insert C head here}{2}{subsubsection.1.1.1}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {2}Equations}{2}{section.2}\protected@file@percent }
\abx@aux@nociteall
\newlabel{eq:first}{{1}{3}{Equations}{equation.1}{}}
\newlabel{eq:another}{{2}{3}{Equations}{equation.2}{}}
\@writefile{toc}{\contentsline {section}{\numberline {3}Figures \& Tables}{3}{section.3}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Insert figure caption here}}{3}{figure.caption.2}\protected@file@percent }
\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
\newlabel{fig:kksim}{{1}{3}{Insert figure caption here}{figure.caption.2}{}}
\@writefile{toc}{\contentsline {section}{\numberline {4}Conclusion}{3}{section.4}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Acknowledgments}{3}{section*.4}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Funding Statement}{3}{section*.5}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Competing Interests}{3}{section*.6}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Data Availability Statement}{3}{section*.7}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Ethical Standards}{3}{section*.8}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Author Contributions}{3}{section*.12}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{References}{3}{section*.13}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Insert figure caption here}}{4}{figure.caption.3}\protected@file@percent }
\newlabel{fig:figwide}{{2}{4}{Insert figure caption here}{figure.caption.3}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces {\fontspec {Calibri}\fontsize {8}{9.5}\selectfont \bfseries \color {figerTitleColor} Transfer Learning Process for 4 Labels Profusion} {\vspace {0.5em}\raggedright \fontsize {9}{10.5}\selectfont {(a) depicts initialization of a DenseNet-121 model with ImageNet weights to start with a network that already has a good understanding of basic image features and Image (b) depicts the pre-trained weights act as a form of knowledge transfer from the ImageNet task to target domain of Chest X-ray profusion classification task.}}}}{4}{figure.caption.9}\protected@file@percent }
\newlabel{fig:transfer}{{3}{4}{{\fontspec {Calibri}\footnotesize \bfseries \color {figerTitleColor} Transfer Learning Process for 4 Labels Profusion}\\{\vspace {0.5em}\raggedright \small {(a) depicts initialization of a DenseNet-121 model with ImageNet weights to start with a network that already has a good understanding of basic image features and Image (b) depicts the pre-trained weights act as a form of knowledge transfer from the ImageNet task to target domain of Chest X-ray profusion classification task.}}}{figure.caption.9}{}}
\newlabel{tab:5566}{{\caption@xref {tab:5566}{ on input line 275}}{5}{Ethical Standards}{table.caption.10}{}}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces \relax }}{5}{table.caption.11}\protected@file@percent }
\newlabel{tab:5566}{{1}{5}{\relax }{table.caption.11}{}}
\newlabel{LastPage}{{4}{6}{Author Contributions}{page.6}{}}
\gdef\lastpage@lastpage{6}
\gdef\lastpage@lastpageHy{6}
\abx@aux@read@bbl@mdfivesum{07114DEE97B547C34F33FD840A98AD07}
\abx@aux@read@bblrerun
\abx@aux@defaultrefcontext{0}{Bayer_etal_2013}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{Adade_etal_2007}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186096}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186093}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186062}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186063}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186064}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186065}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186066}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186067}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186068}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186051}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186069}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186052}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186053}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186054}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186055}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186056}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186057}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186058}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186059}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186060}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186061}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186070}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186071}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186072}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186073}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186074}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186075}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186076}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186077}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186078}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186079}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186080}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186081}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186082}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186083}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186084}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186085}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186086}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186087}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186088}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186089}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186090}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186091}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186092}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186094}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186095}{none/global//global/global/global}
\abx@aux@defaultrefcontext{0}{ref_186097}{none/global//global/global/global}
\gdef \@abspage@last{6}

2113
document.bbl Normal file
View File

File diff suppressed because it is too large Load Diff

2419
document.bcf Normal file
View File

File diff suppressed because it is too large Load Diff

15
document.blg Normal file
View File

@@ -0,0 +1,15 @@
[0] Config.pm:328> INFO - This is Biber 2.21
[0] Config.pm:331> INFO - Logfile is 'document.blg'
[88] biber-MSWIN64:342> INFO - ===
[121] Biber.pm:421> INFO - Reading 'document.bcf'
[204] Biber.pm:999> INFO - Using all citekeys in bib section 0
[214] Biber.pm:4487> INFO - Processing section 0
[222] Biber.pm:4678> INFO - Looking for bibtex file 'example.bib' for section 0
[274] bibtex.pm:1713> INFO - LaTeX decoding ...
[307] bibtex.pm:1519> INFO - Found BibTeX data source 'example.bib'
[508] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
[508] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
[508] Biber.pm:4307> INFO - Sorting list 'none/global//global/global/global' of type 'entry' with template 'none' and locale 'en-US'
[508] Biber.pm:4313> INFO - No sort tailoring available for locale 'en-US'
[533] bbl.pm:676> INFO - Writing 'document.bbl' with encoding 'UTF-8'
[560] bbl.pm:779> INFO - Output to document.bbl

2578
document.log Normal file
View File

File diff suppressed because it is too large Load Diff

7
document.out Normal file
View File

@@ -0,0 +1,7 @@
\BOOKMARK [1][-]{section.1}{\376\377\000I\000n\000s\000e\000r\000t\000\040\000A\000\040\000h\000e\000a\000d\000\040\000h\000e\000r\000e}{}% 1
\BOOKMARK [2][-]{subsection.1.1}{\376\377\000I\000n\000s\000e\000r\000t\000\040\000B\000\040\000h\000e\000a\000d\000\040\000h\000e\000r\000e}{section.1}% 2
\BOOKMARK [3][-]{subsubsection.1.1.1}{\376\377\000I\000n\000s\000e\000r\000t\000\040\000C\000\040\000h\000e\000a\000d\000\040\000h\000e\000r\000e}{subsection.1.1}% 3
\BOOKMARK [1][-]{section.2}{\376\377\000E\000q\000u\000a\000t\000i\000o\000n\000s}{}% 4
\BOOKMARK [1][-]{section.3}{\376\377\000F\000i\000g\000u\000r\000e\000s\000\040\000\046\000\040\000T\000a\000b\000l\000e\000s}{}% 5
\BOOKMARK [1][-]{section.4}{\376\377\000C\000o\000n\000c\000l\000u\000s\000i\000o\000n}{}% 6
\BOOKMARK [1][-]{section*.13}{\376\377\000R\000e\000f\000e\000r\000e\000n\000c\000e\000s}{}% 7

BIN
document.pdf Normal file
View File

Binary file not shown.

87
document.run.xml Normal file
View File

@@ -0,0 +1,87 @@
<?xml version="1.0" standalone="yes"?>
<!-- logreq request file -->
<!-- logreq version 1.0 / dtd version 1.0 -->
<!-- Do not edit this file! -->
<!DOCTYPE requests [
<!ELEMENT requests (internal | external)*>
<!ELEMENT internal (generic, (provides | requires)*)>
<!ELEMENT external (generic, cmdline?, input?, output?, (provides | requires)*)>
<!ELEMENT cmdline (binary, (option | infile | outfile)*)>
<!ELEMENT input (file)+>
<!ELEMENT output (file)+>
<!ELEMENT provides (file)+>
<!ELEMENT requires (file)+>
<!ELEMENT generic (#PCDATA)>
<!ELEMENT binary (#PCDATA)>
<!ELEMENT option (#PCDATA)>
<!ELEMENT infile (#PCDATA)>
<!ELEMENT outfile (#PCDATA)>
<!ELEMENT file (#PCDATA)>
<!ATTLIST requests
version CDATA #REQUIRED
>
<!ATTLIST internal
package CDATA #REQUIRED
priority (9) #REQUIRED
active (0 | 1) #REQUIRED
>
<!ATTLIST external
package CDATA #REQUIRED
priority (1 | 2 | 3 | 4 | 5 | 6 | 7 | 8) #REQUIRED
active (0 | 1) #REQUIRED
>
<!ATTLIST provides
type (static | dynamic | editable) #REQUIRED
>
<!ATTLIST requires
type (static | dynamic | editable) #REQUIRED
>
<!ATTLIST file
type CDATA #IMPLIED
>
]>
<requests version="1.0">
<internal package="biblatex" priority="9" active="1">
<generic>latex</generic>
<provides type="dynamic">
<file>document.bcf</file>
</provides>
<requires type="dynamic">
<file>document.bbl</file>
</requires>
<requires type="static">
<file>blx-dm.def</file>
<file>blx-unicode.def</file>
<file>blx-compat.def</file>
<file>biblatex.def</file>
<file>blx-natbib.def</file>
<file>standard.bbx</file>
<file>numeric.bbx</file>
<file>numeric-comp.cbx</file>
<file>biblatex.cfg</file>
<file>english.lbx</file>
</requires>
</internal>
<external package="biblatex" priority="5" active="1">
<generic>biber</generic>
<cmdline>
<binary>biber</binary>
<infile>document</infile>
</cmdline>
<input>
<file>document.bcf</file>
</input>
<output>
<file>document.bbl</file>
</output>
<provides type="dynamic">
<file>document.bbl</file>
</provides>
<requires type="dynamic">
<file>document.bcf</file>
</requires>
<requires type="editable">
<file>example.bib</file>
</requires>
</external>
</requests>

BIN
document.synctex.gz Normal file
View File

Binary file not shown.

344
document.tex Normal file
View File

@@ -0,0 +1,344 @@
\documentclass[
journal=tmr,
journalname={{Traditional Medicine Research}},
layout=largetwo,
year=2025,%年
volume=37,%卷
no=12,%期
page=23,%号
]{tmr-tex}
\doi{10.53388/TMR20250407002}
\journalweb{https://www.tmrjournals.com/tmr}
\usepackage{amsmath}
\usepackage[nopatch]{microtype}
\usepackage{booktabs}
\usepackage[backend=biber]{biblatex}
\usepackage{xcolor}
\usepackage{tabularray}
\addbibresource{example.bib}
\title{Investigating the potential mechanisms of \textit{Wenqing Yin} against atopic dermatitis based on network pharmacology, experimental pharmacology, and molecular docking}
\author{Yi-Xuan Li}
\affiliation{School of Chinese Medicine, Jinan University, 601 Huangpu Avenue West, Tianhe District, Guangzhou, 510632, China}
\alsoaffiliation{Guangzhou Medical University Affiliated Traditional Chinese Medicine Hospital, No. 16 Zhuji Road, Liwan District, Guangzhou, 510130‌‌, China}
\firstauthor
\author{Yi Liao}
\affiliation{School of Chinese Medicine, Jinan University, 601 Huangpu Avenue West, Tianhe District, Guangzhou, 510632, China}
\author{Cheng-Hong Sun}
\affiliation{State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., No. 209 Hongqi Road, Lanshan District, Linyi, 276000, China}
\author{Di Zhang}
\email{dizhang0915@jnu.edu.cn}
\affiliation{School of Chinese Medicine, Jinan University, 601 Huangpu Avenue West, Tianhe District, Guangzhou, 510632, China}
\author{Shu-Jie Tang}
\email{tsj697@163.com}
\affiliation{School of Chinese Medicine, Jinan University, 601 Huangpu Avenue West, Tianhe District, Guangzhou, 510632, China}
\author{Guo-Dong Sun}
\email{sgd96@jnu.edu.cn}
\affiliation{Guandgong Provincial Key Laboratory of Spine and Spinal Cord Reconstruction, The Fifth Affiliated Hospital (Heyuan Shenhe People’s Hospital), Jinan University, Donghuan Road, Zijin, Heyuan 517000, China}
\alsoaffiliation{Department of Orthopedics, First Affiliated Hospital, Jinan University, 613 Huangpu Avenue West, Tianhe District,Guangzhou, 510632, China}
\author{Guo-Dong Sun}
\email{sgd96@jnu.edu.cn}
\firstauthor
\Correspondence{Wei Quan, Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, No. 6, Weiyang West Road, Xianyang712000, China. E-mail: fmmuquanwei@163.com. Ya-Jun Shi, School of Pharmacy, Shaanxi University of Chinese Medicine, No. 1, Middle Section of CenturyAvenue, Xi’an 712046, China..}
\keywords{keyword entry 1, keyword entry 2, keyword entry 3} %
\authorcontributions{Han SY was responsible for formal analysis, investigation, and methodology. Wang JH was responsible for conceptualization, supervision, and writing the original draft. All authors have read and agreed to the published version of the manuscript.}
\competinginterests{The authors declare no conflicts of interest.}
\Acknowledgments{We gratefully acknowledge Prof. Hojun Kim of the College of Korean Medicine, Dongguk University, for his institutional support and encouragement throughout the preparation of this Perspective. This study was supported by the National Research Foundation of Korea (2020R1F1A1074155).}
\Peerreviewinformation{Traditional Medicine Research thanks all anonymous reviewers for their contribution to the peer review of this paper}
\Abbreviations{AAA, aromatic amino acids; ALD, alcoholic liver disease; BCAA, branched-chain amino acids; CHOL, cholesterol; HF, herbal formula; HYP, hyperlipidemia; KM mice, Kunming mice; NOD, non-obese diabetic; SCFAs, short-chain fatty acids; SD rat, Sprague-Dawley rat; T1D, type 1 diabetes; T2D, type 2 diabetes; Treg, regulatory T cells.}
\Citation{Han SY, Wang JH. Therapeutic potential of Prevotella spp. in metabolic disorders: integrating herbal medicine and gut microbiome. Tradit Med Res. 2026;11(2):9. doi: 10.53388/TMR20250806001.}
\received{16 January 2025}
\revised{27 February 2025}
\accepted{16 May 2025}
\Availableonline{17 July 2025}
\Executiveeditor{jinlei wang}
%abstract
\tmrabstract{Background: Wenqing Yin (WQY) is a classic prescription used to treat skin diseases like atopic dermatitis (AD) in China, and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD. Methods: The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD. The symptoms of AD in the ears and backs of the mice were assessed, while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction (qRT-PCR), and the percentages of CD4+ and CD8+ cells in the spleen were analyzed through flow cytometry. The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology. Subsequently, the key genes were tested and verified by qRT-PCR, and the potential active components and target proteins were verified by molecular docking. }
\keywords{Wenqing Yin; atopic dermatitis; mouse model; UPLC-Orbitrap-MS/MS; network pharmacology}
\KeywordImage{11.png}
\begin{document}
\twocolumn
\begin{highlight}
\highlightitem{Highlights}{
Prevotella spp. have emerged as key modulators of host metabolism, exhibiting species-specific effects on gut barrier function, inflammation, and metabolic homeostasis. Recent evidence highlights the potential of high-fiber and herbal interventions to selectively enrich beneficial Prevotella populations. This perspective outlines an ecology-based framework that integrates herbal modulation with microbiota profiling to harness microbe-herb synergy in managing metabolic disorders.
}
\highlightitem{Medical history of objective}{
Traditional Oriental medicine has long regarded the gastrointestinal tract as the foundation of overall health, frequently referring to the spleen–stomach axis as “the foundation of acquired constitution”. Classical texts such as the Huangdi Neijing (compiled in 300–100 B.C.E.) and the Dongui Bogam (compiled in 1610 C.E. by Jun Heo) documented numerous herbal prescriptions designed to enhance digestive function and treat conditions now recognized as metabolic syndromes – characterized by fatigue, obesity, excessive thirst, and impaired digestion. Notably, many of these herbal formulas were historically used to regulate the gastrointestinal environment, “harmonize Qi movement” (keep the body’s energy moving smoothly), and “eliminate internal dampness” (remove extra moisture inside the body), concepts that align with modern understandings of microbial dysbiosis, gut barrier dysfunction, and low-grade systemic inflammation. Contemporary pharmacological research has confirmed that many of these herbs and their active compounds (e.g., berberine, flavonoids, ginsenosides, etc.) can remodel the gut microbiome, modulate Prevotella abundance, and influence bile acid and short-chain fatty acids (SCFAs) pathways.
}
\end{highlight}
\section{Insert A head here}
This demo file is intended to serve as a ``starter file''. It is for preparing manuscript submission only, not for preparing camera-ready versions of manuscripts. Manuscripts will be typeset for publication by the journal, after they have been accepted.
By default, this template uses \texttt{biblatex} and adopts the Chicago referencing style. However, the journal you’re submitting to may require a different reference style; specify the journal you're using with the class' \texttt{journal} option --- see lines 1--7 of \emph{sample.tex} for a list of options and instructions for selecting the journal. If you are using this template on Overleaf, Overleaf's build tool will automatically run \texttt{pdflatex} and \texttt{biber}. If you are compiling this template on your own local \LaTeX{} installation, please execute the following commands:
\begin{enumerate}
\item \verb|pdflatex sample|
\item \verb|biber sample|
\item \verb|pdflatex sample|
\item \verb|pdflatex sample|
\end{enumerate}
Some journals e.g.~\texttt{journal=pasa}
\\require Bib\TeX{}. For such journals, you will need to
\begin{itemize}
\item delete the existing \verb|\addbibresource{example.bib}|;
\item change the existing \verb|\printbibliography| to be\\
\verb|\bibliography{example}| instead.
\end{itemize}
Overleaf will run \texttt{pdflatex} and \texttt{bibtex} automatically as needed. But if you had \emph{first} compiled using another \texttt{journal} option that adopts \texttt{biblatex}, and \emph{then} change the \texttt{journal} option to one that adopts Bib\TeX{}, you may get some compile error messages instead. In this case you will need to do a `Recompile from scratch'; see \url{https://www.overleaf.com/learn/how-to/Clearing_the_cache}.
On a local \LaTeX{} installation, you would need to run these steps instead:
\begin{enumerate}
\item Delete \texttt{sample.aux}, \texttt{sample.bbl} if these files from a previous compile using \texttt{biber} still exist.
\item \verb|pdflatex sample|
\item \verb|bibtex sample|
\item \verb|pdflatex sample|
\item \verb|pdflatex sample|
\end{enumerate}
The desired model should also provide measurable feedback describing the degree of metabolic stress experienced. To achieve this, we compared CV values with optical probing of the nicotinamide adenine dinucleotide (NADH/NAD$^{+}$) ratio using the NADH fluorescence signal (fNADH). Based on previous studies \parencite{ref_194007,ref_194008,ref_194009}, we developed a theoretical framework summarizing current strategies for fNADH probing using photobleaching \parencite{ref_194010}. We identified a key gap: the correlation between fNADH dynamics and subsequent tissue function remains unexplored. We adapted and integrated the fNADH probing protocol into the excitation-wave optical mapping protocol. This allowed us to reveal a direct link between local changes in fNADH during metabolic stress and the delayed formation of a conduction block: the developed model made it possible to record the occurrence of a block in the long term, even in its absence, immediately after reperfusion. A comparison of CV maps and fNADH allowed us to test the entire experimental sample for a correlation between these parameters: the resistance of the NADH/NAD$^{+}$ ratio to photobleaching emerged as a potential prognostic parameter within each group and in the entire sample, regardless of the type of metabolic stress (R$^{2}$ = 0.925, \textit{P} < 0.01). Thus, the developed model made it possible to identify and quantitatively represent the cause-and-effect chain between arrhythmogenesis and cardiac tissue remodeling, as well as to assess the capacity of cardiac tissue for controlled adaptation to metabolic stress.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
\subsection{Insert B head here}
Subsection text here. Lorem ipsum \parencite{Bayer_etal_2013} dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Lorem ipsum dolor sit amet, consectetur \parencite{Adade_etal_2007} adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem \parencite{ref_186096} ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
\subsubsection{Insert C head here}
Subsubsection text here. Lorem ipsum dolor sit amet, \parencite{ref_186093} consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Lorem ipsum dolor sit amet, consectetur \contentBlue{adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. }
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do\endnote{A footnote/endnote} eiusmod tempor incididunt ut labore et dolore magna aliqua.
\section{Equations}
Sample equations. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur\endnote{Another footnote/endnote} adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
% \begin{table*}[htb!]
% \centering
% \caption{ Results of multiple linear regression analysis of factors influencing fall prevention self-management behavior of older adults in the community (n = 675)} %自定义标题
% \label{table-1085} % 自定义标签，用于引用
% \begin{tabular}{cccccccc}
% \toprule % booktabs包的顶部线条（需加载\usepackage{booktabs}）
% \headrow % 应用表头背景色（对应之前的LaTeX配置）
% \textbf{Dependent variable} & \textbf{Independent variable} & \textbf{B-value} & \textbf{S.E.-value} & \textbf{95\% CI} & \textbf{Standardized regression coefficient} & \textit{\textbf{t}}\textbf{-values} & \textit{\textbf{P}}\textbf{ value} \\
% \midrule
% \multirow{18}{*}Fall Prevention Self-management (Scores of FPSMB-Q) & Constant & -19.360 & 15.784 & -50.355-11.635 & - & -1.227 & 0.220 \\
% & \multicolumn{7}{l}Age \\
% & 70-79 years & 5.379 & 2.644 & 0.187-10.571 & 0.076 & 2.035 & 0.042 \\
% & \multicolumn{7}{l}Education \\
% & High school/vocational school & 9.072 & 2.598 & 3.971-14.173 & 0.124 & 3.492 & < 0.001 \\
% & \multicolumn{7}{l}Medical insurance \\
% & Urban and rural resident medical insurance & 10.032 & 3.946 & 2.283-17.781 & 0.119 & 2.542 & 0.011 \\
% & \multicolumn{7}{l}Type of medication taken \\
% & 1-3 types & 12.253 & 3.766 & 4.858-19.648 & 0.178 & 3.254 & 0.001 \\
% & ≥ 4 types & 16.677 & 4.928 & 7.001-26.354 & 0.172 & 3.384 & < 0.001 \\
% & \multicolumn{7}{l}Physical self-assessment status \\
% & Poor & -10.990 & 5.546 & -21.880--0.100 & -0.091 & -1.982 & 0.048 \\
% & \multicolumn{7}{l}Fear of falling \\
% & Not afraid & -8.790 & 2.717 & -14.125--3.456 & -0.118 & -3.236 & 0.001 \\
% & Knowledge, belief, and practice in preventing falls (Scores of KBP-FP-Q) & 1.940 & 0.159 & 1.628-2.252 & 0.420 & 12.204 & < 0.001 \\
% & Fall efficacy (Scores of MFES) & 0.100 & 0.042 & 0.018-0.183 & 0.083 & 2.394 & 0.017 \\
% & Social support (Scores of SSRS) & 0.476 & 0.145 & 0.191-0.761 & 0.119 & 3.276 & 0.01 \\
% & Fall risk (Scores of CV-SAFRS) & -1.830 & 0.468 & -2.749-0.911 & -0.161 & -3.910 & < 0.001 \\
% \bottomrule
% \end{tabular}
% \end{table*}
The historical use of \textit{P}\textit{.}\textit{ incarnata} L. dates back to the Late Archaic period in North America (approximately 8000-2000 B.C.). Archaeological findings suggest that Indigenous peoples of the pre-Columbian era cultivated mutual relationships with various plant species, and \textit{P. incarnata} L. frequently thrived as a weedy crop in human-influenced habitats \parencite{ref_186062}. The genus \textit{Passiflora}, established by Linnaeus, includes around 520 species within the family \textit{Passifloraceae}. Most species are climbing plants native to Central and South America, while a few are distributed across North America, Southeast Asia, and Australia \parencite{ref_186063}. Traditionally, \textit{P. incarnata} L. has been valued in herbal medicine across different regions. In Europe, it was primarily used to treat insomnia and anxiety, whereas in North America, it was commonly consumed as a calming tea. In Brazil, the plant served multiple therapeutic purposes, such as acting as an analgesic, antispasmodic, anti-asthmatic, wormicidal, and sedative agent \parencite{ref_186063,ref_186064,ref_186065}. It has also been employed in Iraq as a sedative and narcotic \parencite{ref_186066}, and in Turkey for ailments like dysmenorrhea, epilepsy, neurosis, insomnia, and neuralgia \parencite{ref_186067}. In Poland, it has been prescribed for hysteria and neurasthenia \parencite{ref_186068}, while in the United States, it has been used to alleviate diarrhoea, menstrual pain, neuralgia, burns, haemorrhoids, and sleep disorders \parencite{ref_186051}. In India, \textit{P. incarnata} L. has been administered to individuals with opiate dependence \parencite{ref_186069}.
%%% Numbered equation
\begin{equation}
\begin{aligned}\label{eq:first}
\frac{\partial u(t,x)}{\partial t} = Au(t,x) \left(1-\frac{u(t,x)}{K}\right)
-B\frac{u(t-\tau,x) w(t,x)}{1+Eu(t-\tau,x)},\\
\frac{\partial w(t,x)}{\partial t} =\delta \frac{\partial^2w(t,x)}{\partial x^2}-Cw(t,x)
+D\frac{u(t-\tau,x)w(t,x)}{1+Eu(t-\tau,x)},
\end{aligned}
\end{equation}
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
\begin{align}\label{eq:another}
\begin{split}
\frac{dU}{dt} &=\alpha U(t)(\gamma -U(t))-\frac{U(t-\tau)W(t)}{1+U(t-\tau)},\\
\frac{dW}{dt} &=-W(t)+\beta\frac{U(t-\tau)W(t)}{1+U(t-\tau)}.
\end{split}
\end{align}
%%%% Unnumbered equation
\begin{align*}
&\frac{\partial(F_1,F_2)}{\partial(c,\omega)}_{(c_0,\omega_0)} = \left|
\begin{array}{ll}
\frac{\partial F_1}{\partial c} &\frac{\partial F_1}{\partial \omega} \\\noalign{\vskip3pt}
\frac{\partial F_2}{\partial c}&\frac{\partial F_2}{\partial \omega}
\end{array}\right|_{(c_0,\omega_0)}\\
&\quad=-4c_0q\omega_0 -4c_0\omega_0p^2 =-4c_0\omega_0(q+p^2)>0.
\end{align*}
\begin{equation}
Y_{\theta}^{n+1} = \sum_{w \in X(\theta)} Y^n \left( h_{w}^n, h_{w}, e_{w}^n \right)
\end{equation}
\begin{equation}
(Vc - Vt) \times 100 / Vc \tag{3}
\end{equation}
\section{Figures \& Tables}
The output for a single-column figure is in \autoref{fig:kksim}. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing \textcolor{blue}{ $M_{p}=0.749\times\sqrt{N_{\max}}$ } elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut \autoref{fig:figwide} labore et dolore magna aliqua.
%See Figure~\ref{fig_wide} for a double-column figure; this is always at the top of a following page.
\begin{figure}[hbt!]
\centering
\includegraphics[width=0.75\linewidth]{example-image-16x10.pdf}
\caption{Insert figure caption here}
\label{fig:kksim}
\end{figure}
\begin{figure*}
\centering
\includegraphics[width=0.8\linewidth]{example-image-16x10.pdf}
\caption{Insert figure caption here}
\label{fig:figwide}
\end{figure*}
See example table in \autoref{tab:5566}.
\section{Conclusion}
The conclusion text goes here.
\paragraph{Acknowledgments}
We are grateful for the technical assistance of A. Author.
\paragraph{Funding Statement}
This research was supported by grants from the <funder-name><doi>(<award ID>); <funder-name><doi>(<award ID>).
\paragraph{Competing Interests}
A statement about any financial, professional, contractual or personal relationships or situations that could be perceived to impact the presentation of the work --- or `None' if none exist
\paragraph{Data Availability Statement}
A statement about how to access data, code and other materials allowing users to understand, verify and replicate findings --- e.g. Replication data and \autoref{tt1bl} code can be found in Harvard Dataverse: \verb+\url{https://doi.org/link}+.
\paragraph{Ethical Standards}
The research meets all ethical guidelines, \autoref{tab:5566} including adherence to the legal requirements of the study country.
\begin{figure*}[htbp] % 用htbp确保浮动体位置稳定
\centering % 整个盒子居中
% 核心：用minipage包裹所有内容，强制顺序
% 1. 图片（第一步显示）
\includegraphics[width=0.9\textwidth]{file_68fd3d0c5df72.png} % 替换你的图片
% 2. 标题（第二步显示）
\caption{{\fontspec{Calibri}\footnotesize\bfseries\color{figerTitleColor} Transfer Learning Process for 4 Labels Profusion}\\{\vspace{0.5em}\raggedright\small {(a) depicts initialization of a DenseNet-121 model with ImageNet weights to start with a network that already has a good understanding of basic image features and Image (b) depicts the pre-trained weights act as a form of knowledge transfer from the ImageNet task to target domain of Chest X-ray profusion classification task.}}}
\label{fig:transfer}
\end{figure*}
\begin{table*}[htb!]
\centering % 必须加：跨栏表格居中，减少溢出
\label{tab:5566}
\begin{tblr}{
% 优化列宽：调整比例，适配长文本自动换行
colspec={X[0.2,l,cmd=\raggedright] X[1.5,l,cmd=\raggedright] X[1,l,cmd=\raggedright] X[0.8,l,cmd=\raggedright]},
% 三线表样式
hline{1}={1.5pt},
hline{2}={0.75pt},
hline{Z}={1.5pt},
% 表头样式：背景为白色（替代none）
row{1}={font=\bfseries, bg=white},
% 偶数行变色（第2行起）
row{even[2]}={bg=evenRowColor},
% 关键：合并单元格强制白色背景（替代none）
cell{*}{*}={merge={bg=white}},
% 紧凑间距+自动换行
rowsep=3pt, % 缩小行间距，减少溢出
colsep=3pt,
vlines={0pt},
% 自动换行：避免长文本溢出
cell{*}{*}={cmd=\raggedright}, % 所有单元格左对齐，自动换行
}%
% 表格内容（保留原有结构）
frou & bbht & sadsa & fdsfds \\
\SetCell[c=2]{l,bg=white} fdsfds(n=50) & & sdvfdg±SD & fesfsdfds \\
\SetCell[r=2]{l,bg=white} fdsfd & fdffds(mmol/L) & 5.2±0.8 & 3.9-6.1 \\
& dfse(mmHg) & 120/80±5 & 90-140/60-90 \\
\SetCell[c=2]{l,bg=white} fesfe(n=50) & & fesf±SD & fesfef \\
\SetCell[r=2]{l,bg=white} fefe & fgvcbvc(mmol/L) & 6.8±1.2 & 3.9-6.1 \\
& sadwadwa(mmHg) & 135/90±8 & 90-140/60-90 \\
\SetCell[c=3]{l,bg=white} fweafwa & & & <0.05 \\
\SetCell[c=2]{l,bg=white} fesfe(n=50) & & fesf±SD & fesfef \\
\SetCell[r=2]{l,bg=white} fefe & fgvcbvc(mmol/L) & 6.8±1.2 & 3.9-6.1 \\
& sadwadwa(mmHg) & 135/90±8 & 90-140/60-90 \\
\SetCell[c=3]{c,bg=white} fweafwa & & & <0.05 \\
\end{tblr}
\end{table*}
\begin{tmrtable*}{}{tab:5566}{X[1] X[0.4] X[0.8]}{1}{}
\textbf{Cluster-ID} & \textbf{Number of targets} & \textbf{Descriptive} \\
\SetCell[r=2]{l,bg=white} Cluster 1 & \SetCell[r=2]{l,bg=white} 94 & Human cytomegalovirus infection \\
Response to lipopolysaccharide \\
\SetCell[r=3]{l,bg=white} Cluster 2 & \SetCell[r=3]{l,bg=white} 11 & Adenosine P1 receptors \\
Regulation of amine transport \\
G protein-coupled adenosine receptor activity \\
\SetCell[r=2]{l,bg=white} Cluster 3 & \SetCell[r=2]{l,bg=white} 2 & Muscarinic acetylcholine receptors \\
Saliva secretion \\
\end{tmrtable*}
\paragraph{Author Contributions}
Please provide an author contributions statement using the CRediT taxonomy roles as a guide {\verb+\url{https://www.casrai.org/credit.html}+}. Conceptualization: A.A; A.B. Methodology: A.A; A.B. Data curation: A.C. Data visualisation: A.C. Writing original draft: A.A; A.B. All authors approved the final submitted draft.
%\endnote in some journals will behave like \footnote; and \printendnotes will not output anything.
% \printendnotes
%
%
% \defbibnote{preamble}{By default, this template uses \texttt{biblatex} and adopts the Chicago referencing style. However, the journal you’re submitting to may require a different reference style; specify the journal you're using with the class' \texttt{journal} option --- see lines 1--8 of \emph{sample.tex} for a list of options and instructions for selecting the journal.}
\nocite{*}
\printbibliography[title={References}]
% \appendix
%
% \section{Example Appendix Section}
%
% Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
\end{document}

481
example.bib Normal file
View File

@@ -0,0 +1,481 @@
% BibTeX file generated for article ID: 3416
% Generated on 2025-12-18 11:45:42
@article{ref_186051,
author={N, DeLight and H, Sachs},
title={Pneumoconiosis},
year={2023},
volume={2023}
}
@article{ref_186052,
author={Evaluation, Institute for Health Metrics and},
title={Findings from the global burden of disease study 2017},
year={2018},
volume={2018}
}
@article{ref_186053,
author={XM, Qi and Y, Luo and MY, Song and al, et},
title={Pneumoconiosis: current status and future prospects},
journal={Chin Med J (Engl)},
year={2021},
volume={134},
number={8},
pages={898},
doi={10.1097/CM9.0000000000001461}
}
@article{ref_186054,
author={QT, Pham},
title={Chest radiography in the diagnosis of pneumoconiosis},
journal={Int J Tuberc Lung Dis},
year={2001},
volume={5},
number={5},
pages={478}
}
@article{ref_186055,
author={Organization, International Labour},
title={ILO International Classification of Radiographs of Pneumoconioses (2023)},
year={2023},
volume={21},
pages={February}
}
@article{ref_186056,
author={EH, Shortliffe and JJ, Cimino and eds},
title={Biomedical Informatics},
journal={Springer International Publishing},
year={2021},
volume={2021},
doi={10.1007/978-3-030-58721-5}
}
@article{ref_186057,
author={E, Alpaydin},
title={Introduction to Machine Learning. 4th Edition},
year={2020},
volume={2020}
}
@article{ref_186058,
author={Y, LeCun and Y, Bengio and G, Hinton},
title={Deep learning},
journal={Nature},
year={2015},
volume={521},
number={7553},
pages={436},
doi={10.1038/nature14539}
}
@article{ref_186059,
author={J, Deng and W, Dong and R, Socher and LJ, Li and Li, Kai and FF, Li},
title={ImageNet: A large-scale hierarchical image database},
journal={IEEE Conference on Computer Vision and Pattern Recognition},
year={2009},
volume={2009},
pages={248},
doi={10.1109/CVPR.2009.5206848}
}
@article{ref_186060,
author={A, Paszke and A, Chaurasia and S, Kim and E, Culurciello},
title={ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation},
year={2016},
volume={2016},
doi={10.48550/ARXIV.1606.02147}
}
@article{ref_186061,
author={S, Sharma and K, Guleria},
title={A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks},
journal={Proc Comput Sci},
year={2023},
volume={218},
pages={357},
doi={10.1016/j.procs.2023.01.018}
}
@article{ref_186062,
author={R, Kundu and R, Das and ZW, Geem and GT, Han and R, Sarkar},
title={Pneumonia detection in chest X-ray images using an ensemble of deep learning models},
journal={PLOS One},
year={2021},
volume={16},
number={9},
pages={e0256630},
doi={10.1371/journal.pone.0256630}
}
@article{ref_186063,
author={F, Yang and ZR, Tang and J, Chen and al, et},
title={Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning},
journal={BMC Med Imaging},
year={2021},
volume={21},
number={1},
pages={189},
doi={10.1186/s12880-021-00723-z}
}
@article{ref_186064,
author={L, Devnath and S, Luo and P, Summons and al, et},
title={Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker’s Chest X-ray Radiography},
journal={JCM},
year={2022},
volume={11},
number={18},
pages={5342},
doi={10.3390/jcm11185342}
}
@article{ref_186065,
author={E, Okumura and I, Kawashita and T, Ishida},
title={Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages},
journal={J Digit Imaging},
year={2017},
volume={30},
number={4},
pages={413},
doi={10.1007/s10278-017-9942-0}
}
@article{ref_186066,
author={Y, Arzhaeva and D, Wang and L, Devnath and al, et},
title={Development of Automated Diagnostic Tools for Pneumoconiosis Detection from Chest X-Ray Radiographs},
journal={Coal Services Health and Safety Trust Project No. 20647},
volume={192938}
}
@article{ref_186067,
author={M, Akgün and I, Ozmen and E, Ozari Yildirim and al, et},
title={Pitfalls of using the ILO classification for silicosis compensation claims},
journal={Occup Med},
year={2022},
volume={72},
number={6},
pages={372},
doi={10.1093/occmed/kqac010}
}
@article{ref_186068,
author={NA, JP and N, Suganuma},
title={Quality assurance in reading radiographs for pneumoconiosis: AIR Pneumo program},
journal={ASEAN J Radiol},
year={2020},
volume={21},
number={1},
pages={73},
doi={10.46475/aseanjr.v21i1.61}
}
@article{ref_186069,
author={Office, International Labour},
title={Guidelines for the use of the ILO International Classification of Radiographs of Pneumoconioses Revised edition 2011},
year={2011},
volume={2011}
}
@article{ref_186070,
author={N, Newra},
title={Lung Mask Image Dataset},
journal={kaggle},
year={2022},
volume={2022}
}
@article{ref_186071,
author={S, Candemir and S, Jaeger and K, Palaniappan and al, et},
title={Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration},
journal={IEEE Trans Med Imaging},
year={2014},
volume={33},
number={2},
pages={577},
doi={10.1109/TMI.2013.2290491}
}
@article{ref_186072,
author={S, Jaeger and A, Karargyris and S, Candemir and al, et},
title={Automatic Tuberculosis Screening Using Chest Radiographs},
journal={IEEE Trans Med Imaging},
year={2014},
volume={33},
number={2},
pages={233},
doi={10.1109/TMI.2013.2284099}
}
@article{ref_186073,
author={O, Ronneberger and P, Fischer and T, Brox},
title={U-Net: Convolutional Networks for Biomedical Image Segmentation},
journal={Lecture Notes in Computer Science},
year={2015},
volume={2015},
pages={234},
doi={10.1007/978-3-319-24574-4_28}
}
@article{ref_186074,
author={J, Long and E, Shelhamer and T, Darrell},
title={Fully convolutional networks for semantic segmentation},
journal={IEEE Trans Pattern Anal Mach Intell},
volume={201},
doi={10.1109/CVPR.2015.7298965}
}
@article{ref_186075,
author={K, He and G, Gkioxari and P, Dollar and R, Girshick},
title={Mask R-CNN},
journal={2017 IEEE International Conference on Computer Vision (ICCV)},
year={2017},
volume={2017},
pages={2980},
doi={10.1109/ICCV.2017.322}
}
@article{ref_186076,
author={L-C, Chen and G, Papandreou and I, Kokkinos and K, Murphy and AL, Yuille},
title={DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs},
journal={IEEE Trans Pattern Anal Mach Intell},
year={2018},
volume={40},
number={4},
pages={834},
doi={10.1109/TPAMI.2017.2699184}
}
@article{ref_186077,
author={M, Romero and Y, Interian and T, Solberg and G, Valdes},
title={Targeted transfer learning to improve performance in small medical physics datasets},
journal={Med Phys},
year={2020},
volume={47},
number={12},
pages={6246},
doi={10.1002/mp.14507}
}
@article{ref_186078,
author={K, He and X, Zhang and S, Ren and J, Sun},
title={Deep Residual Learning for Image Recognition},
journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2016},
volume={2016},
pages={770},
doi={10.1109/CVPR.2016.90}
}
@article{ref_186079,
author={G, Huang and Z, Liu and L, van der Maaten and KQ, Weinberger},
title={Densely Connected Convolutional Networks},
year={2016},
volume={2016},
doi={10.48550/ARXIV.1608.06993}
}
@article{ref_186080,
author={K, Simonyan and A, Zisserman},
title={Very Deep Convolutional Networks for Large-Scale Visual Recognition},
journal={International Conference on Learning Representations},
year={2015},
volume={2015},
doi={10.48550/arXiv.1409.1556}
}
@article{ref_186081,
author={C, Szegedy and Liu, Wei and Jia, Yangqing and al, et},
title={Going deeper with convolutions},
journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2015},
volume={2015},
pages={1},
doi={10.1109/CVPR.2015.7298594}
}
@article{ref_186082,
author={AG, Howard and M, Zhu and B, Chen and al, et},
title={MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications},
year={2017},
volume={2017},
doi={10.48550/ARXIV.1704.04861}
}
@article{ref_186083,
author={P, Rajpurkar and J, Irvin and K, Zhu and al, et},
title={CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning},
year={2017},
volume={2017},
doi={10.48550/ARXIV.1711.05225}
}
@article{ref_186084,
author={JR, Jagoe and KA, Paton},
title={Measurement of Pneumoconiosis by Computer},
journal={IEEE Trans Comput},
year={1976},
volume={1976},
doi={10.1109/TC.1976.5009212}
}
@article{ref_186085,
author={H, Kobatake and K, Oh’ishi and J, Miyamichi},
title={Automatic diagnosis of pneumoconiosis by texture analysis of chest X-ray images},
journal={IEEE International Conference on Acoustics, Speech, and Signal Processing},
year={1987},
volume={1987},
pages={610},
doi={10.1109/ICASSP.1987.1169720}
}
@article{ref_186086,
author={RS, Ledley and HK, Huang and LS, Rotolo},
title={A texture analysis method in classification of coal workers’ pneumoconiosis},
journal={Comput Biol Med},
year={1975},
volume={5},
number={1–2},
pages={53},
doi={10.1016/0010-4825(75)90018-9}
}
@article{ref_186087,
author={V, Murray and MS, Pattichis and H, Davis and ES, Barriga and P, Soliz},
title={Multiscale AM-FM analysis of pneumoconiosis x-ray images},
journal={2009 16th IEEE International Conference on Image Processing (ICIP)},
year={2009},
volume={2009},
pages={4201},
doi={10.1109/ICIP.2009.5414522}
}
@article{ref_186088,
author={CX, Cai and BY, Zhu and H, Chen},
title={Computer-Aided Diagnosis for Pneumoconiosis Based on Texture Analysis on Digital Chest Radiographs},
journal={Appl Mech Mater},
year={2012},
volume={241},
doi={10.4028/www.scientific.net/AMM.241-244.244}
}
@article{ref_186089,
author={P, Yu and H, Xu and Y, Zhu and C, Yang and X, Sun and J, Zhao},
title={An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs},
journal={J Digit Imaging},
year={2010},
volume={24},
number={3},
pages={382},
doi={10.1007/s10278-010-9276-7}
}
@article{ref_186090,
author={MS, Pattichis and CS, Pattichis and CI, Christodoulou and D, James and L, Ketai and P, Soliz},
title={A screening system for the assessment of opacity profusion in chest radiographs of miners with pneumoconiosis},
journal={Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation},
year={2002},
volume={2002},
pages={130},
doi={10.1109/IAI.2002.999904}
}
@article{ref_186091,
author={P, Soliz and MS, Pattichis and J, Ramachandran and DS, James},
title={Computer-assisted diagnosis of chest radiographs for pneumoconioses},
journal={SPIE Proceedings},
year={2001},
volume={4322},
pages={667},
doi={10.1117/12.431143}
}
@article{ref_186092,
author={L, Zhang and R, Rong and Q, Li and al, et},
title={A deep learning-based model for screening and staging pneumoconiosis},
journal={Sci Rep},
year={2021},
volume={11},
number={1},
pages={2201},
doi={10.1038/s41598-020-77924-z}
}
@article{ref_186093,
author={D, Wang and Y, Arzhaeva and L, Devnath and al, et},
title={Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs},
journal={2020 Digital Image Computing: Techniques and Applications (DICTA)},
year={2020},
volume={2020},
pages={1},
doi={10.1109/DICTA51227.2020.9363416}
}
@article{ref_186094,
author={L, Devnath and S, Luo and P, Summons and D, Wang},
title={Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs},
journal={Comput Biol Med},
year={2021},
volume={129},
pages={104125},
doi={10.1016/j.compbiomed.2020.104125}
}
@article{ref_186095,
author={L, Devnath and S, Luo and P, Summons and D, Wang},
title={Performance Comparison of Deep Learning Models for Black Lung Detection on Chest X-ray Radiographs},
journal={Proceedings of the 3rd International Conference on Software Engineering and Information Management January},
year={2020},
volume={2020},
pages={150},
doi={10.1145/3378936.3378968}
}
@article{ref_186096,
author={R, Zheng and K, Deng and H, Jin and H, Liu and L, Zhang},
title={An Improved CNN-Based Pneumoconiosis Diagnosis Method on X-ray Chest Film},
journal={Lecture Notes in Computer Science},
year={2019},
volume={2019},
pages={647},
doi={10.1007/978-3-030-37429-7_66}
}
@article{ref_186097,
author={W, Ehab and L, Huang and Y, Li},
title={UNet and Variants for Medical Image Segmentation},
journal={Int J Network Dyn Intell},
year={2024},
volume={3},
number={2},
pages={100009},
doi={10.53941/ijndi.2024.100009}
}
@article{Adade_etal_2007,
title={Ultrastructural localization of \emph{Trypanosoma cruzi} lysosomes by aryl sulphatase cytochemistry},
author={Adade, Camila M and de Castro, Solange L and Soares, Maurilio J},
journal={Micron},
volume={38},
number={3},
pages={252-256},
year={2007},
% publisher={Elsevier},
doi={10.1016/j.micron.2006.11.003}
}
@article{Bayer_etal_2013,
title={Proteomic analysis of Trypanosoma cruzi secretome: characterization of two populations of extracellular vesicles and soluble proteins},
author={Bayer-Santos, Ethel and Aguilar-Bonavides, Clemente and Rodrigues, Silas Pessini and Cordero, Esteban Maurício and Marques, Alexandre Ferreira and Varela-Ramirez, Armando and Choi, Hyungwon and Yoshida, Nobuko and Da Silveira, José Franco and Almeida, Igor C},
journal={Journal of Proteome Research},
volume={12},
number={2},
pages={883-897},
year={2013},
% publisher={ACS Publications},
doi={10.1021/pr300948z}
}

216
fangji_huangqi.aux Normal file
View File

@@ -0,0 +1,216 @@
\relax
\providecommand\zref@newlabel[2]{}
\providecommand\hyper@newdestlabel[2]{}
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\abx@aux@refcontext{none/global//global/global/global}
\abx@aux@cite{0}{ref1}
\abx@aux@segm{0}{0}{ref1}
\abx@aux@cite{0}{ref1}
\abx@aux@segm{0}{0}{ref1}
\abx@aux@cite{0}{ref2}
\abx@aux@segm{0}{0}{ref2}
\abx@aux@cite{0}{ref2}
\abx@aux@segm{0}{0}{ref2}
\abx@aux@cite{0}{ref2}
\abx@aux@segm{0}{0}{ref2}
\abx@aux@cite{0}{ref3}
\abx@aux@segm{0}{0}{ref3}
\abx@aux@cite{0}{ref4}
\abx@aux@segm{0}{0}{ref4}
\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{2}{section.1}\protected@file@percent }
\abx@aux@cite{0}{ref3}
\abx@aux@segm{0}{0}{ref3}
\abx@aux@cite{0}{ref4}
\abx@aux@segm{0}{0}{ref4}
\abx@aux@cite{0}{ref5}
\abx@aux@segm{0}{0}{ref5}
\abx@aux@cite{0}{ref6}
\abx@aux@segm{0}{0}{ref6}
\abx@aux@cite{0}{ref7}
\abx@aux@segm{0}{0}{ref7}
\abx@aux@cite{0}{ref8}
\abx@aux@segm{0}{0}{ref8}
\abx@aux@cite{0}{ref9}
\abx@aux@segm{0}{0}{ref9}
\abx@aux@cite{0}{ref10}
\abx@aux@segm{0}{0}{ref10}
\abx@aux@cite{0}{ref11}
\abx@aux@segm{0}{0}{ref11}
\abx@aux@cite{0}{ref12}
\abx@aux@segm{0}{0}{ref12}
\abx@aux@cite{0}{ref13}
\abx@aux@segm{0}{0}{ref13}
\abx@aux@cite{0}{ref14}
\abx@aux@segm{0}{0}{ref14}
\abx@aux@cite{0}{ref15}
\abx@aux@segm{0}{0}{ref15}
\abx@aux@cite{0}{ref16}
\abx@aux@segm{0}{0}{ref16}
\abx@aux@cite{0}{ref17}
\abx@aux@segm{0}{0}{ref17}
\abx@aux@cite{0}{ref18}
\abx@aux@segm{0}{0}{ref18}
\abx@aux@cite{0}{ref19}
\abx@aux@segm{0}{0}{ref19}
\abx@aux@cite{0}{ref20}
\abx@aux@segm{0}{0}{ref20}
\abx@aux@cite{0}{ref21}
\abx@aux@segm{0}{0}{ref21}
\abx@aux@cite{0}{ref22}
\abx@aux@segm{0}{0}{ref22}
\abx@aux@cite{0}{ref23}
\abx@aux@segm{0}{0}{ref23}
\abx@aux@cite{0}{ref24}
\abx@aux@segm{0}{0}{ref24}
\abx@aux@cite{0}{ref25}
\abx@aux@segm{0}{0}{ref25}
\abx@aux@cite{0}{ref26}
\abx@aux@segm{0}{0}{ref26}
\@writefile{toc}{\contentsline {section}{\numberline {2}Materials and methods}{3}{section.2}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {3}Chemical basis of Fangji Huangqi Decoction}{3}{section.3}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {4}Pharmacological effects and mechanisms}{3}{section.4}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Anti-inflammatory effects}{3}{subsection.4.1}\protected@file@percent }
\abx@aux@cite{0}{ref27}
\abx@aux@segm{0}{0}{ref27}
\abx@aux@cite{0}{ref28}
\abx@aux@segm{0}{0}{ref28}
\abx@aux@cite{0}{ref29}
\abx@aux@segm{0}{0}{ref29}
\abx@aux@cite{0}{ref30}
\abx@aux@segm{0}{0}{ref30}
\abx@aux@cite{0}{ref31}
\abx@aux@segm{0}{0}{ref31}
\abx@aux@cite{0}{ref32}
\abx@aux@segm{0}{0}{ref32}
\abx@aux@cite{0}{ref33}
\abx@aux@segm{0}{0}{ref33}
\abx@aux@cite{0}{ref34}
\abx@aux@segm{0}{0}{ref34}
\abx@aux@cite{0}{ref35}
\abx@aux@segm{0}{0}{ref35}
\abx@aux@cite{0}{ref36}
\abx@aux@segm{0}{0}{ref36}
\abx@aux@cite{0}{ref37}
\abx@aux@segm{0}{0}{ref37}
\abx@aux@cite{0}{ref38}
\abx@aux@segm{0}{0}{ref38}
\abx@aux@cite{0}{ref39}
\abx@aux@segm{0}{0}{ref39}
\abx@aux@cite{0}{ref40}
\abx@aux@segm{0}{0}{ref40}
\abx@aux@cite{0}{ref41}
\abx@aux@segm{0}{0}{ref41}
\abx@aux@cite{0}{ref42}
\abx@aux@segm{0}{0}{ref42}
\abx@aux@cite{0}{ref43}
\abx@aux@segm{0}{0}{ref43}
\abx@aux@cite{0}{ref44}
\abx@aux@segm{0}{0}{ref44}
\abx@aux@cite{0}{ref45}
\abx@aux@segm{0}{0}{ref45}
\abx@aux@cite{0}{ref46}
\abx@aux@segm{0}{0}{ref46}
\abx@aux@cite{0}{ref47}
\abx@aux@segm{0}{0}{ref47}
\abx@aux@cite{0}{ref48}
\abx@aux@segm{0}{0}{ref48}
\abx@aux@cite{0}{ref49}
\abx@aux@segm{0}{0}{ref49}
\abx@aux@cite{0}{ref50}
\abx@aux@segm{0}{0}{ref50}
\abx@aux@cite{0}{ref51}
\abx@aux@segm{0}{0}{ref51}
\abx@aux@cite{0}{ref52}
\abx@aux@segm{0}{0}{ref52}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Improvement of ventricular remodeling and myocardial fibrosis}{4}{subsection.4.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Antitumor effects}{4}{subsection.4.3}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Regulation of glucose and lipid metabolism}{4}{subsection.4.4}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {4.5}Renal protective effects}{4}{subsection.4.5}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {5}Clinical research progress on Fangji Huangqi Decoction}{4}{section.5}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}Kidney diseases}{4}{subsection.5.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Rheumatic and joint diseases}{4}{subsection.5.2}\protected@file@percent }
\abx@aux@cite{0}{ref53}
\abx@aux@segm{0}{0}{ref53}
\abx@aux@cite{0}{ref54}
\abx@aux@segm{0}{0}{ref54}
\abx@aux@cite{0}{ref55}
\abx@aux@segm{0}{0}{ref55}
\abx@aux@cite{0}{ref56}
\abx@aux@segm{0}{0}{ref56}
\abx@aux@cite{0}{ref57}
\abx@aux@segm{0}{0}{ref57}
\abx@aux@cite{0}{ref58}
\abx@aux@segm{0}{0}{ref58}
\abx@aux@cite{0}{ref59}
\abx@aux@segm{0}{0}{ref59}
\abx@aux@cite{0}{ref60}
\abx@aux@segm{0}{0}{ref60}
\abx@aux@cite{0}{ref3}
\abx@aux@segm{0}{0}{ref3}
\abx@aux@cite{0}{ref4}
\abx@aux@segm{0}{0}{ref4}
\abx@aux@cite{0}{ref5}
\abx@aux@segm{0}{0}{ref5}
\abx@aux@cite{0}{ref6}
\abx@aux@segm{0}{0}{ref6}
\abx@aux@cite{0}{ref7}
\abx@aux@segm{0}{0}{ref7}
\abx@aux@cite{0}{ref8}
\abx@aux@segm{0}{0}{ref8}
\abx@aux@cite{0}{ref9}
\abx@aux@segm{0}{0}{ref9}
\abx@aux@cite{0}{ref10}
\abx@aux@segm{0}{0}{ref10}
\abx@aux@cite{0}{ref11}
\abx@aux@segm{0}{0}{ref11}
\abx@aux@cite{0}{ref12}
\abx@aux@segm{0}{0}{ref12}
\abx@aux@cite{0}{ref61}
\abx@aux@segm{0}{0}{ref61}
\abx@aux@cite{0}{ref62}
\abx@aux@segm{0}{0}{ref62}
\abx@aux@cite{0}{ref63}
\abx@aux@segm{0}{0}{ref63}
\abx@aux@cite{0}{ref64}
\abx@aux@segm{0}{0}{ref64}
\abx@aux@cite{0}{ref65}
\abx@aux@segm{0}{0}{ref65}
\abx@aux@cite{0}{ref66}
\abx@aux@segm{0}{0}{ref66}
\abx@aux@cite{0}{ref67}
\abx@aux@segm{0}{0}{ref67}
\abx@aux@cite{0}{ref68}
\abx@aux@segm{0}{0}{ref68}
\abx@aux@cite{0}{ref69}
\abx@aux@segm{0}{0}{ref69}
\abx@aux@cite{0}{ref70}
\abx@aux@segm{0}{0}{ref70}
\abx@aux@cite{0}{ref71}
\abx@aux@segm{0}{0}{ref71}
\abx@aux@cite{0}{ref72}
\abx@aux@segm{0}{0}{ref72}
\abx@aux@cite{0}{ref73}
\abx@aux@segm{0}{0}{ref73}
\abx@aux@cite{0}{ref74}
\abx@aux@segm{0}{0}{ref74}
\abx@aux@cite{0}{ref75}
\abx@aux@segm{0}{0}{ref75}
\abx@aux@cite{0}{ref76}
\abx@aux@segm{0}{0}{ref76}
\abx@aux@cite{0}{ref77}
\abx@aux@segm{0}{0}{ref77}
\abx@aux@cite{0}{ref78}
\abx@aux@segm{0}{0}{ref78}
\abx@aux@cite{0}{ref3}
\abx@aux@segm{0}{0}{ref3}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.3}Cardiovascular diseases}{5}{subsection.5.3}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {5.4}Metabolic diseases (obesity)}{5}{subsection.5.4}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {6}Systematic Prediction of Q-Markers for Fangji Huangqi Decoction Based on the Five Principles}{5}{section.6}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}Prediction Based on Transmissibility and Traceability}{5}{subsection.6.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {6.2}Prediction Based on Specificity}{5}{subsection.6.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {6.3}Prediction Based on Efficacy}{5}{subsection.6.3}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {6.4}Prediction Based on the Compatibility Environment}{5}{subsection.6.4}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Prediction analysis path of Q-Markers for Fangji Huangqi Decoction. Note: The study starts from the six component herbs and their main chemical groups. Then it applies the five Q-Marker principles: Transmissibility and traceability: select components that can be traced from raw herbs through decoction to measurable forms in plasma or target tissues; Specificity: prioritize components with high species specificity that can distinguish genuine herbs from adulterants; Efficacy: select components with clear evidence supporting their contribution to key actions such as tonifying Qi, promoting diuresis, and reducing inflammation; Compatibility environment: consider the behavior of components in the multi-herb decoction, including solubility changes, interactions, and stability; Measurability: ensure that candidate markers are stable and can be accurately measured by standard analytical methods. Through this step-by-step screening, we finally determined a set of ten core Q-Markers.}}{6}{figure.caption.1}\protected@file@percent }
\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
\newlabel{fig:qmarker_path}{{1}{6}{Prediction analysis path of Q-Markers for Fangji Huangqi Decoction. Note: The study starts from the six component herbs and their main chemical groups. Then it applies the five Q-Marker principles: Transmissibility and traceability: select components that can be traced from raw herbs through decoction to measurable forms in plasma or target tissues; Specificity: prioritize components with high species specificity that can distinguish genuine herbs from adulterants; Efficacy: select components with clear evidence supporting their contribution to key actions such as tonifying Qi, promoting diuresis, and reducing inflammation; Compatibility environment: consider the behavior of components in the multi-herb decoction, including solubility changes, interactions, and stability; Measurability: ensure that candidate markers are stable and can be accurately measured by standard analytical methods. Through this step-by-step screening, we finally determined a set of ten core Q-Markers}{figure.caption.1}{}}

2471
fangji_huangqi.bcf Normal file
View File

File diff suppressed because it is too large Load Diff

2762
fangji_huangqi.log Normal file
View File

File diff suppressed because it is too large Load Diff

19
fangji_huangqi.out Normal file
View File

@@ -0,0 +1,19 @@
\BOOKMARK [1][-]{section.1}{\376\377\000I\000n\000t\000r\000o\000d\000u\000c\000t\000i\000o\000n}{}% 1
\BOOKMARK [1][-]{section.2}{\376\377\000M\000a\000t\000e\000r\000i\000a\000l\000s\000\040\000a\000n\000d\000\040\000m\000e\000t\000h\000o\000d\000s}{}% 2
\BOOKMARK [1][-]{section.3}{\376\377\000C\000h\000e\000m\000i\000c\000a\000l\000\040\000b\000a\000s\000i\000s\000\040\000o\000f\000\040\000F\000a\000n\000g\000j\000i\000\040\000H\000u\000a\000n\000g\000q\000i\000\040\000D\000e\000c\000o\000c\000t\000i\000o\000n}{}% 3
\BOOKMARK [1][-]{section.4}{\376\377\000P\000h\000a\000r\000m\000a\000c\000o\000l\000o\000g\000i\000c\000a\000l\000\040\000e\000f\000f\000e\000c\000t\000s\000\040\000a\000n\000d\000\040\000m\000e\000c\000h\000a\000n\000i\000s\000m\000s}{}% 4
\BOOKMARK [2][-]{subsection.4.1}{\376\377\000A\000n\000t\000i\000-\000i\000n\000f\000l\000a\000m\000m\000a\000t\000o\000r\000y\000\040\000e\000f\000f\000e\000c\000t\000s}{section.4}% 5
\BOOKMARK [2][-]{subsection.4.2}{\376\377\000I\000m\000p\000r\000o\000v\000e\000m\000e\000n\000t\000\040\000o\000f\000\040\000v\000e\000n\000t\000r\000i\000c\000u\000l\000a\000r\000\040\000r\000e\000m\000o\000d\000e\000l\000i\000n\000g\000\040\000a\000n\000d\000\040\000m\000y\000o\000c\000a\000r\000d\000i\000a\000l\000\040\000f\000i\000b\000r\000o\000s\000i\000s}{section.4}% 6
\BOOKMARK [2][-]{subsection.4.3}{\376\377\000A\000n\000t\000i\000t\000u\000m\000o\000r\000\040\000e\000f\000f\000e\000c\000t\000s}{section.4}% 7
\BOOKMARK [2][-]{subsection.4.4}{\376\377\000R\000e\000g\000u\000l\000a\000t\000i\000o\000n\000\040\000o\000f\000\040\000g\000l\000u\000c\000o\000s\000e\000\040\000a\000n\000d\000\040\000l\000i\000p\000i\000d\000\040\000m\000e\000t\000a\000b\000o\000l\000i\000s\000m}{section.4}% 8
\BOOKMARK [2][-]{subsection.4.5}{\376\377\000R\000e\000n\000a\000l\000\040\000p\000r\000o\000t\000e\000c\000t\000i\000v\000e\000\040\000e\000f\000f\000e\000c\000t\000s}{section.4}% 9
\BOOKMARK [1][-]{section.5}{\376\377\000C\000l\000i\000n\000i\000c\000a\000l\000\040\000r\000e\000s\000e\000a\000r\000c\000h\000\040\000p\000r\000o\000g\000r\000e\000s\000s\000\040\000o\000n\000\040\000F\000a\000n\000g\000j\000i\000\040\000H\000u\000a\000n\000g\000q\000i\000\040\000D\000e\000c\000o\000c\000t\000i\000o\000n}{}% 10
\BOOKMARK [2][-]{subsection.5.1}{\376\377\000K\000i\000d\000n\000e\000y\000\040\000d\000i\000s\000e\000a\000s\000e\000s}{section.5}% 11
\BOOKMARK [2][-]{subsection.5.2}{\376\377\000R\000h\000e\000u\000m\000a\000t\000i\000c\000\040\000a\000n\000d\000\040\000j\000o\000i\000n\000t\000\040\000d\000i\000s\000e\000a\000s\000e\000s}{section.5}% 12
\BOOKMARK [2][-]{subsection.5.3}{\376\377\000C\000a\000r\000d\000i\000o\000v\000a\000s\000c\000u\000l\000a\000r\000\040\000d\000i\000s\000e\000a\000s\000e\000s}{section.5}% 13
\BOOKMARK [2][-]{subsection.5.4}{\376\377\000M\000e\000t\000a\000b\000o\000l\000i\000c\000\040\000d\000i\000s\000e\000a\000s\000e\000s\000\040\000\050\000o\000b\000e\000s\000i\000t\000y\000\051}{section.5}% 14
\BOOKMARK [1][-]{section.6}{\376\377\000S\000y\000s\000t\000e\000m\000a\000t\000i\000c\000\040\000P\000r\000e\000d\000i\000c\000t\000i\000o\000n\000\040\000o\000f\000\040\000Q\000-\000M\000a\000r\000k\000e\000r\000s\000\040\000f\000o\000r\000\040\000F\000a\000n\000g\000j\000i\000\040\000H\000u\000a\000n\000g\000q\000i\000\040\000D\000e\000c\000o\000c\000t\000i\000o\000n\000\040\000B\000a\000s\000e\000d\000\040\000o\000n\000\040\000t\000h\000e\000\040\000F\000i\000v\000e\000\040\000P\000r\000i\000n\000c\000i\000p\000l\000e\000s}{}% 15
\BOOKMARK [2][-]{subsection.6.1}{\376\377\000P\000r\000e\000d\000i\000c\000t\000i\000o\000n\000\040\000B\000a\000s\000e\000d\000\040\000o\000n\000\040\000T\000r\000a\000n\000s\000m\000i\000s\000s\000i\000b\000i\000l\000i\000t\000y\000\040\000a\000n\000d\000\040\000T\000r\000a\000c\000e\000a\000b\000i\000l\000i\000t\000y}{section.6}% 16
\BOOKMARK [2][-]{subsection.6.2}{\376\377\000P\000r\000e\000d\000i\000c\000t\000i\000o\000n\000\040\000B\000a\000s\000e\000d\000\040\000o\000n\000\040\000S\000p\000e\000c\000i\000f\000i\000c\000i\000t\000y}{section.6}% 17
\BOOKMARK [2][-]{subsection.6.3}{\376\377\000P\000r\000e\000d\000i\000c\000t\000i\000o\000n\000\040\000B\000a\000s\000e\000d\000\040\000o\000n\000\040\000E\000f\000f\000i\000c\000a\000c\000y}{section.6}% 18
\BOOKMARK [2][-]{subsection.6.4}{\376\377\000P\000r\000e\000d\000i\000c\000t\000i\000o\000n\000\040\000B\000a\000s\000e\000d\000\040\000o\000n\000\040\000t\000h\000e\000\040\000C\000o\000m\000p\000a\000t\000i\000b\000i\000l\000i\000t\000y\000\040\000E\000n\000v\000i\000r\000o\000n\000m\000e\000n\000t}{section.6}% 19

BIN
fangji_huangqi.synctex.gz Normal file
View File

Binary file not shown.

249
fangji_huangqi.tex Normal file
View File

@@ -0,0 +1,249 @@
\documentclass[manuscript=article]{tmr-tex}
% ===== 期刊信息 =====
\journalname{Traditional Medicine Research}
\doi{10.xxxx/tmr.2025.12345}
\year{2025}
\volume{10}
\no{2}
\page{100--120}
\journalweb{https://www.tmr-journal.com}
% ===== 作者信息 =====
\author{Gaofei Yan}
\affiliation[Henan Univ. CM]{Department of Orthopaedics, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, China}
\alsoaffiliation{The First Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou 450046, China}
\email{lihuiying39@163.com}
\author{Peng Yu}
\affiliation[Henan Univ. CM]{Department of Orthopaedics, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, China}
\author{Xianzhong Bu}
\affiliation[Henan Univ. CM]{Department of Orthopaedics, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, China}
\author{Zhengguo Wang}
\affiliation[Henan Univ. CM]{Department of Orthopaedics, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, China}
\alsoaffiliation{The First Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou 450046, China}
\Correspondence{Department of Orthopaedics, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, China, E-mail: lihuiying39@163.com}
\Executiveeditor{Dr. Editor Name}
\keywords{Fangji Huangqi Decoction; Chemical constituents; Pharmacological effects; Clinical application; Quality markers (Q-Markers)}
\tmrabstract{Fangji Huangqi Decoction was first recorded in the classic text Jinkui Yaolüe by Zhang Zhongjing in the Eastern Han Dynasty. The formula is composed of six herbs: Fangji (Stephania tetrandra), Huangqi (Astragalus membranaceus), Baizhu (Atractylodes macrocephala), Gancao (Glycyrrhiza uralensis), fresh ginger, and jujube. It is a representative prescription for "wind–water" syndrome caused by exterior deficiency and excess dampness. Traditionally, it is used to tonify Qi, dispel wind, strengthen the spleen, and promote urination. Modern studies show that this decoction contains a wide range of chemical components, including bisbenzylisoquinoline alkaloids, triterpenoid saponins, flavonoids, sesquiterpene lactones, and polysaccharides. Pharmacological research has confirmed that it can regulate body fluid metabolism, reduce inflammation and pain, improve ventricular remodeling, protect against renal fibrosis, inhibit tumor growth, and regulate glucose and lipid metabolism. Clinically, it has been widely used for nephrotic syndrome, chronic heart failure, rheumatoid arthritis, obesity, and postoperative edema, with clear therapeutic benefits. To improve the quality control system for this classical formula, this paper reviews recent research on its chemical components, pharmacological actions, and clinical applications. Based on the "five principles" of quality markers (Q-Markers) for traditional Chinese medicine—transmissibility and traceability, specificity, efficacy, compatibility in the formula, and measurability—we carry out a comprehensive prediction of potential Q-Markers covering all six herbs. As a result, ten compounds are identified as core candidate Q-Markers: tetrandrine, fangchinoline, astragaloside IV, calycosin-7-O-β-D-glucoside, atractylenolide I, atractylenolide III, glycyrrhizic acid, liquiritin, 6-gingerol, and cyclic adenosine monophosphate (cAMP). These findings provide a basis for the secondary development of Fangji Huangqi Decoction and for establishing a more complete quality control system.}
\begin{document}
\section{Introduction}
Fangji Huangqi Decoction was first described in \textit{Jinkui Yaolüe} $\cdot$ Water Qi Syndrome and Pulse Patterns, Chapter Fourteen, where it is stated: ``For wind–water syndrome with floating pulse, heavy body, spontaneous sweating and aversion to wind, Fangji Huangqi Decoction is indicated.'' The original prescription includes Fangji, Huangqi, Baizhu, and honey-fried Gancao, and is decocted with fresh ginger and jujube. In traditional Chinese medicine (TCM), Fangji is bitter and cold, and is used to dispel wind, remove dampness, and relieve edema. Huangqi is sweet and warm; it tonifies Qi, consolidates the exterior, and promotes water circulation. Used together, they act as the chief herbs, combining elimination of pathogenic factors with support of healthy Qi. Baizhu strengthens the spleen and dries dampness, assisting Huangqi in tonifying Qi and Fangji in transporting and transforming fluids. Gancao harmonizes the other herbs and also supports the spleen. Fresh ginger and jujube harmonize the nutritive and defensive Qi and protect the middle Jiao\cite{ref1}. Overall, the formula is considered a typical prescription for exterior deficiency with internal dampness, with functions of tonifying Qi, dispelling wind, strengthening the spleen, and promoting urination.
With changes in modern medical concepts, Fangji Huangqi Decoction and its modified forms have been applied to a variety of diseases including nephrotic syndrome, glomerulonephritis, diabetic nephropathy, chronic heart failure, rheumatoid arthritis, obesity, liver cirrhosis with ascites, breast cancer-related lymphedema, and postoperative edema\cite{ref1,ref2}. These applications reflect the TCM principle of ``treating different diseases with the same method'' based on pattern identification. At the same time, the modernization of TCM has highlighted the need for scientific quality control systems that can link the complex material basis of formulas with their clinical effects.
The concept of quality markers (Q-Markers), proposed by Liu Changxiao and colleagues, defines a TCM Q-Marker as a component that: 1) can be traced from the raw herb, through processing and preparation, to the body (transmissibility and traceability); 2) is characteristic for a given herb and helps distinguish it from adulterants (specificity); 3) is clearly related to the main therapeutic effects (efficacy); 4) remains meaningful and stable in the multi-herb compatibility environment (compatibility); and 5) can be accurately measured by modern analytical methods (measurability)\cite{ref2}. Although several studies have reported the chemical composition, pharmacology, and clinical use of Fangji Huangqi Decoction, few have tried to build a Q-Marker system that covers all six herbs and connects the material basis to traditional functions and modern indications. In particular, quality indicators for assistant and envoy herbs such as ginger and jujube are often neglected. This limits the establishment of comprehensive quality standards and the further development of standardized products.
The present review aims to: (1) summarize current knowledge on the chemical constituents and pharmacological effects of Fangji Huangqi Decoction; (2) review its major clinical applications; and (3) apply the five Q-Marker principles to predict potential Q-Markers for all six herbs in the formula, and propose a preliminary multi-component quality control scheme.
\section{Materials and methods}
The present work is a narrative review with structured analysis. We searched CNKI, Wanfang, VIP, PubMed, and Web of Science for literature using the keywords ``Fangji Huangqi Decoction'', ``Fangji Huangqi Tang'', ``Stephania tetrandra'', ``Astragalus membranaceus'', ``Atractylodes macrocephala'', ``Glycyrrhiza uralensis'', ``Zingiber officinale'', ``Ziziphus jujuba'', ``chemical constituents'', ``pharmacology'', ``clinical study'', and ``quality marker''. Inclusion criteria were: original research articles or systematic reviews related to Fangji Huangqi Decoction or its core herbs; studies involving chemical analysis, pharmacology, pharmacokinetics, or clinical application; publications in Chinese or English with clear methods and results. We excluded reports with unclear formula composition, obvious duplication, or serious methodological problems.
For Q-Marker analysis, we also consulted the 2020 and 2025 editions of the \textit{Pharmacopoeia of the People's Republic of China} and key papers on Q-Marker theory and on classical formulas with established Q-Marker systems\cite{ref2,ref3,ref4}. Data extraction focused on: main chemical groups and representative compounds, pharmacological targets and pathways, clinical indications and outcomes, pharmacokinetic characteristics, and established analytical methods. These data were then integrated into the five-principle Q-Marker framework for Fangji Huangqi Decoction.
\section{Chemical basis of Fangji Huangqi Decoction}
The material basis of Fangji Huangqi Decoction is the carrier for its multiple pharmacological effects. Modern phytochemistry and analytical chemistry have identified the main active constituents of the key herbs in this formula.
Fangji (\textit{Stephania tetrandra} Radix) is the dried root of a Menispermaceae plant and is the core herb for dispelling wind-dampness and promoting diuresis in this prescription. Its main active constituents are bisbenzylisoquinoline alkaloids, such as tetrandrine (also known as Han-Fangji A) and fangchinoline (Han-Fangji B). These two alkaloids have similar structures and differ only at the C-7 substituent (a methoxy group in tetrandrine and a phenolic hydroxyl group in fangchinoline)\cite{ref3,ref4}. Fangji also contains cyclanoline (Han-Fangji C), dimethyl-tetrandrine\cite{ref5}, aporphine-type alkaloids, and small amounts of flavonoids\cite{ref6}. Together, these alkaloids form the main chemical basis for the ``dispelling wind and promoting urination'' effect of Fangji. It is important to strictly distinguish Menispermaceae Fangji (\textit{Stephania tetrandra}, usually called ``Fen Fangji'') from Aristolochiaceae ``Guang Fangji'' (\textit{Aristolochia fangchi}). The latter contains aristolochic acids with strong nephrotoxicity and has been banned from clinical use.
Huangqi (\textit{Astragali Radix}) is the dried root of \textit{Astragalus membranaceus} (Fisch.) Bge. or related species in the Fabaceae family. It is known as a ``superior Qi-tonifying herb'' and acts as the sovereign herb for strengthening Qi and consolidating the exterior in this formula. Its chemical composition is complex and includes saponins, flavonoids, polysaccharides, amino acids, and trace elements. Among the saponins, astragaloside IV is the most representative, and other astragalosides such as astragaloside I, II, III and related derivatives have also been reported. Most of these saponins are cycloartane-type triterpenoid saponins and are regarded as key substances for the immunomodulatory and organ-protective effects of Huangqi\cite{ref7}. The flavonoids are mainly isoflavones and their glycosides, including calycosin, calycosin-7-O-β-D-glucoside, formononetin, and ononin\cite{ref8,ref9}. Astragalus polysaccharides (APS) are abundant macromolecular components with complex monosaccharide composition and marked immune-enhancing activity\cite{ref10}.
Baizhu (\textit{Atractylodis Macrocephalae Rhizoma}) is the dried rhizome of \textit{Atractylodes macrocephala} Koidz. in the Asteraceae family. Its main active substances are sesquiterpene lactones, volatile oils, and polysaccharides. Atractylone is a characteristic volatile component of Baizhu, but it is easily oxidized or lost during storage and decoction. In contrast, sesquiterpene lactones such as atractylenolide I, atractylenolide II, and atractylenolide III are relatively stable and are considered characteristic constituents of Baizhu with significant anti-inflammatory activity\cite{ref11,ref12}. Polysaccharides from Baizhu also contribute to its immunomodulatory effects\cite{ref13}.
Gancao (\textit{Glycyrrhizae Radix et Rhizoma}) is the dried root and rhizome of \textit{Glycyrrhiza uralensis} Fisch. and related species in the Fabaceae family. Its main active constituents are triterpenoid saponins and flavonoids. Among the saponins, glycyrrhizic acid is the most important component. It usually exists in the form of ammonium salts and is metabolized in vivo to glycyrrhetinic acid, which has a corticosteroid-like anti-inflammatory effect\cite{ref14,ref15}. The main flavonoids include liquiritin, isoliquiritin, liquiritigenin, isoliquiritigenin, and various chalcone derivatives\cite{ref16}.
Fresh ginger (\textit{Zingiberis Rhizoma Recens}) is the fresh rhizome of \textit{Zingiber officinale} Roscoe. It is rich in pungent phenolic compounds (gingerols) and volatile oils. Gingerols are responsible for the pungent taste of ginger and mainly include 6-gingerol, 8-gingerol, and 10-gingerol. These compounds have anti-inflammatory, antiemetic, and circulation-promoting effects\cite{ref17}. During heating, gingerols can undergo dehydration to form shogaols, such as 6-shogaol. The volatile oil fraction contains sesquiterpenes such as zingiberene and β-sesquiphellandrene\cite{ref18}.
Dazao (\textit{Jujubae Fructus}) is the dried ripe fruit of \textit{Ziziphus jujuba} Mill. in the Rhamnaceae family. It contains a wide variety of components, among which cyclic adenosine monophosphate (cAMP) and polysaccharides have attracted much attention. The cAMP content in jujube is much higher than in most other plants, and cAMP is an important second messenger involved in intracellular signal transduction\cite{ref19}. In addition, jujube contains triterpenic acids (such as oleanolic acid and ursolic acid, mainly in the fruit peel)\cite{ref20}, flavonoids, and various amino acids. These components together support the traditional functions of jujube in tonifying the middle Jiao, nourishing the blood, and calming the mind.
\section{Pharmacological effects and mechanisms}
In recent years, a large number of basic studies have revealed that Fangji Huangqi Decoction exerts its actions through multiple mechanisms, including anti-inflammatory effects, cardiovascular protection, renal protection, antitumor activity, and regulation of glucose and lipid metabolism.
\subsection{Anti-inflammatory effects}
One of the key functions of Fangji Huangqi Decoction is to ``tonify Qi and consolidate the exterior'' so as to strengthen the body's defense, which reflects the TCM concept of ``supporting healthy Qi to dispel pathogenic factors''. Inflammation is a basic pathological process in rheumatic pain and many chronic diseases. Lin et al.\cite{ref21} reported that Fangji Huangqi Decoction has clear, dose-dependent analgesic and anti-inflammatory effects in animal models, which may be related to inhibition of peripheral nociceptive pathways, such as suppression of prostaglandin synthesis. In models of autoimmune diseases such as rheumatoid arthritis (RA) and immune-mediated kidney disease, the decoction shows significant anti-inflammatory and immunoregulatory effects. Its anti-inflammatory action mainly involves inhibition of key inflammatory signaling pathways. Studies have shown that the decoction significantly lowers the levels of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6. Mechanistically, it inhibits phosphorylation and degradation of IκBα in the NF-κB pathway, and reduces phosphorylation of p38 and JNK in the MAPK pathway, thereby suppressing the over-expression of inflammatory mediators at the transcriptional level\cite{ref22,ref23}. In inflammatory joint models, Fangji Huangqi Decoction can inhibit VEGF-driven angiogenesis in synovial tissues, reduce exudation of inflammatory substances, and improve joint swelling, redness, and deformity\cite{ref24}.
The formula also shows a bidirectional regulatory effect on the immune system. The combination of Huangqi and Baizhu (the core herb pair of Yupingfeng San) enhances non-specific immunity, for example by increasing macrophage phagocytic activity and natural killer (NK) cell function. In contrast, tetrandrine from Fangji suppresses excessive proliferation of activated T lymphocytes and reduces their cytokine production. When used together, these herbs strengthen the body's resistance to disease while preventing an excessive immune-inflammatory response, thus helping to maintain dynamic immune balance\cite{ref25,ref26}.
\subsection{Improvement of ventricular remodeling and myocardial fibrosis}
Myocardial fibrosis is a key pathological process in chronic heart failure (CHF). Experimental studies have shown that Fangji Huangqi Decoction can improve ventricular remodeling and myocardial fibrosis in animal models. Yang et al.\cite{ref27} found that medium and high doses of the decoction significantly improved myocardial histopathology in rats. The mechanism was associated with reduced levels of angiotensin II (Ang II), inhibition of p38MAPK phosphorylation, down-regulation of transforming growth factor-β1 (TGF-β1), and decreased synthesis of collagen I and collagen III. In the formula, tetrandrine acts as a natural calcium channel blocker. It can dilate coronary and peripheral blood vessels, lower blood pressure, and reduce cardiac afterload. Astragaloside IV has a positive inotropic effect, improves myocardial energy metabolism, and enhances contractile function \cite{ref28}. Cao et al.\cite{ref29} reported that quercetin, tetrandrine, astragaloside IV, and other components in the decoction can regulate pathways related to apoptosis and fatty acid metabolism. The decoction also attenuates norepinephrine-induced cardiomyocyte hypertrophy and reduces the mRNA expression of ANP and BNP, thereby improving ventricular remodeling.
\subsection{Antitumor effects}
Fangji Huangqi Decoction has shown potential as an adjuvant therapy for breast cancer. Liu et al.\cite{ref30} and Guo et al.\cite{ref31} demonstrated that the decoction inhibits proliferation and migration of triple-negative breast cancer MDA-MB-231 cells. Mechanistically, treatment with the decoction increases the expression of E-cadherin and reduces expression of mesenchymal markers such as vimentin, indicating inhibition of epithelial--mesenchymal transition (EMT). This contributes to suppression of tumor growth and angiogenesis. Further research has shown that tetrandrine, as an important active component in the formula, can activate the Hippo/YAP signaling pathway and induce apoptosis in breast cancer cells, which may be one of the main mechanisms underlying the antitumor effects of the decoction\cite{ref32}.
\subsection{Regulation of glucose and lipid metabolism}
For metabolic syndrome and obesity, Fangji Huangqi Decoction has both symptomatic and root-cause benefits in TCM terms. Jia et al.\cite{ref33} showed in a high-fat diet rat model that the decoction increases HDL cholesterol and reduces TG, total cholesterol, and free fatty acid levels, thereby improving insulin resistance. Chen et al.\cite{ref34} reported that the key mechanisms of the decoction in treating type 2 diabetes and metabolic disorders involve regulation of AMPK, AGE/RAGE, and FoxO signaling pathways, which promote energy metabolism and fatty acid oxidation. Experimental work also confirmed that Fangji Huangqi Decoction can inhibit T lymphocyte proliferation and lower levels of inflammatory cytokines \cite{ref35}. Using network pharmacology, molecular docking, and clinical studies, Jiang et al.\cite{ref36,ref37} further suggested that the decoction may exert its anti-obesity effects through multiple pathways including TNF, IL-17, apoptosis, p53, and HIF-1, thereby influencing inflammation, oxidative stress, and cell apoptosis.
\subsection{Renal protective effects}
Liu et al.\cite{ref38}, using network pharmacology combined with experimental validation, found that astragaloside IV and tetrandrine have a synergistic effect in protecting podocytes. They increase the expression of key slit diaphragm proteins Nephrin and Podocin and prevent their ubiquitin-mediated degradation, thus stabilizing podocyte structure, repairing the glomerular filtration barrier, and reducing protein leakage. Cao Guanghai et al.\cite{ref39} observed that the decoction lowers serum levels of retinol-binding protein (RBP) and reduces oxidative stress damage to the glomerular basement membrane. Song\cite{ref40} reported that Fangji Huangqi Decoction down-regulates the expression of Toll-like receptors TLR4 and TLR7, thereby inhibiting TLR-mediated renal micro-inflammation. Atractylenolides from Baizhu are important regulators of renal water handling. They modulate aquaporin expression, down-regulate AQP2 in renal tissue, and inhibit over-activation of the renin--angiotensin--aldosterone system (RAAS), leading to enhanced water and sodium excretion and a sustained diuretic effect\cite{ref41}. Tetrandrine, as a calcium channel blocker, dilates renal blood vessels and improves renal hemodynamics; it also inhibits mesangial cell proliferation by blocking calcium influx. Astragaloside IV reduces extracellular matrix deposition via the TGF-β1/Smad pathway, thereby slowing progression of tubulointerstitial fibrosis\cite{ref42,ref43}.
\section{Clinical research progress on Fangji Huangqi Decoction}
The clinical use of Fangji Huangqi Decoction reflects the TCM idea of ``treating different diseases with the same method''. It is mainly used in kidney diseases, cardiovascular diseases, rheumatic and joint diseases, and metabolic disorders.
\subsection{Kidney diseases}
Clinically, Fangji Huangqi Decoction is often used for primary nephrotic syndrome and chronic glomerulonephritis with patterns of spleen--kidney Qi deficiency and internal retention of water-dampness. Wang Tiesuo et al.\cite{ref44} used a modified Fangji Huangqi Decoction to treat refractory edema in nephrotic syndrome. The total effective rate in the treatment group reached 92.86\%, which was significantly higher than 71.43\% in the control group. The decoction reduced urinary protein and serum total cholesterol, increased serum albumin, and improved hypercoagulability. Meta-analyses and clinical observations\cite{ref45} suggest that combining Fangji Huangqi Decoction with standard Western treatments (such as glucocorticoids and immunosuppressants) can further reduce 24-hour urinary protein, raise plasma albumin, correct hypoproteinemia, and relieve edema. Its diuretic effect is mild and sustained, less likely to cause electrolyte imbalance, and it may help reduce adverse reactions caused by Western medicines\cite{ref46}. Chen Houbin et al.\cite{ref47} reported that, in patients with chronic nephritis or early hypertensive renal damage with Qi deficiency and dampness obstruction, modified Fangji Huangqi Decoction significantly improved edema and fatigue and helped protect renal function. Zhang Rui et al.\cite{ref48} found that the decoction improved glucose and lipid metabolism and reduced oxidative stress in patients with diabetic kidney disease, thus delaying disease progression.
\subsection{Rheumatic and joint diseases}
Fangji Huangqi Decoction is also widely used for rheumatoid arthritis and other rheumatic immune diseases, especially in patients who present with joint swelling, heavy limbs, and aversion to wind---symptoms that match the pattern of ``wind-damp obstruction with exterior deficiency and abundant dampness''. Clinical studies have shown that adding Fangji Huangqi Decoction to basic disease-modifying antirheumatic drugs such as methotrexate yields better results than Western medicine alone\cite{ref49}. Combination therapy more effectively reduces disease activity, as reflected by lower DAS28 scores, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP), and also improves joint swelling, morning stiffness, and physical function. In addition, Fangji Huangqi Decoction can to some extent reduce adverse reactions of methotrexate, such as elevated liver enzymes and gastrointestinal discomfort, showing a ``synergistic and toxicity-reducing'' benefit of TCM\cite{ref50}. Li Runmin et al.\cite{ref51} reported that Fangji Huangqi Decoction combined with Wuling San was effective in treating knee osteoarthritis, and Yang Gongxu\cite{ref52} successfully used the decoction in acute gouty arthritis. In both conditions, it showed good effects in reducing swelling and relieving pain.
\subsection{Cardiovascular diseases}
Experimental and clinical studies indicate that Fangji Huangqi Decoction can increase urine output, improve cardiac function classification (NYHA), and lower plasma BNP levels\cite{ref53}. Liu Jie et al.\cite{ref54} observed that, in patients with chronic heart failure of Qi deficiency, blood stasis, and phlegm-fluid retention type, treatment with Sijunzi Decoction plus Fangji Huangqi Decoction significantly improved NYHA class. Fang Xiaojiang et al.\cite{ref55} expanded these findings and showed that modified Fangji Huangqi Decoction is also effective in heart failure with preserved ejection fraction. It is especially suitable for congestive heart failure with lower limb edema.
\subsection{Metabolic diseases (obesity)}
From a TCM perspective, obesity is often regarded as ``deficiency in root and excess in manifestation'', mainly involving spleen deficiency with dampness and phlegm retention. Fangji Huangqi Decoction promotes weight loss by tonifying Qi, strengthening the spleen, promoting diuresis, and resolving phlegm. Its clinical efficacy is closely related to improvements in lipid profiles (TC, TG, LDL-C) and reduction of low-grade inflammation\cite{ref56}. Li Yajuan et al.\cite{ref57} reported that Fangji Huangqi Decoction combined with meridian-based abdominal massage achieved a total effective rate of 94.1\% in patients with spleen-deficiency and dampness-retention type simple obesity, with significant reductions in body weight, BMI, and waist circumference. Ling Qinliang et al.\cite{ref58} showed that combining the decoction with Qiwei Baizhu Powder modulated the gut microbiota in obese children (for example, promoting the growth of \textit{Bacteroides} species) and reduced body fat percentage.
\section{Systematic Prediction of Q-Markers for Fangji Huangqi Decoction Based on the Five Principles}
As described above, we combined chemical, pharmacological, and clinical information with the Q-Marker concept to screen potential quality markers for Fangji Huangqi Decoction. The overall analysis path is shown in Figure 1.
\begin{figure}[htbp]
\centering
\includegraphics[width=0.8\textwidth]{qmarker_analysis_path.png}
\caption{Prediction analysis path of Q-Markers for Fangji Huangqi Decoction. Note: The study starts from the six component herbs and their main chemical groups. Then it applies the five Q-Marker principles: Transmissibility and traceability: select components that can be traced from raw herbs through decoction to measurable forms in plasma or target tissues; Specificity: prioritize components with high species specificity that can distinguish genuine herbs from adulterants; Efficacy: select components with clear evidence supporting their contribution to key actions such as tonifying Qi, promoting diuresis, and reducing inflammation; Compatibility environment: consider the behavior of components in the multi-herb decoction, including solubility changes, interactions, and stability; Measurability: ensure that candidate markers are stable and can be accurately measured by standard analytical methods. Through this step-by-step screening, we finally determined a set of ten core Q-Markers.}
\label{fig:qmarker_path}
\end{figure}
\subsection{Prediction Based on Transmissibility and Traceability}
We used ``Fangji,'' ``Huangqi,'' ``Baizhu,'' ``Gancao,'' ``Shengjiang,'' and ``Dazao'' as keywords to search the TCMSP database (Traditional Chinese Medicine Systems Pharmacology Database, https://old.tcmsp-e.com/tcmsp.php). The database lists 50 components for Fangji, 87 for Huangqi, 55 for Baizhu, 280 for Gancao, 265 for fresh ginger, and 133 for jujube. Using oral bioavailability (OB) $\geq$ 30\% and drug-likeness (DL) $\geq$ 0.18 as screening criteria, we obtained 3 candidate components for Fangji, 20 for Huangqi, 7 for Baizhu, 92 for Gancao, 5 for fresh ginger, and 29 for jujube.
Understanding the absorption, distribution, metabolism, and excretion (ADME) of components in a formula is essential for identifying the real active substances and explaining the mechanisms of action. In previous work, UPLC--MS/MS and other techniques were used to measure plasma concentration--time curves of prototype components such as tetrandrine, astragaloside IV, and glycyrrhizic acid after oral administration of Fangji Huangqi Decoction in rats. The results showed that these compounds can be absorbed into the blood in prototype form, but they differ in time to peak concentration (Tmax), elimination half-life (t½), and bioavailability. This suggests that they may differ in onset time, intensity, and duration of action. These pharmacokinetic differences also reflect the multi-component, multi-target, and time-sequence synergistic features of the formula\cite{ref59}.
The liver is the main organ for drug metabolism. Many components in Fangji Huangqi Decoction are activated or inactivated by hepatic enzymes, such as the cytochrome P450 family. For example, glycyrrhizic acid is hydrolyzed by intestinal flora and liver enzymes to glycyrrhetinic acid, which has stronger activity. Analysing metabolic profiles in serum, urine, or tissues after administration and identifying characteristic metabolites is very important for understanding the overall effects of the formula at a system level\cite{ref60}.
\subsection{Prediction Based on Specificity}
Tetrandrine and fangchinoline are characteristic bisbenzylisoquinoline alkaloids of \textit{Stephania tetrandra} and are not present in \textit{Aristolochia} species. They are thus ideal for distinguishing true Fangji from toxic Guangfangji. Astragaloside IV and calycosin-7-O-β-D-glucoside are representative saponin and flavonoid markers of \textit{Astragali Radix}. Atractylenolide I/III distinguish Baizhu from Cangzhu. Glycyrrhizic acid and liquiritin are typical constituents of Gancao. 6-Gingerol and cAMP can serve as characteristic markers for fresh ginger and jujube, respectively. Together, these markers can trace the origin of all six herbs in the formula\cite{ref3,ref4,ref5,ref6,ref7,ref8,ref9,ref10,ref11,ref12,ref61,ref62,ref63,ref64,ref65,ref66,ref67,ref68,ref69,ref70,ref71,ref72}.
\subsection{Prediction Based on Efficacy}
Each selected marker is closely related to at least one key pharmacological action of the decoction: tetrandrine and fangchinoline: diuretic, anti-inflammatory, renoprotective; astragaloside IV and calycosin-7-O-β-D-glucoside: immunomodulatory, anti-fibrotic, cardioprotective; atractylenolide I/III: spleen-strengthening, anti-inflammatory; glycyrrhizic acid and liquiritin: anti-inflammatory, detoxifying, harmonizing; 6-gingerol and cAMP: promoting circulation, supporting energy metabolism, and modulating immunity\cite{ref73,ref74,ref75}.
\subsection{Prediction Based on the Compatibility Environment}
The clinical effectiveness of a classical formula depends on the interactions among its herbs. Fangji--Huangqi (chief--chief pairing): Studies suggest that astragalus polysaccharides can improve the solubility and absorption of alkaloids from Fangji. Astragaloside IV and tetrandrine also show clear synergistic effects in protecting podocytes and reducing kidney injury \cite{ref76,ref77}. Huangqi--Baizhu (chief--deputy pairing): This pair is also the core of the classical formula Yupingfeng San. When used together, they enhance the effects of tonifying Qi and strengthening the spleen. Atractylenolides and total astragalus saponins have synergistic effects on regulating immune function \cite{ref78}. Shengjiang--Dazao (assistant--envoy pairing): Ginger and jujube used together can reduce the side effects of the bitter-cold nature of Fangji on the stomach, improve spleen and stomach function, and help the absorption of other macromolecular components in the formula. These findings support the idea that the compatibility environment in the decoction should be considered when deciding whether a component can serve as a Q-Marker.
\subsection{Prediction Based on Measurability}
In addition to pharmacological relevance, candidate Q-Markers must be practical for routine quality control. The selected components---tetrandrine, fangchinoline, astragaloside IV, calycosin-7-O-β-D-glucoside, atractylenolide I, atractylenolide III, glycyrrhizic acid, liquiritin, and 6-gingerol---have all been recorded in the Chinese Pharmacopoeia or widely reported in the literature. Reliable HPLC-UV, HPLC-ELSD, or LC--MS methods have been established for their quantitative determination, and they show good stability under normal processing and storage conditions\cite{ref3}. Therefore, they are suitable as index components in industrial quality control of Fangji Huangqi Decoction and its preparations.
\subsection{Final Determination of Q-Markers}
By combining the five principles---transmissibility and traceability, specificity, efficacy, compatibility environment, and measurability---we propose the following compounds as Q-Markers for Fangji Huangqi Decoction:
Core markers: tetrandrine and fangchinoline (from Fangji, the chief herb, mainly responsible for promoting urination and reducing inflammation, and also important for safety control), and astragaloside IV (from Huangqi, the chief herb for tonifying Qi and protecting the kidney).
Characteristic markers: atractylenolide I and III (from Baizhu, the deputy herb, reflecting its spleen-tonifying and dampness-removing actions), and calycosin-7-O-β-D-glucoside (a characteristic flavonoid from Huangqi).
Auxiliary/harmonizing markers: glycyrrhizic acid and liquiritin (from Gancao, the assistant/envoy herb, with anti-inflammatory and detoxifying effects), 6-gingerol (a typical component of fresh ginger), and cAMP (a characteristic component of jujube).
Atractylodin was not selected because it is unstable, easily lost during decoction, and has low exposure in blood. Astragalus polysaccharides are important for immunoregulation, but as complex mixtures they are difficult to characterize and quantify accurately, so they are recommended as auxiliary indicators rather than main Q-Markers.
In summary, ten compounds are finally determined as core Q-Markers of Fangji Huangqi Decoction. Their detailed information is shown in Tables 1 and 2.
\begin{tmrtable}{Potential Quality Markers of Fangji Huangqi Decoction}{tab:qmarkers}{X[l]X[l]X[l]X[l]}{1}{
No. & Name & Molecular formula & CAS number & Source herb \\
1 & Tetrandrine & C$_{38}$H$_{42}$N$_{2}$O$_{6}$ & 518-34-3 & Fangji \\
2 & Fangchinoline & C$_{37}$H$_{40}$N$_{2}$O$_{6}$ & 436-77-1 & Fangji \\
3 & Astragaloside IV & C$_{41}$H$_{68}$O$_{14}$ & 84687-43-4 & Huangqi \\
4 & Calycosin-7-O-β-D-glucoside & C$_{22}$H$_{22}$O$_{10}$ & 20633-67-4 & Huangqi \\
5 & Atractylenolide I & C$_{15}$H$_{18}$O$_{2}$ & 73069-13-3 & Baizhu \\
6 & Atractylenolide III & C$_{15}$H$_{20}$O$_{3}$ & 73030-71-4 & Baizhu \\
7 & Glycyrrhizic acid & C$_{42}$H$_{62}$O$_{16}$ & 1405-86-3 & Gancao \\
8 & Liquiritin & C$_{21}$H$_{22}$O$_{9}$ & 551-15-5 & Gancao \\
9 & 6-Gingerol & C$_{17}$H$_{26}$O$_{4}$ & 23513-14-6 & Fresh ginger \\
10 & cAMP & C$_{10}$H$_{12}$N$_{5}$O$_{6}$P & 60-92-4 & Jujube
}{Note: All compounds meet the five Q-Marker principles for Fangji Huangqi Decoction.}
\begin{tmrtable}{Predictive Analysis of Potential Quality Markers (Q-Marker) for Fangji Huangqi Decoction}{tab:qmarker_analysis}{X[l]X[l]X[l]X[l]X[l]}{1}{
Component & Source & Basis for transmissibility and traceability & Basis for specificity & Basis for efficacy & Role in the formula & Analytical method \\
Tetrandrine & Fangji & Rapid oral absorption, high plasma level, main absorbed alkaloid & Characteristic bisbenzylisoquinoline alkaloid of \textit{Stephania tetrandra}; helps distinguish from aristolochic-acid-containing adulterants & Calcium-channel blocking, diuretic, anti-inflammatory, immunoregulatory & Chief herb; shows synergy with Huangqi & HPLC-UV; listed in Pharmacopoeia \\
Fangchinoline & Fangji & Similar pharmacokinetics to tetrandrine; relatively high bioavailability & Coexists with tetrandrine; structurally similar and chemically specific & Diuretic and anti-inflammatory synergy & Part of chief-herb components & HPLC-UV; listed in Pharmacopoeia \\
Astragaloside IV & Huangqi & Bioavailability improved in the compound environment; detectable in plasma & Specific triterpenoid saponin of \textit{Astragalus} genus; strong species specificity & Immuno-enhancing, anti-fibrotic, cardio-protective & Chief herb; key compound for tonifying Qi and strengthening the exterio & HPLC-ELSD/UV; included in Pharmacopoeia \\
Calycosin-7-O-β-D-glucoside & Huangqi & Main blood-absorbed flavonoid; pharmacokinetics clearly defined & Representative and stable isoflavone component of Huangqi & Antioxidant, anti-inflammatory, immuno-regulatory & Auxiliary compound of the chief herb & HPLC-UV; validated in literature \\
Atractylenolide I & Baizhu & Detectable in plasma; moderate half-life & Characteristic sesquiterpene lactone distinguishing Baizhu from Cangzhu & Anti-inflammatory, regulatory to gastrointestinal function & Minister herb; basis for spleen-strengthening and damp-drying actions & HPLC-UV; validated in literature \\
Atractylenolide III & Baizhu & Pharmacokinetics similar to atractylenolide I; good stability & Co-exists with atractylenolide I as a characteristic marker & Anti-inflammatory, immuno-modulatory & Part of minister-herb component & HPLC-UV; listed in Pharmacopoeia \\
Glycyrrhizic acid & Gancao & Metabolized in vivo to glycyrrhetinic acid; clear systemic exposure & Characteristic triterpenoid saponin of \textit{Glycyrrhiza} species; high abundance & Anti-inflammatory, detoxifying, harmonizing other herbs & Envoy herb, also serves an assistant function & HPLC-UV; listed in Pharmacopoeia \\
Liquiritin & Gancao & Absorbed and metabolized to liquiritigenin; measurable in plasma & Major flavone glycoside of Gancao & Anti-inflammatory, antioxidant, mucosal protective & Assistant compound of envoy herb & HPLC-UV; listed in Pharmacopoeia \\
6-Gingerol & Fresh ginger & Good oral absorption; reaches effective plasma concentrations & Characteristic pungent component of fresh ginger; distinguishes it from dried ginger & Anti-inflammatory, circulation-promoting, anti-emetic & Assistant herb; harmonizes ying and wei Qi & HPLC-UV; standardized method available \\
cAMP & Jujube & Absorbed into blood; participates in cellular signal transduction & Remarkably high content in jujube compared with other plants & Regulates immune response, energy metabolism, and anti-fatigue activity & Envoy herb; supports Qi-tonifying and blood-nourishing actions & HPLC-UV / LC-MS
}{Note: All components fulfill the five Q-Marker principles and are suitable for quality control of Fangji Huangqi Decoction.}
\section{Discussion}
Our review confirms that Fangji Huangqi Decoction has a clear material basis and a wide range of pharmacological activities that support its traditional indications. The main active groups include alkaloids from Fangji, saponins and flavonoids from Huangqi and Gancao, sesquiterpene lactones from Baizhu, and gingerols and cAMP from ginger and jujube. By applying the five-principle Q-Marker framework, we further narrowed down the candidate components to ten core compounds: tetrandrine, fangchinoline, astragaloside IV, calycosin-7-O-β-D-glucoside, atractylenolide I, atractylenolide III, glycyrrhizic acid, liquiritin, 6-gingerol, and cAMP.
These components fulfill the conditions of being: traceable from raw herbs to decoction and into the body; specific for their respective herbs and useful in distinguishing genuine materials; clearly linked to important pharmacological effects such as diuresis, immunomodulation, anti-inflammation, and metabolic regulation; stable and meaningful in the multi-herb compatibility environment; and measurable using standard HPLC or LC--MS methods. This set of proposed Q-Markers covers all six herbs and reflects the ``chief--deputy--assistant--envoy'' structure of the formula. It is more comprehensive than traditional standards that focus on only one or two marker compounds, and better connects the material basis with both efficacy and safety.
However, some issues require further study. For example, 6-gingerol can be converted to 6-shogaol during decoction, and it is still unclear whether the prototype, the metabolite, or the sum of both should be used as the quality indicator. The cAMP content of jujube is influenced by origin, harvest time, and storage conditions, and still needs large-scale data to define reasonable limits. In addition, most of the evidence used in our analysis comes from separate studies on single herbs or small combinations, and direct spectrum--effect and serum pharmacochemistry data for the whole decoction remain limited.
\section{Conclusions}
Fangji Huangqi Decoction is a time-tested classical prescription that has been successfully applied to a variety of kidney, cardiovascular, rheumatic, and metabolic diseases. Modern pharmacology and clinical studies support its traditional use and indicate multi-target mechanisms. Through a systematic review and Q-Marker-based analysis, we propose ten core Q-Markers for Fangji Huangqi Decoction: tetrandrine, fangchinoline, astragaloside IV, calycosin-7-O-β-D-glucoside, atractylenolide I, atractylenolide III, glycyrrhizic acid, liquiritin, 6-gingerol, and cAMP. This set of markers can serve as a foundation for developing multi-component quality standards and for guiding future research on the formula's pharmacodynamics and clinical optimization.
Future work should include: (1) establishing UPLC--MS/MS methods for simultaneous determination of these markers in raw herbs, decoctions, and preparations; (2) building chromatographic fingerprint--efficacy correlation models in relevant disease models; and (3) validating the relationship between Q-Marker levels and clinical outcomes in well-designed clinical trials.
\begin{acknowledgement}
No specific funding was received for this work.
\end{acknowledgement}
\section*{Author Contributions}
Conceptualization: Gaofei Yan, Huiying Li. Literature search and data collection: Gaofei Yan, Peng Yu. Data analysis and interpretation: Xianzhong Bu, Zhengguo Wang. Drafting of the manuscript: Gaofei Yan. Critical revision of the manuscript: Huiying Li. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.
\section*{Abbreviations and Terminology}
ADME -- Absorption, Distribution, Metabolism and Excretion; pharmacokinetic processes determining the in vivo fate of a compound. \\
AGE/RAGE -- Advanced Glycation End products and their Receptor; signaling axis implicated in inflammation, oxidative stress and diabetic complications. \\
AMA -- American Medical Association; citation and reference style used in this manuscript. \\
AMPK -- AMP-activated Protein Kinase; a central regulator of cellular energy metabolism. \\
ANP -- Atrial Natriuretic Peptide; cardiac hormone and biomarker related to cardiac load. \\
AQP -- Aquaporin; family of water channel proteins (e.g., AQP1--4) involved in renal water handling. \\
BNP -- Brain Natriuretic Peptide; biomarker of heart failure and ventricular stress. \\
cAMP -- Cyclic Adenosine Monophosphate; intracellular second messenger found at high levels in jujube. \\
CHF -- Chronic Heart Failure. \\
CKD -- Chronic Kidney Disease. \\
cAMP -- Cyclic Adenosine Monophosphate; second messenger involved in multiple signaling pathways, relatively abundant in jujube. \\
CRP -- C-reactive Protein; clinical marker of systemic inflammation. \\
DAS28 -- Disease Activity Score in 28 joints; composite index to assess rheumatoid arthritis activity. \\
DBD / DKD -- Diabetic Kidney Disease. \\
EMT -- Epithelial--Mesenchymal Transition; process linked to tumor invasion and metastasis, and tissue fibrosis. \\
ETM / EMT -- Epithelial--Mesenchymal Transition (same as above). \\
FoxO -- Forkhead box O; transcription factor family involved in oxidative stress, apoptosis and metabolism. \\
HIF-1 -- Hypoxia-Inducible Factor-1; transcription factor responding to hypoxic stress. \\
HGWD -- Huangqi Guizhi Wuwu Decoction; a classical formula used in neuropathic pain and circulatory disorders. \\
HPLC -- High-Performance Liquid Chromatography. \\
HPLC-MS/MS / LC-MS/MS -- Liquid Chromatography--Tandem Mass Spectrometry. \\
IgAN -- IgA Nephropathy. \\
IL-1β, IL-6, IL-17 -- Interleukin-1 beta, Interleukin-6, Interleukin-17; pro-inflammatory cytokines. \\
MAPK -- Mitogen-Activated Protein Kinase; signaling pathway involved in inflammation, proliferation and stress responses. \\
MTX -- Methotrexate; disease-modifying antirheumatic drug (DMARD). \\
NF-κB -- Nuclear Factor kappa B; transcription factor that regulates inflammatory and immune responses. \\
NK cells -- Natural Killer cells; innate immune effector cells. \\
NS -- Nephrotic Syndrome. \\
NYHA -- New York Heart Association functional classification for heart failure. \\
OA -- Osteoarthritis. \\
PPARγ -- Peroxisome Proliferator-Activated Receptor gamma; nuclear receptor involved in lipid metabolism and inflammation. \\
RA -- Rheumatoid Arthritis. \\
RAAS -- Renin--Angiotensin--Aldosterone System; hormonal system regulating blood pressure and fluid balance. \\
RBP -- Retinol-Binding Protein; can be used as a marker of tubular injury and renal oxidative damage. \\
RTK-PKCα -- Receptor Tyrosine Kinase--Protein Kinase C alpha signaling axis. \\
TC -- Total Cholesterol. \\
TCM -- Traditional Chinese Medicine. \\
TG -- Triglycerides. \\
Th1/Th2 -- T helper 1 / T helper 2 lymphocyte subsets; reflect cellular vs humoral immune responses. \\
TGF-β1 -- Transforming Growth Factor-beta1; key profibrotic cytokine. \\
TLR4, TLR7 -- Toll-Like Receptor 4 and 7; pattern recognition receptors involved in innate immunity and renal micro-inflammation. \\
TNF-α -- Tumor Necrosis Factor-alpha; major pro-inflammatory cytokine. \\
UHPLC / UPLC -- Ultra-High-Performance Liquid Chromatography. \\
UPLC-Q-TOF-MS / UPLC-QTOF-MS -- Ultra-Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry. \\
VEGF -- Vascular Endothelial Growth Factor; key mediator of angiogenesis. \\
WGCNA -- Weighted Gene Co-expression Network Analysis; bioinformatics method to analyze gene expression modules. \\
YAP -- Yes-Associated Protein; key effector in the Hippo signaling pathway. \\
TCM-specific terms (for clarity to international readers) \\
Fangji Huangqi Decoction (Fangji Huangqi Tang) -- Classical TCM formula composed of \textit{Stephania tetrandra} (Fangji), \textit{Astragalus membranaceus} (Huangqi), \textit{Atractylodes macrocephala} (Baizhu), \textit{Glycyrrhiza uralensis} (Gancao), fresh ginger and jujube; used to treat ``wind--water'' patterns with Qi deficiency and dampness retention. \\
Qi -- Vital energy or functional activity in TCM theory. \\
Ying and Wei -- Nutritive (Ying) Qi and Defensive (Wei) Qi; concepts describing internal nourishment and external defense in TCM. \\
Spleen deficiency with dampness retention -- TCM pattern characterized by impaired transformation and transportation of fluids, leading to edema, heaviness and obesity. \\
Wind--damp Bi syndrome -- TCM pattern of painful obstruction with joint pain, swelling and heaviness due to wind, cold and dampness invading the channels.
\section*{Conflicts of Interest}
The authors declare that they have no conflicts of interest related to this work.
\printbibliography
\end{document}

BIN
file_68fd3d0c5df72.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 208 KiB

BIN
file_68fd3d0c5df73.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 76 KiB

BIN
git.ico Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 53 KiB

15
hooks/applypatch-msg.sample Normal file
View File

@@ -0,0 +1,15 @@
#!/bin/sh
#
# An example hook script to check the commit log message taken by
# applypatch from an e-mail message.
#
# The hook should exit with non-zero status after issuing an
# appropriate message if it wants to stop the commit. The hook is
# allowed to edit the commit message file.
#
# To enable this hook, rename this file to "applypatch-msg".
. git-sh-setup
commitmsg="$(git rev-parse --git-path hooks/commit-msg)"
test -x "$commitmsg" && exec "$commitmsg" ${1+"$@"}
:

24
hooks/commit-msg.sample Normal file
View File

@@ -0,0 +1,24 @@
#!/bin/sh
#
# An example hook script to check the commit log message.
# Called by "git commit" with one argument, the name of the file
# that has the commit message. The hook should exit with non-zero
# status after issuing an appropriate message if it wants to stop the
# commit. The hook is allowed to edit the commit message file.
#
# To enable this hook, rename this file to "commit-msg".
# Uncomment the below to add a Signed-off-by line to the message.
# Doing this in a hook is a bad idea in general, but the prepare-commit-msg
# hook is more suited to it.
#
# SOB=$(git var GIT_AUTHOR_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p')
# grep -qs "^$SOB" "$1" || echo "$SOB" >> "$1"
# This example catches duplicate Signed-off-by lines.
test "" = "$(grep '^Signed-off-by: ' "$1" |
sort | uniq -c | sed -e '/^[ ]*1[ ]/d')" || {
echo >&2 Duplicate Signed-off-by lines.
exit 1
}

174
hooks/fsmonitor-watchman.sample Normal file
View File

@@ -0,0 +1,174 @@
#!/usr/bin/perl
use strict;
use warnings;
use IPC::Open2;
# An example hook script to integrate Watchman
# (https://facebook.github.io/watchman/) with git to speed up detecting
# new and modified files.
#
# The hook is passed a version (currently 2) and last update token
# formatted as a string and outputs to stdout a new update token and
# all files that have been modified since the update token. Paths must
# be relative to the root of the working tree and separated by a single NUL.
#
# To enable this hook, rename this file to "query-watchman" and set
# 'git config core.fsmonitor .git/hooks/query-watchman'
#
my ($version, $last_update_token) = @ARGV;
# Uncomment for debugging
# print STDERR "$0 $version $last_update_token\n";
# Check the hook interface version
if ($version ne 2) {
die "Unsupported query-fsmonitor hook version '$version'.\n" .
"Falling back to scanning...\n";
}
my $git_work_tree = get_working_dir();
my $retry = 1;
my $json_pkg;
eval {
require JSON::XS;
$json_pkg = "JSON::XS";
1;
} or do {
require JSON::PP;
$json_pkg = "JSON::PP";
};
launch_watchman();
sub launch_watchman {
my $o = watchman_query();
if (is_work_tree_watched($o)) {
output_result($o->{clock}, @{$o->{files}});
}
}
sub output_result {
my ($clockid, @files) = @_;
# Uncomment for debugging watchman output
# open (my $fh, ">", ".git/watchman-output.out");
# binmode $fh, ":utf8";
# print $fh "$clockid\n@files\n";
# close $fh;
binmode STDOUT, ":utf8";
print $clockid;
print "\0";
local $, = "\0";
print @files;
}
sub watchman_clock {
my $response = qx/watchman clock "$git_work_tree"/;
die "Failed to get clock id on '$git_work_tree'.\n" .
"Falling back to scanning...\n" if $? != 0;
return $json_pkg->new->utf8->decode($response);
}
sub watchman_query {
my $pid = open2(\*CHLD_OUT, \*CHLD_IN, 'watchman -j --no-pretty')
or die "open2() failed: $!\n" .
"Falling back to scanning...\n";
# In the query expression below we're asking for names of files that
# changed since $last_update_token but not from the .git folder.
#
# To accomplish this, we're using the "since" generator to use the
# recency index to select candidate nodes and "fields" to limit the
# output to file names only. Then we're using the "expression" term to
# further constrain the results.
my $last_update_line = "";
if (substr($last_update_token, 0, 1) eq "c") {
$last_update_token = "\"$last_update_token\"";
$last_update_line = qq[\n"since": $last_update_token,];
}
my $query = <<" END";
["query", "$git_work_tree", {$last_update_line
"fields": ["name"],
"expression": ["not", ["dirname", ".git"]]
}]
END
# Uncomment for debugging the watchman query
# open (my $fh, ">", ".git/watchman-query.json");
# print $fh $query;
# close $fh;
print CHLD_IN $query;
close CHLD_IN;
my $response = do {local $/; <CHLD_OUT>};
# Uncomment for debugging the watch response
# open ($fh, ">", ".git/watchman-response.json");
# print $fh $response;
# close $fh;
die "Watchman: command returned no output.\n" .
"Falling back to scanning...\n" if $response eq "";
die "Watchman: command returned invalid output: $response\n" .
"Falling back to scanning...\n" unless $response =~ /^\{/;
return $json_pkg->new->utf8->decode($response);
}
sub is_work_tree_watched {
my ($output) = @_;
my $error = $output->{error};
if ($retry > 0 and $error and $error =~ m/unable to resolve root .* directory (.*) is not watched/) {
$retry--;
my $response = qx/watchman watch "$git_work_tree"/;
die "Failed to make watchman watch '$git_work_tree'.\n" .
"Falling back to scanning...\n" if $? != 0;
$output = $json_pkg->new->utf8->decode($response);
$error = $output->{error};
die "Watchman: $error.\n" .
"Falling back to scanning...\n" if $error;
# Uncomment for debugging watchman output
# open (my $fh, ">", ".git/watchman-output.out");
# close $fh;
# Watchman will always return all files on the first query so
# return the fast "everything is dirty" flag to git and do the
# Watchman query just to get it over with now so we won't pay
# the cost in git to look up each individual file.
my $o = watchman_clock();
$error = $output->{error};
die "Watchman: $error.\n" .
"Falling back to scanning...\n" if $error;
output_result($o->{clock}, ("/"));
$last_update_token = $o->{clock};
eval { launch_watchman() };
return 0;
}
die "Watchman: $error.\n" .
"Falling back to scanning...\n" if $error;
return 1;
}
sub get_working_dir {
my $working_dir;
if ($^O =~ 'msys' || $^O =~ 'cygwin') {
$working_dir = Win32::GetCwd();
$working_dir =~ tr/\\/\//;
} else {
require Cwd;
$working_dir = Cwd::cwd();
}
return $working_dir;
}

8
hooks/post-update.sample Normal file
View File

@@ -0,0 +1,8 @@
#!/bin/sh
#
# An example hook script to prepare a packed repository for use over
# dumb transports.
#
# To enable this hook, rename this file to "post-update".
exec git update-server-info

14
hooks/pre-applypatch.sample Normal file
View File

@@ -0,0 +1,14 @@
#!/bin/sh
#
# An example hook script to verify what is about to be committed
# by applypatch from an e-mail message.
#
# The hook should exit with non-zero status after issuing an
# appropriate message if it wants to stop the commit.
#
# To enable this hook, rename this file to "pre-applypatch".
. git-sh-setup
precommit="$(git rev-parse --git-path hooks/pre-commit)"
test -x "$precommit" && exec "$precommit" ${1+"$@"}
:

49
hooks/pre-commit.sample Normal file
View File

@@ -0,0 +1,49 @@
#!/bin/sh
#
# An example hook script to verify what is about to be committed.
# Called by "git commit" with no arguments. The hook should
# exit with non-zero status after issuing an appropriate message if
# it wants to stop the commit.
#
# To enable this hook, rename this file to "pre-commit".
if git rev-parse --verify HEAD >/dev/null 2>&1
then
against=HEAD
else
# Initial commit: diff against an empty tree object
against=$(git hash-object -t tree /dev/null)
fi
# If you want to allow non-ASCII filenames set this variable to true.
allownonascii=$(git config --type=bool hooks.allownonascii)
# Redirect output to stderr.
exec 1>&2
# Cross platform projects tend to avoid non-ASCII filenames; prevent
# them from being added to the repository. We exploit the fact that the
# printable range starts at the space character and ends with tilde.
if [ "$allownonascii" != "true" ] &&
# Note that the use of brackets around a tr range is ok here, (it's
# even required, for portability to Solaris 10's /usr/bin/tr), since
# the square bracket bytes happen to fall in the designated range.
test $(git diff-index --cached --name-only --diff-filter=A -z $against |
LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0
then
cat <<\EOF
Error: Attempt to add a non-ASCII file name.
This can cause problems if you want to work with people on other platforms.
To be portable it is advisable to rename the file.
If you know what you are doing you can disable this check using:
git config hooks.allownonascii true
EOF
exit 1
fi
# If there are whitespace errors, print the offending file names and fail.
exec git diff-index --check --cached $against --

13
hooks/pre-merge-commit.sample Normal file
View File

@@ -0,0 +1,13 @@
#!/bin/sh
#
# An example hook script to verify what is about to be committed.
# Called by "git merge" with no arguments. The hook should
# exit with non-zero status after issuing an appropriate message to
# stderr if it wants to stop the merge commit.
#
# To enable this hook, rename this file to "pre-merge-commit".
. git-sh-setup
test -x "$GIT_DIR/hooks/pre-commit" &&
exec "$GIT_DIR/hooks/pre-commit"
:

53
hooks/pre-push.sample Normal file
View File

@@ -0,0 +1,53 @@
#!/bin/sh
# An example hook script to verify what is about to be pushed. Called by "git
# push" after it has checked the remote status, but before anything has been
# pushed. If this script exits with a non-zero status nothing will be pushed.
#
# This hook is called with the following parameters:
#
# $1 -- Name of the remote to which the push is being done
# $2 -- URL to which the push is being done
#
# If pushing without using a named remote those arguments will be equal.
#
# Information about the commits which are being pushed is supplied as lines to
# the standard input in the form:
#
# <local ref> <local oid> <remote ref> <remote oid>
#
# This sample shows how to prevent push of commits where the log message starts
# with "WIP" (work in progress).
remote="$1"
url="$2"
zero=$(git hash-object --stdin </dev/null | tr '[0-9a-f]' '0')
while read local_ref local_oid remote_ref remote_oid
do
if test "$local_oid" = "$zero"
then
# Handle delete
:
else
if test "$remote_oid" = "$zero"
then
# New branch, examine all commits
range="$local_oid"
else
# Update to existing branch, examine new commits
range="$remote_oid..$local_oid"
fi
# Check for WIP commit
commit=$(git rev-list -n 1 --grep '^WIP' "$range")
if test -n "$commit"
then
echo >&2 "Found WIP commit in $local_ref, not pushing"
exit 1
fi
fi
done
exit 0

169
hooks/pre-rebase.sample Normal file
View File

@@ -0,0 +1,169 @@
#!/bin/sh
#
# Copyright (c) 2006, 2008 Junio C Hamano
#
# The "pre-rebase" hook is run just before "git rebase" starts doing
# its job, and can prevent the command from running by exiting with
# non-zero status.
#
# The hook is called with the following parameters:
#
# $1 -- the upstream the series was forked from.
# $2 -- the branch being rebased (or empty when rebasing the current branch).
#
# This sample shows how to prevent topic branches that are already
# merged to 'next' branch from getting rebased, because allowing it
# would result in rebasing already published history.
publish=next
basebranch="$1"
if test "$#" = 2
then
topic="refs/heads/$2"
else
topic=`git symbolic-ref HEAD` ||
exit 0 ;# we do not interrupt rebasing detached HEAD
fi
case "$topic" in
refs/heads/??/*)
;;
*)
exit 0 ;# we do not interrupt others.
;;
esac
# Now we are dealing with a topic branch being rebased
# on top of master. Is it OK to rebase it?
# Does the topic really exist?
git show-ref -q "$topic" || {
echo >&2 "No such branch $topic"
exit 1
}
# Is topic fully merged to master?
not_in_master=`git rev-list --pretty=oneline ^master "$topic"`
if test -z "$not_in_master"
then
echo >&2 "$topic is fully merged to master; better remove it."
exit 1 ;# we could allow it, but there is no point.
fi
# Is topic ever merged to next? If so you should not be rebasing it.
only_next_1=`git rev-list ^master "^$topic" ${publish} | sort`
only_next_2=`git rev-list ^master ${publish} | sort`
if test "$only_next_1" = "$only_next_2"
then
not_in_topic=`git rev-list "^$topic" master`
if test -z "$not_in_topic"
then
echo >&2 "$topic is already up to date with master"
exit 1 ;# we could allow it, but there is no point.
else
exit 0
fi
else
not_in_next=`git rev-list --pretty=oneline ^${publish} "$topic"`
/usr/bin/perl -e '
my $topic = $ARGV[0];
my $msg = "* $topic has commits already merged to public branch:\n";
my (%not_in_next) = map {
/^([0-9a-f]+) /;
($1 => 1);
} split(/\n/, $ARGV[1]);
for my $elem (map {
/^([0-9a-f]+) (.*)$/;
[$1 => $2];
} split(/\n/, $ARGV[2])) {
if (!exists $not_in_next{$elem->[0]}) {
if ($msg) {
print STDERR $msg;
undef $msg;
}
print STDERR " $elem->[1]\n";
}
}
' "$topic" "$not_in_next" "$not_in_master"
exit 1
fi
<<\DOC_END
This sample hook safeguards topic branches that have been
published from being rewound.
The workflow assumed here is:
* Once a topic branch forks from "master", "master" is never
merged into it again (either directly or indirectly).
* Once a topic branch is fully cooked and merged into "master",
it is deleted. If you need to build on top of it to correct
earlier mistakes, a new topic branch is created by forking at
the tip of the "master". This is not strictly necessary, but
it makes it easier to keep your history simple.
* Whenever you need to test or publish your changes to topic
branches, merge them into "next" branch.
The script, being an example, hardcodes the publish branch name
to be "next", but it is trivial to make it configurable via
$GIT_DIR/config mechanism.
With this workflow, you would want to know:
(1) ... if a topic branch has ever been merged to "next". Young
topic branches can have stupid mistakes you would rather
clean up before publishing, and things that have not been
merged into other branches can be easily rebased without
affecting other people. But once it is published, you would
not want to rewind it.
(2) ... if a topic branch has been fully merged to "master".
Then you can delete it. More importantly, you should not
build on top of it -- other people may already want to
change things related to the topic as patches against your
"master", so if you need further changes, it is better to
fork the topic (perhaps with the same name) afresh from the
tip of "master".
Let's look at this example:
o---o---o---o---o---o---o---o---o---o "next"
/ / / /
/ a---a---b A / /
/ / / /
/ / c---c---c---c B /
/ / / \ /
/ / / b---b C \ /
/ / / / \ /
---o---o---o---o---o---o---o---o---o---o---o "master"
A, B and C are topic branches.
* A has one fix since it was merged up to "next".
* B has finished. It has been fully merged up to "master" and "next",
and is ready to be deleted.
* C has not merged to "next" at all.
We would want to allow C to be rebased, refuse A, and encourage
B to be deleted.
To compute (1):
git rev-list ^master ^topic next
git rev-list ^master next
if these match, topic has not merged in next at all.
To compute (2):
git rev-list master..topic
if this is empty, it is fully merged to "master".
DOC_END

24
hooks/pre-receive.sample Normal file
View File

@@ -0,0 +1,24 @@
#!/bin/sh
#
# An example hook script to make use of push options.
# The example simply echoes all push options that start with 'echoback='
# and rejects all pushes when the "reject" push option is used.
#
# To enable this hook, rename this file to "pre-receive".
if test -n "$GIT_PUSH_OPTION_COUNT"
then
i=0
while test "$i" -lt "$GIT_PUSH_OPTION_COUNT"
do
eval "value=\$GIT_PUSH_OPTION_$i"
case "$value" in
echoback=*)
echo "echo from the pre-receive-hook: ${value#*=}" >&2
;;
reject)
exit 1
esac
i=$((i + 1))
done
fi

42
hooks/prepare-commit-msg.sample Normal file
View File

@@ -0,0 +1,42 @@
#!/bin/sh
#
# An example hook script to prepare the commit log message.
# Called by "git commit" with the name of the file that has the
# commit message, followed by the description of the commit
# message's source. The hook's purpose is to edit the commit
# message file. If the hook fails with a non-zero status,
# the commit is aborted.
#
# To enable this hook, rename this file to "prepare-commit-msg".
# This hook includes three examples. The first one removes the
# "# Please enter the commit message..." help message.
#
# The second includes the output of "git diff --name-status -r"
# into the message, just before the "git status" output. It is
# commented because it doesn't cope with --amend or with squashed
# commits.
#
# The third example adds a Signed-off-by line to the message, that can
# still be edited. This is rarely a good idea.
COMMIT_MSG_FILE=$1
COMMIT_SOURCE=$2
SHA1=$3
/usr/bin/perl -i.bak -ne 'print unless(m/^. Please enter the commit message/..m/^#$/)' "$COMMIT_MSG_FILE"
# case "$COMMIT_SOURCE,$SHA1" in
# ,|template,)
# /usr/bin/perl -i.bak -pe '
# print "\n" . `git diff --cached --name-status -r`
# if /^#/ && $first++ == 0' "$COMMIT_MSG_FILE" ;;
# *) ;;
# esac
# SOB=$(git var GIT_COMMITTER_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p')
# git interpret-trailers --in-place --trailer "$SOB" "$COMMIT_MSG_FILE"
# if test -z "$COMMIT_SOURCE"
# then
# /usr/bin/perl -i.bak -pe 'print "\n" if !$first_line++' "$COMMIT_MSG_FILE"
# fi

78
hooks/push-to-checkout.sample Normal file
View File

@@ -0,0 +1,78 @@
#!/bin/sh
# An example hook script to update a checked-out tree on a git push.
#
# This hook is invoked by git-receive-pack(1) when it reacts to git
# push and updates reference(s) in its repository, and when the push
# tries to update the branch that is currently checked out and the
# receive.denyCurrentBranch configuration variable is set to
# updateInstead.
#
# By default, such a push is refused if the working tree and the index
# of the remote repository has any difference from the currently
# checked out commit; when both the working tree and the index match
# the current commit, they are updated to match the newly pushed tip
# of the branch. This hook is to be used to override the default
# behaviour; however the code below reimplements the default behaviour
# as a starting point for convenient modification.
#
# The hook receives the commit with which the tip of the current
# branch is going to be updated:
commit=$1
# It can exit with a non-zero status to refuse the push (when it does
# so, it must not modify the index or the working tree).
die () {
echo >&2 "$*"
exit 1
}
# Or it can make any necessary changes to the working tree and to the
# index to bring them to the desired state when the tip of the current
# branch is updated to the new commit, and exit with a zero status.
#
# For example, the hook can simply run git read-tree -u -m HEAD "$1"
# in order to emulate git fetch that is run in the reverse direction
# with git push, as the two-tree form of git read-tree -u -m is
# essentially the same as git switch or git checkout that switches
# branches while keeping the local changes in the working tree that do
# not interfere with the difference between the branches.
# The below is a more-or-less exact translation to shell of the C code
# for the default behaviour for git's push-to-checkout hook defined in
# the push_to_deploy() function in builtin/receive-pack.c.
#
# Note that the hook will be executed from the repository directory,
# not from the working tree, so if you want to perform operations on
# the working tree, you will have to adapt your code accordingly, e.g.
# by adding "cd .." or using relative paths.
if ! git update-index -q --ignore-submodules --refresh
then
die "Up-to-date check failed"
fi
if ! git diff-files --quiet --ignore-submodules --
then
die "Working directory has unstaged changes"
fi
# This is a rough translation of:
#
# head_has_history() ? "HEAD" : EMPTY_TREE_SHA1_HEX
if git cat-file -e HEAD 2>/dev/null
then
head=HEAD
else
head=$(git hash-object -t tree --stdin </dev/null)
fi
if ! git diff-index --quiet --cached --ignore-submodules $head --
then
die "Working directory has staged changes"
fi
if ! git read-tree -u -m "$commit"
then
die "Could not update working tree to new HEAD"
fi

77
hooks/sendemail-validate.sample Normal file
View File

@@ -0,0 +1,77 @@
#!/bin/sh
# An example hook script to validate a patch (and/or patch series) before
# sending it via email.
#
# The hook should exit with non-zero status after issuing an appropriate
# message if it wants to prevent the email(s) from being sent.
#
# To enable this hook, rename this file to "sendemail-validate".
#
# By default, it will only check that the patch(es) can be applied on top of
# the default upstream branch without conflicts in a secondary worktree. After
# validation (successful or not) of the last patch of a series, the worktree
# will be deleted.
#
# The following config variables can be set to change the default remote and
# remote ref that are used to apply the patches against:
#
# sendemail.validateRemote (default: origin)
# sendemail.validateRemoteRef (default: HEAD)
#
# Replace the TODO placeholders with appropriate checks according to your
# needs.
validate_cover_letter () {
file="$1"
# TODO: Replace with appropriate checks (e.g. spell checking).
true
}
validate_patch () {
file="$1"
# Ensure that the patch applies without conflicts.
git am -3 "$file" || return
# TODO: Replace with appropriate checks for this patch
# (e.g. checkpatch.pl).
true
}
validate_series () {
# TODO: Replace with appropriate checks for the whole series
# (e.g. quick build, coding style checks, etc.).
true
}
# main -------------------------------------------------------------------------
if test "$GIT_SENDEMAIL_FILE_COUNTER" = 1
then
remote=$(git config --default origin --get sendemail.validateRemote) &&
ref=$(git config --default HEAD --get sendemail.validateRemoteRef) &&
worktree=$(mktemp --tmpdir -d sendemail-validate.XXXXXXX) &&
git worktree add -fd --checkout "$worktree" "refs/remotes/$remote/$ref" &&
git config --replace-all sendemail.validateWorktree "$worktree"
else
worktree=$(git config --get sendemail.validateWorktree)
fi || {
echo "sendemail-validate: error: failed to prepare worktree" >&2
exit 1
}
unset GIT_DIR GIT_WORK_TREE
cd "$worktree" &&
if grep -q "^diff --git " "$1"
then
validate_patch "$1"
else
validate_cover_letter "$1"
fi &&
if test "$GIT_SENDEMAIL_FILE_COUNTER" = "$GIT_SENDEMAIL_FILE_TOTAL"
then
git config --unset-all sendemail.validateWorktree &&
trap 'git worktree remove -ff "$worktree"' EXIT &&
validate_series
fi

128
hooks/update.sample Normal file
View File

@@ -0,0 +1,128 @@
#!/bin/sh
#
# An example hook script to block unannotated tags from entering.
# Called by "git receive-pack" with arguments: refname sha1-old sha1-new
#
# To enable this hook, rename this file to "update".
#
# Config
# ------
# hooks.allowunannotated
# This boolean sets whether unannotated tags will be allowed into the
# repository. By default they won't be.
# hooks.allowdeletetag
# This boolean sets whether deleting tags will be allowed in the
# repository. By default they won't be.
# hooks.allowmodifytag
# This boolean sets whether a tag may be modified after creation. By default
# it won't be.
# hooks.allowdeletebranch
# This boolean sets whether deleting branches will be allowed in the
# repository. By default they won't be.
# hooks.denycreatebranch
# This boolean sets whether remotely creating branches will be denied
# in the repository. By default this is allowed.
#
# --- Command line
refname="$1"
oldrev="$2"
newrev="$3"
# --- Safety check
if [ -z "$GIT_DIR" ]; then
echo "Don't run this script from the command line." >&2
echo " (if you want, you could supply GIT_DIR then run" >&2
echo " $0 <ref> <oldrev> <newrev>)" >&2
exit 1
fi
if [ -z "$refname" -o -z "$oldrev" -o -z "$newrev" ]; then
echo "usage: $0 <ref> <oldrev> <newrev>" >&2
exit 1
fi
# --- Config
allowunannotated=$(git config --type=bool hooks.allowunannotated)
allowdeletebranch=$(git config --type=bool hooks.allowdeletebranch)
denycreatebranch=$(git config --type=bool hooks.denycreatebranch)
allowdeletetag=$(git config --type=bool hooks.allowdeletetag)
allowmodifytag=$(git config --type=bool hooks.allowmodifytag)
# check for no description
projectdesc=$(sed -e '1q' "$GIT_DIR/description")
case "$projectdesc" in
"Unnamed repository"* | "")
echo "*** Project description file hasn't been set" >&2
exit 1
;;
esac
# --- Check types
# if $newrev is 0000...0000, it's a commit to delete a ref.
zero=$(git hash-object --stdin </dev/null | tr '[0-9a-f]' '0')
if [ "$newrev" = "$zero" ]; then
newrev_type=delete
else
newrev_type=$(git cat-file -t $newrev)
fi
case "$refname","$newrev_type" in
refs/tags/*,commit)
# un-annotated tag
short_refname=${refname##refs/tags/}
if [ "$allowunannotated" != "true" ]; then
echo "*** The un-annotated tag, $short_refname, is not allowed in this repository" >&2
echo "*** Use 'git tag [ -a | -s ]' for tags you want to propagate." >&2
exit 1
fi
;;
refs/tags/*,delete)
# delete tag
if [ "$allowdeletetag" != "true" ]; then
echo "*** Deleting a tag is not allowed in this repository" >&2
exit 1
fi
;;
refs/tags/*,tag)
# annotated tag
if [ "$allowmodifytag" != "true" ] && git rev-parse $refname > /dev/null 2>&1
then
echo "*** Tag '$refname' already exists." >&2
echo "*** Modifying a tag is not allowed in this repository." >&2
exit 1
fi
;;
refs/heads/*,commit)
# branch
if [ "$oldrev" = "$zero" -a "$denycreatebranch" = "true" ]; then
echo "*** Creating a branch is not allowed in this repository" >&2
exit 1
fi
;;
refs/heads/*,delete)
# delete branch
if [ "$allowdeletebranch" != "true" ]; then
echo "*** Deleting a branch is not allowed in this repository" >&2
exit 1
fi
;;
refs/remotes/*,commit)
# tracking branch
;;
refs/remotes/*,delete)
# delete tracking branch
if [ "$allowdeletebranch" != "true" ]; then
echo "*** Deleting a tracking branch is not allowed in this repository" >&2
exit 1
fi
;;
*)
# Anything else (is there anything else?)
echo "*** Update hook: unknown type of update to ref $refname of type $newrev_type" >&2
exit 1
;;
esac
# --- Finished
exit 0

BIN
image/2e1a016cbe73185352e2defa285e5599.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 217 KiB

BIN
image/c83075d3cbe5dd333419e6ecf1028e70.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 963 KiB

BIN
image/ce2ac5c9695bbf4e6ddf433a3c11f4e1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 186 KiB

BIN
image/file_67aee377039e2.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 778 KiB

BIN
image/file_67be74d65f458.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 3.9 MiB

BIN
image/file_67be752349316.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 186 KiB

BIN
image/file_67c187e9a21fe.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 180 KiB

BIN
image/file_67c5589dd2aff.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 114 KiB

BIN
image/file_67c558b8c3bad.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 147 KiB

BIN
image/file_67c55ded4eece.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 137 KiB

BIN
image/file_682161fb0fcfd.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 4.1 MiB

BIN
image/file_68999e3c72e62.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 1.5 MiB

BIN
image/image-60721.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 495 KiB

BIN
image/zipimg67aea6fbb71406.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 930 KiB

6
info/exclude Normal file
View File

@@ -0,0 +1,6 @@
# git ls-files --others --exclude-from=.git/info/exclude
# Lines that start with '#' are comments.
# For a project mostly in C, the following would be a good set of
# exclude patterns (uncomment them if you want to use them):
# *.[oa]
# *~

9
longTable.aux Normal file
View File

@@ -0,0 +1,9 @@
\relax
\providecommand \babel@aux [2]{\global \let \babel@toc \@gobbletwo }
\@nameuse{bbl@beforestart}
\babel@aux{english}{}
\newlabel{tab-1038}{{1}{1}{}{table.1}{}}
\@writefile{lot}{\contentsline {table}{\numberline {1}{Table 3 Efficacy/effectiveness and safety results in phase III clinical trials}}{1}{}\protected@file@percent }
\newlabel{tab-1039}{{2}{1}{}{table.2}{}}
\@writefile{lot}{\contentsline {table}{\numberline {2}{Table 4 Infusion schedule, target dose, and in-vivo persistence}}{1}{}\protected@file@percent }
\gdef \@abspage@last{2}

2389
longTable.bcf Normal file
View File

File diff suppressed because it is too large Load Diff

1979
longTable.log Normal file
View File

File diff suppressed because it is too large Load Diff

0
longTable.out Normal file
View File

BIN
longTable.pdf Normal file
View File

Binary file not shown.

84
longTable.run.xml Normal file
View File

@@ -0,0 +1,84 @@
<?xml version="1.0" standalone="yes"?>
<!-- logreq request file -->
<!-- logreq version 1.0 / dtd version 1.0 -->
<!-- Do not edit this file! -->
<!DOCTYPE requests [
<!ELEMENT requests (internal | external)*>
<!ELEMENT internal (generic, (provides | requires)*)>
<!ELEMENT external (generic, cmdline?, input?, output?, (provides | requires)*)>
<!ELEMENT cmdline (binary, (option | infile | outfile)*)>
<!ELEMENT input (file)+>
<!ELEMENT output (file)+>
<!ELEMENT provides (file)+>
<!ELEMENT requires (file)+>
<!ELEMENT generic (#PCDATA)>
<!ELEMENT binary (#PCDATA)>
<!ELEMENT option (#PCDATA)>
<!ELEMENT infile (#PCDATA)>
<!ELEMENT outfile (#PCDATA)>
<!ELEMENT file (#PCDATA)>
<!ATTLIST requests
version CDATA #REQUIRED
>
<!ATTLIST internal
package CDATA #REQUIRED
priority (9) #REQUIRED
active (0 | 1) #REQUIRED
>
<!ATTLIST external
package CDATA #REQUIRED
priority (1 | 2 | 3 | 4 | 5 | 6 | 7 | 8) #REQUIRED
active (0 | 1) #REQUIRED
>
<!ATTLIST provides
type (static | dynamic | editable) #REQUIRED
>
<!ATTLIST requires
type (static | dynamic | editable) #REQUIRED
>
<!ATTLIST file
type CDATA #IMPLIED
>
]>
<requests version="1.0">
<internal package="biblatex" priority="9" active="1">
<generic>latex</generic>
<provides type="dynamic">
<file>longTable.bcf</file>
</provides>
<requires type="dynamic">
<file>longTable.bbl</file>
</requires>
<requires type="static">
<file>blx-dm.def</file>
<file>blx-unicode.def</file>
<file>blx-compat.def</file>
<file>biblatex.def</file>
<file>blx-natbib.def</file>
<file>standard.bbx</file>
<file>numeric.bbx</file>
<file>numeric-comp.cbx</file>
<file>biblatex.cfg</file>
<file>english.lbx</file>
</requires>
</internal>
<external package="biblatex" priority="5" active="1">
<generic>biber</generic>
<cmdline>
<binary>biber</binary>
<infile>longTable</infile>
</cmdline>
<input>
<file>longTable.bcf</file>
</input>
<output>
<file>longTable.bbl</file>
</output>
<provides type="dynamic">
<file>longTable.bbl</file>
</provides>
<requires type="dynamic">
<file>longTable.bcf</file>
</requires>
</external>
</requests>

BIN
longTable.synctex.gz Normal file
View File

Binary file not shown.

72
longTable.tex Normal file
View File

@@ -0,0 +1,72 @@
\documentclass[8pt]{article}
\usepackage[UTF8]{ctex} % 设置字体
\usepackage{tabularray} % longtblr核心宏包
\usepackage{xcolor} % 颜色支持
\usepackage{geometry}
\usepackage{textcomp}
\usepackage[english]{babel}
\geometry{
a4paper, % A4纸张
left=1.5cm, % 左页边距
right=1.5cm, % 右页边距
top=2cm, % 上页边距
bottom=2cm, % 下页边距
}\linespread{1.05}\definecolor{tablegray}{RGB}{250,231,232}
\begin{document}
\begin{longtblr}[
caption = {Table 3 Efficacy/effectiveness and safety results in phase III clinical trials},
label = {tab-1038},
]{
% 你指定的列格式：自定义宽度比例 + 左对齐 + 防止文字溢出
colspec={X[0.3] X[0.3] X[0.3] X[0.3] X[0.3] X[0.3] X[0.3] X[0.3] X[1] },
width = \textwidth, % 表格宽度占满行宽（配合X列生效）
rowhead = 1, % 表头跨页重复
% 三线表核心配置
hline{1} = {1pt}, % 顶线（粗）
hline{2} = {0.5pt}, % 表头分隔线（细）
hline{Z} = {1pt}, % 底线（粗）
% 隔行变色 + 样式优化
row{1} = {bg=gray!30, font=\bfseries, abovesep=5pt, belowsep=5pt}, % 表头样式
row{even} = {bg=tablegray}, % 偶数行浅灰
row{odd} = {bg=white}, % 奇数行白色
rowsep = 4pt, % 行间距
}
% 表头
\textbf{Author, Year} & \textbf{Sample size} & \textbf{Intervention} & \textbf{Target} & \textbf{Comparator} & \textbf{Efficacy} & \textbf{Comparison of intervention and comparator} & \textbf{Safety} & \textbf{Safety in the intervention} \\
Sidiqi, 2023 \textcolor[HTML]{0082AA}{[61]} & 208 & Cilta-cel & BCMA & PVd or DPd & PFS & HR: 0.26; \textit{P} < 0.0001 & CRS, ICANS & CRS: 76\% any grade, 1\% Grade 3; ICANS: 5\% any grade, 0\% Grade 3/4; Other neurotoxicities: 17\% any grade, 2\% Grade 3/4 (including cranial nerve paralysis 9\%, peripheral neuropathy 3\%, and one case of Grade 1 movement/neurocognitive-related AE) \\
Rodriguez-Otero, 2023 \textcolor[HTML]{0082AA}{[62]} & 386 & Ide-cel & BCMA & Standard regimens (five different regimens) & PFS & HR for disease progression or death: 0.49 (95\% CI, 0.38 to 0.65; \textit{P} < 0.001) & Grade 3 or 4 adverse events & CRS: 93\% \\
San-Miguel, 2024 \textcolor[HTML]{0082AA}{[63]} & 419 & Cilta-cel & BMCA & SOC & Overall survival & 30mo OS 76.4\% vs 63.8\% (HR 0.55) & CRS, ICANS & ≥ G3 CRS:  1\%, ICANS: 0\% \\
Ailawadhi, 2024 \textcolor[HTML]{0082AA}{[57]} & 386 & Ide-cel & BMCA & SOC & mPFS & mPFS 13.3 vs 4.4 mo (HR 0.49) & CRS, ICANS & ≥ G3 CRS: 0\%, ICANS: 2\% \\
\end{longtblr}
\begin{longtblr}[
caption = {Table 4 Infusion schedule, target dose, and in-vivo persistence},
label = {tab-1039},
]{
% 你指定的列格式：自定义宽度比例 + 左对齐 + 防止文字溢出
colspec={X[0.3] X[0.6] X[1] X[1] },
width = \textwidth, % 表格宽度占满行宽（配合X列生效）
rowhead = 1, % 表头跨页重复
% 三线表核心配置
hline{1} = {1pt}, % 顶线（粗）
hline{2} = {0.5pt}, % 表头分隔线（细）
hline{Z} = {1pt}, % 底线（粗）
% 隔行变色 + 样式优化
row{1} = {bg=gray!30, font=\bfseries, abovesep=5pt, belowsep=5pt}, % 表头样式
row{even} = {bg=tablegray}, % 偶数行浅灰
row{odd} = {bg=white}, % 奇数行白色
rowsep = 4pt, % 行间距
}
% 表头
\textbf{Product} & \textbf{Infusion schedule} & \textbf{Target dose} & \textbf{Reported in }\textbf{vivo persistence*} \\
Cilta-cel & Single & 0.75 × 10⁶ CAR$^{{+}}$  cells/kg & Median 277 days \\
Ide-cel & Single & 150 × 10$^{{6}}$ to 450 × 10$^{{6}}$ cells & Median 4.2 months \\
CT103A & Single & 1.0 × 10⁶  cells/kg & Detectable ≥ 12 months (61\%) \\
LCAR-B38M & 3 split (20/30/50\%) & 0.5 × 10⁶  cells/kg & Median 8 months \\
ALLO-715 & Single & 320 × 10⁶  cells & < 28 days \\
\end{longtblr}
\end{document}

BIN
orcid_icon.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 6.7 KiB

1827
references_3113.bib Normal file
View File

File diff suppressed because it is too large Load Diff

9
table.aux Normal file
View File

@@ -0,0 +1,9 @@
\relax
\providecommand \babel@aux [2]{\global \let \babel@toc \@gobbletwo }
\@nameuse{bbl@beforestart}
\babel@aux{english}{}
\newlabel{tab-1038}{{1}{1}{}{table.1}{}}
\@writefile{lot}{\contentsline {table}{\numberline {1}{Table 3 Efficacy/effectiveness and safety results in phase III clinical trials}}{1}{}\protected@file@percent }
\newlabel{tab-1039}{{2}{1}{}{table.2}{}}
\@writefile{lot}{\contentsline {table}{\numberline {2}{Table 4 Infusion schedule, target dose, and in-vivo persistence}}{1}{}\protected@file@percent }
\gdef \@abspage@last{2}

1978
table.log Normal file
View File

File diff suppressed because it is too large Load Diff

BIN
table.pdf Normal file
View File

Binary file not shown.

BIN
table.synctex.gz Normal file
View File

Binary file not shown.

71
table.tex Normal file
View File

@@ -0,0 +1,71 @@
\documentclass[8pt]{article}
\usepackage{tabularray} % longtblr核心宏包
\usepackage{xcolor} % 颜色支持
\usepackage{geometry}
\usepackage{textcomp}
\usepackage[english]{babel}
\geometry{
a4paper, % A4纸张
left=1.5cm, % 左页边距
right=1.5cm, % 右页边距
top=2cm, % 上页边距
bottom=2cm, % 下页边距
}\linespread{1.05}\definecolor{tablegray}{RGB}{250,231,232}
\begin{document}
\begin{longtblr}[
caption = {Table 3 Efficacy/effectiveness and safety results in phase III clinical trials},
label = {tab-1038},
]{
% 你指定的列格式：自定义宽度比例 + 左对齐 + 防止文字溢出
colspec={X[0.3] X[0.3] X[0.3] X[0.3] X[0.3] X[0.3] X[0.3] X[0.3] X[1] },
width = \textwidth, % 表格宽度占满行宽（配合X列生效）
rowhead = 1, % 表头跨页重复
% 三线表核心配置
hline{1} = {1pt}, % 顶线（粗）
hline{2} = {0.5pt}, % 表头分隔线（细）
hline{Z} = {1pt}, % 底线（粗）
% 隔行变色 + 样式优化
row{1} = {bg=gray!30, font=\bfseries, abovesep=5pt, belowsep=5pt}, % 表头样式
row{even} = {bg=tablegray}, % 偶数行浅灰
row{odd} = {bg=white}, % 奇数行白色
rowsep = 4pt, % 行间距
}
% 表头
\textbf{Author, Year} & \textbf{Sample size} & \textbf{Intervention} & \textbf{Target} & \textbf{Comparator} & \textbf{Efficacy} & \textbf{Comparison of intervention and comparator} & \textbf{Safety} & \textbf{Safety in the intervention} \\
Sidiqi, 2023 \textcolor[HTML]{0082AA}{[61]} & 208 & Cilta-cel & BCMA & PVd or DPd & PFS & HR: 0.26; \textit{P} < 0.0001 & CRS, ICANS & CRS: 76\% any grade, 1\% Grade 3; ICANS: 5\% any grade, 0\% Grade 3/4; Other neurotoxicities: 17\% any grade, 2\% Grade 3/4 (including cranial nerve paralysis 9\%, peripheral neuropathy 3\%, and one case of Grade 1 movement/neurocognitive-related AE) \\
Rodriguez-Otero, 2023 \textcolor[HTML]{0082AA}{[62]} & 386 & Ide-cel & BCMA & Standard regimens (five different regimens) & PFS & HR for disease progression or death: 0.49 (95\% CI, 0.38 to 0.65; \textit{P} < 0.001) & Grade 3 or 4 adverse events & CRS: 93\% \\
San-Miguel, 2024 \textcolor[HTML]{0082AA}{[63]} & 419 & Cilta-cel & BMCA & SOC & Overall survival & 30mo OS 76.4\% vs 63.8\% (HR 0.55) & CRS, ICANS & ≥ G3 CRS:  1\%, ICANS: 0\% \\
Ailawadhi, 2024 \textcolor[HTML]{0082AA}{[57]} & 386 & Ide-cel & BMCA & SOC & mPFS & mPFS 13.3 vs 4.4 mo (HR 0.49) & CRS, ICANS & ≥ G3 CRS: 0\%, ICANS: 2\% \\
\end{longtblr}
\begin{longtblr}[
caption = {Table 4 Infusion schedule, target dose, and in-vivo persistence},
label = {tab-1039},
]{
% 你指定的列格式：自定义宽度比例 + 左对齐 + 防止文字溢出
colspec={X[0.3] X[0.6] X[1] X[1] },
width = \textwidth, % 表格宽度占满行宽（配合X列生效）
rowhead = 1, % 表头跨页重复
% 三线表核心配置
hline{1} = {1pt}, % 顶线（粗）
hline{2} = {0.5pt}, % 表头分隔线（细）
hline{Z} = {1pt}, % 底线（粗）
% 隔行变色 + 样式优化
row{1} = {bg=gray!30, font=\bfseries, abovesep=5pt, belowsep=5pt}, % 表头样式
row{even} = {bg=tablegray}, % 偶数行浅灰
row{odd} = {bg=white}, % 奇数行白色
rowsep = 4pt, % 行间距
}
% 表头
\textbf{Product} & \textbf{Infusion schedule} & \textbf{Target dose} & \textbf{Reported in }\textbf{vivo persistence*} \\
Cilta-cel & Single & 0.75 × 10⁶ CAR$^{{+}}$  cells/kg & Median 277 days \\
Ide-cel & Single & 150 × 10$^{{6}}$ to 450 × 10$^{{6}}$ cells & Median 4.2 months \\
CT103A & Single & 1.0 × 10⁶  cells/kg & Detectable ≥ 12 months (61\%) \\
LCAR-B38M & 3 split (20/30/50\%) & 0.5 × 10⁶  cells/kg & Median 8 months \\
ALLO-715 & Single & 320 × 10⁶  cells & < 28 days \\
\end{longtblr}
\end{document}

BIN
tabledemo.pdf Normal file
View File

Binary file not shown.

48
tabledemo.tex Normal file
View File

@@ -0,0 +1,48 @@
\documentclass{article}
\usepackage{ctex}
\usepackage{tabularray}
\usepackage{xcolor}
\usepackage{geometry}
% 修正geometry参数：textwidth（小写w），增大边距解决溢出
\geometry{a4paper, margin=3cm, textwidth=12cm}
% 预定义偶数行背景色（简化书写，无冲突）
\definecolor{evenRowColor}{RGB}{250,231,232}
\begin{document}
\section{三线表（零报错+零警告+样式全生效）}
% 纯内联样式：longtblr原生语法，无任何自定义环境
\begin{longtblr}{
% 紧凑自适应列配置（比例0.8:1.5:1:0.8，避免溢出）
colspec={X[0.8,c] X[1.5,l] X[1,r] X[0.8,c]},
% 三线表核心样式（100%生效）
hline{1}={1.5pt}, % 上表框粗线
hline{2}={0.75pt}, % 表头分隔中线
hline{Z}={1.5pt}, % 下表框粗线
row{1}={font=\bfseries}, % 表头仅粗体，无背景
% 偶数行背景（逐行指定，无解析冲突）
row{2}={bg=evenRowColor},
row{4}={bg=evenRowColor},
row{6}={bg=evenRowColor},
row{8}={bg=evenRowColor},
row{10}={bg=evenRowColor},
% 极致紧凑间距，彻底解决溢出
rowsep=5pt,
colsep=4pt,
vlines={0pt}, % 隐藏竖线，纯三线表
}
% 极简适配内容，无宽度溢出
分组 & 指标 & 检测值 & 参考值 \\
\SetCell[c=2]{c} 实验组(n=50) & & 均值±SD & 参考区间 \\
\SetCell[r=2]{c} 血液 & 血糖(mmol/L) & 5.2±0.8 & 3.9-6.1 \\
& 血压(mmHg) & 120/80±5 & 90-140/60-90 \\
\SetCell[c=2]{c} 对照组(n=50) & & 均值±SD & 参考区间 \\
\SetCell[r=2]{c} 血液 & 血糖(mmol/L) & 6.8±1.2 & 3.9-6.1 \\
& 血压(mmHg) & 135/90±8 & 90-140/60-90 \\
\SetCell[c=3]{c} P值 & & & <0.05 \\
\SetCell[c=3]{c} 结论：实验组更优 & & & (P<0.05) \\
\end{longtblr}
\end{document}

20
texput.log Normal file
View File

@@ -0,0 +1,20 @@
This is LuaHBTeX, Version 1.22.0 (TeX Live 2025) (format=lualatex 2025.11.6) 9 FEB 2026 16:13
restricted system commands enabled.
**tmr-tex.tex
! Emergency stop.
<*> tmr-tex.tex
*** (job aborted, file error in nonstop mode)
Here is how much of LuaTeX's memory you used:
5 strings out of 476081
100000,460012 words of node,token memory allocated 270 words of node memory still in use:
1 hlist, 39 glue_spec nodes
avail lists: 2:12,3:3,4:1,5:1
22562 multiletter control sequences out of 65536+600000
14 fonts using 591679 bytes
0i,0n,0p,0b,6s stack positions out of 10000i,1000n,20000p,200000b,200000s
! ==> Fatal error occurred, no output PDF file produced!

BIN
tmr-logo.pdf Normal file
View File

Binary file not shown.

1773
tmr-tex.cls Normal file
View File

File diff suppressed because it is too large Load Diff