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연구 논문

만성 심부전을 위한 진무탕과 오령산의 네트워크 약리학, GEO 전사체학 및 분자 도킹 분석

116 조회수

⸱

2026년 9월 11일

 ,  ,  ,  , 
1National Clinical Research Center for Traditional Chinese Medicine in Cardiovascular Diseases, Xiyuan Hospital

이 논문에서

요약

본 연구는 네트워크 기반 표적 매핑, GEO 발현 데이터 및 도킹을 통합하여 만성 심부전에서 진무-오령(Zhenwu-Wuling) 조합과 관련된 후보 물질의 순위를 결정합니다.

초록

진무탕(Zhenwu Decoction)과 오령산(Wuling Powder)의 병용 투여는 만성 심부전(CHF) 치료에 사용되지만, 그 분자적 기전은 아직 완전히 규명되지 않았습니다. 본 연구에서는 네트워크 기반 타겟 매핑, Gene Expression Omnibus (GEO) 전사체 데이터 및 분자 도킹을 통합하여 후보 타겟과 경로의 우선순위를 선정하였습니다. 8가지 약재의 화합물 기록을 약리학 및 화학 데이터베이스에서 수집하여 화합물 및 타겟 스크리닝을 수행하였습니다. CHF 관련 유전자를 6가지 질환 리소스에서 수집하였으며, PPI 네트워크 및 기능적 풍부도 분석을 통해 처방 관련 타겟과의 중첩 여부를 검토하였습니다. GSE5406을 통해 심부전 및 정상 샘플의 심근 발현 데이터를 확보하였습니다. 이러한 워크플로우를 통해 89개의 스크리닝된 화합물, 314개의 비중복 화합물 타겟, 2,515개의 CHF 관련 타겟이 도출되었으며, 143개의 공통 유전자가 확인되었습니다. 질환-타겟 네트워크에서는 AKT1, IL6, TNF, SRC, ESR1이 가장 높은 순위로 나타났습니다. 발현 분석 결과 1,348개의 차등 발현 유전자(DEGs)가 도출되었으며, HSP90AA1과 STAT1을 전사체 허브 후보로 선정하였습니다. 풍부도 분석 결과 저산소증, 혈액 순환, TNF 관련 신호 전달, PI3K-Akt 및 MAPK 경로를 중심으로 클러스터가 형성되었습니다. 도킹 분석 결과, 대표적인 성분-타겟 쌍에서 음수의 예측 점수가 나타났으며, STAT1과 alisol C 조합에서 가장 낮은 점수(-9.2 kcal/mol)가 관찰되었습니다. 이러한 결과는 검증 가능한 계산적 가설을 제시하며, 실험적 검증을 위한 후보군을 우선순위화하는 근거가 됩니다.

서론

CHF는 구조적 또는 기능적 심근 이상으로 인해 발생하는 이질적인 증후군으로 구성됩니다. 이러한 변화는 심실 충만 또는 수축기 박출을 저해할 수 있으며, 종종 폐성 또는 전신성 울혈을 동반합니다. 전형적인 증상으로는 호흡 곤란, 피로, 체액 정체 등이 있습니다1. 1990년부터 2023년까지의 중국 글로벌 질병 부담 데이터를 회고적으로 분석한 결과, 새로 진단된 CHF의 국가적 유병률이 30년 동안 208.4% 증가한 것으로 나타났습니다2.

전통 중의학(TCM)에서 울혈성 심부전(CHF)은 심성 부종, 호흡곤란 증후군 및 전신 부종에 해당합니다. 그 병인론은 일반적으로 본허표실(root deficiency and branch excess)로 설명됩니다. 음, 양 및 기의 부족이 근본적인 병리적 기초를 구성하며, 내부의 수액 저류와 혈액 정체가 표면적인 실증으로 나타납니다. 이에 따라 중의학적 관리는 양기를 보하고, 정체된 수분을 제거하며, 순환을 촉진하고, 어혈을 제거하는 것을 강조합니다.

진무탕(Zhenwu Decoction)과 오령산(Wuling Powder)의 병용 투여는 임상적 CHF 관리를 위한 고전적인 한약 전략입니다. 진무탕은 양기를 돋우고 이뇨를 촉진하며, 오령산은 양기를 보하고 체액 대사를 조절합니다. 이 두 처방을 함께 사용하면 양기를 강화하고 내부의 습을 제거할 수 있습니다. 임상적 근거에 따르면, 이러한 조합은 증상을 완화하고 심장 기능 지표를 개선하며 CHF의 악화를 늦출 수 있습니다3. 본 연구에서는 먼저 네트워크 약리학을 사용하여 처방 성분과 CHF 후보 타겟을 매핑한 후, 도킹 시뮬레이션을 통해 선택된 성분-타겟 쌍의 순위를 매겼습니다. 이후 GEO 유래 발현 프로파일을 사용하여 네트워크 후보군을 심근 질환 맥락에 배치하고 하위 경로를 분석하였습니다. 본 연구는 후속 실험적 검증을 위한 후보군 우선순위 결정을 위한 가설 생성용 계산 프레임워크를 제공하며, 임상적 효능이나 검증된 기전을 확립하는 것은 아닙니다.

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프로토콜

This study analyzed public database resources and did not involve new human participants, human specimens, or animal experiments. All databases, web-based resources, software, analysis packages, visualization tools, molecular docking tools, and other computational resources used in this study, together with their details, are listed in the Table of Materials.

Formula composition and botanical provenance
The eight distinct crude drugs were aligned to current botanical and pharmacopoeial naming as follows: Aconiti Lateralis Radix Praeparata, the processed lateral root of Aconitum carmichaelii Debeaux; Poria, the sclerotium of Wolfiporia cocos (F.A.Wolf) Ryvarden & Gilb.; Atractylodis Macrocephalae Rhizoma, the rhizome of Atractylodes macrocephala Koidz.; Paeoniae Radix Alba, the root of Paeonia lactiflora Pall.; Zingiberis Rhizoma Recens, the fresh rhizome of Zingiber officinale Roscoe; Alismatis Rhizoma, the rhizome of Alisma orientale (Sam.) Juzep.; Polyporus, the sclerotium of Polyporus umbellatus (Pers.) Fr.; and Cinnamomi Ramulus, the young twig of Cinnamomum cassia (L.) J.Presl. The prescription comprised 9 g Aconiti Lateralis Radix Praeparata, 12 g Poria, 9 g Paeoniae Radix Alba, 12 g Atractylodis Macrocephalae Rhizoma (stir-fried Atractylodes), 9 g Zingiberis Rhizoma Recens, 9 g Polyporus, 15 g Alismatis Rhizoma, and 6 g Cinnamomi Ramulus; the corresponding ratio was 3:4:3:4:3:3:5:2. Aconiti Lateralis Radix Praeparata was treated as processed material. This was an entirely in silico study: no decoction batch was prepared, purchased, chemically assayed, or administered. Manufacturer, lot number, batch-specific processing records, and voucher specimens are therefore not applicable; the gram amounts define the formula composition and relative ratio, not experimental exposure. Poria and Atractylodis Macrocephalae Rhizoma are shared by the two formulae. Botanical names and medicinal-part terminology were checked against the 2025 Pharmacopoeia of the People’s Republic of China4.

Screening and target prediction of active ingredients in Zhenwu Decoction and Wuling Powder
TCMSP was queried separately for compounds recorded for Zhenwu Decoction and Wuling Powder, including Aconiti Lateralis Radix Praeparata, Poria, Atractylodis Macrocephalae Rhizoma, Paeoniae Radix Alba, Zingiberis Rhizoma Recens, Alismatis Rhizoma, Polyporus, and Cinnamomi Ramulus. Initial filtering required oral bioavailability (OB) of at least 30% and drug-likeness (DL) of at least 0.18. When a simplified molecular-input line-entry system (SMILES) record was unavailable, the corresponding structure was retrieved from a public chemical database. SMILES records were evaluated for physicochemical and drug-likeness properties. A compound passed the secondary filter when its molecular weight was ≤500, lipophilicity ≤5, hydrogen-bond donors ≤5, hydrogen-bond acceptors ≤10, at least three of the Lipinski, Ghose, Veber, Egan, and Muegge rule sets were positive, and predicted gastrointestinal absorption was high5. Eligible structures were subjected to target prediction, and only predictions with probability >0.02 were included in the target list. TCMSP and target-prediction records were combined at the herb-compound-target level, duplicate identifiers were collapsed, and gene names were standardized to official gene symbols.

Deduplication produced 646 candidate associations; 71 rows without a mapped official gene symbol were excluded, leaving 575 standardized associations. The independent herb-level integration yielded 314 nonredundant target genes. The compound and target screening criteria are supported by the TCMSP and SwissTargetPrediction source papers6,7.

Screening of disease targets for CHF
CHF-related gene entries were downloaded from GeneCards, OMIM, TTD, DisGeNET, DrugBank, and PharmGKB. The disease search used four phrases: heart failure, congestive heart failure, diastolic heart failure, and systolic heart failure. For GeneCards, entries with relevance scores below 0.20 were excluded. The six database-derived target sets were integrated after duplicate identifiers were removed, while preserving the source database for each record. All screened gene names were standardized to official gene symbols. The overlap between the formula and CHF target lists was visualized using a Venn diagram. The shared identifiers were treated as candidate formula-related targets for CHF.

TCMSP was searched on 7 February 2026; SwissTargetPrediction, GeneCards, OMIM, and DisGeNET on 10 February 2026; and PharmGKB, TTD, and DrugBank on 12 February 2026. Disease queries were entered as heart failure, congestive heart failure, heart failure, diastolic, and heart failure, systolic; the search was restricted to Homo sapiens where supported. Retrieved entries were standardized to official gene symbols before deduplication.

Construction of the protein-protein interaction (PPI) network and screening of core targets
The shared target identifiers were queried in STRING for PPI analysis. The query used Homo sapiens interactions and a confidence cutoff above 0.9. Isolated nodes were omitted, and the resulting STRING interaction data were used for network visualization and topological analysis. Candidate hubs were ranked by network topology. Betweenness, closeness, degree, and eigenvector centralities were calculated as network descriptors. Degree was the primary ranking variable, and the five highest-degree targets were retained as candidate hubs.

Official gene symbols were used for gene-level analyses, including AKT1, IL6, TNF, SRC, ESR1, BCL2, CASP3, PPARG, PTGS2, and MMP9; protein names are written in full only when needed for clarity. The initial PPI network contained 143 nodes and 584 interaction records before visualization-specific filtering; after network processing, the graph contained 143 nodes and 292 edges.

Construction of the drug-disease-component-target network
Once names were standardized, the network data were assembled and used to construct the drug-disease-component-target graph.

GO and KEGG enrichment analyses
The candidate CHF target list was submitted to Metascape for functional annotation and pathway enrichment analysis. The analysis was run for Homo sapiens. The raw p-value cutoff was set at 0.01, a minimum overlap of 3, and a minimum enrichment score of 1.5. GO bar charts and KEGG bubble plots were generated for visualization. The 143-gene input set was used for Gene Ontology biological-process (GO-BP), molecular-function (GO-MF), and KEGG enrichment analyses. The analysis thresholds were raw p < 0.01, minimum overlap 3, and minimum enrichment 1.58. The GO-CC analysis was based on a 60-gene hit list against a 30,230-gene library. Because this input differed from the 143-gene input used for the BP, MF, and KEGG analyses, the GO-CC results were reported separately and were not quantitatively pooled with the other categories.

Screening of heart failure-related targets based on the GEO database
The GSE5406 dataset9 was used for differential-expression analysis. The series included 210 myocardial samples: 108 with ischemic cardiomyopathy, 86 with idiopathic dilated cardiomyopathy, and 16 nonfailing donor controls. The GEO record identifies the platform as GPL96, the Affymetrix Human Genome U133A Array. The series matrix was already robust multi-array average (RMA)-normalized. Probes without matched SYMBOL annotations were removed after matching to the platform annotation, and rowSums(expression > 0.5) ≥ 3 was applied before limma fitting. Group levels were defined as Control, DCM, and ICM, with a no-intercept design matrix (~0 + group) and the contrasts (DCM+ICM)/2-Control, DCM-Control, and ICM-Control. Differential expression was defined as adjusted p value < 0.05 and |log2(Fold Change)| > 0.3. Because all samples were processed within one GEO series and no separate batch variable was available, no batch-correction term was introduced. Differential-expression results were visualized using heatmaps and volcano plots. DEGs were subjected to PPI analysis followed by network-based hub ranking. Docking and pose visualization were conducted for the selected targets and prescription compounds.

The GEO record identifies the platform as GPL96, the Affymetrix Human Genome U133A Array. Group levels were defined as Control, DCM, and ICM, with a no-intercept design matrix (~0 + group) and the contrasts (DCM+ICM)/2-Control, DCM-Control, and ICM-Control. The series matrix was already robust multi-array average (RMA)-normalized; probes with missing SYMBOL annotations were removed after matching to the platform annotation, and the code applies rowSums(expression > 0.5) ≥ 3 before limma fitting. Because all samples were processed within one GEO series and no separate batch variable was available, no batch-correction term was introduced.

Molecular docking verification
A three-dimensional PDB structure for each receptor was obtained from the RCSB Protein Data Bank. Protein structures were prepared by removing crystallographic waters and non-receptor ligands. Structure-data file (SDF) structures for the five highest-degree compounds were obtained from a public chemical database.

The receptor structures used for docking were AKT1/1H10 (chain A, 1.40 Å, 4IP), IL6/1ALU (chain A, 1.90 Å, TLA), TNF/1A8M (chain A, 2.30 Å, no co-crystallized ligand), SRC/1A09 (chain A, 2.00 Å, no ligand), ESR1/1A52 (chain A, 2.80 Å, EST), STAT1/1BF5 (chain A, 2.90 Å, no ligand), and HSP90AA1/3OWB (chain A, 2.05 Å, BSM). The center, box dimensions, and random seed were specified for each tested pair. The search boxes were defined to encompass the receptor for exploratory global docking.

Receptor and ligand files were prepared with the docking-preparation tools. Waters were deleted and polar hydrogens were added to receptor structures. SDF ligands were converted to PDB, then dehydrated and hydrogenated before PDBQT export. The prepared receptor-ligand files were assigned to the docking models. PDBQT files were loaded into AutoDock Vina. Vina runs were executed and scores were recorded. Scores below -5 kcal/mol were treated as comparatively more favorable and scores below -7 kcal/mol as more strongly predicted interactions10; these are not experimental thresholds. The three complexes with the best predicted scores were selected for visualization.

No search-depth or output-mode override was specified; therefore, the documented command-line defaults were used: exhaustiveness 8 and at most 9 modes. Pair-specific random seeds were used, and mode 1 (the best-scoring returned pose) was selected for representative visualization. The HSP90AA1 output-file assignment was confirmed because each mode-1 score matched the corresponding HSP90AA1 value in the docking-score matrix. Molecular dynamics simulations were not performed, so the scores are exploratory computational predictions only.

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결과

진무탕과 오령산 병용 투여의 활성 성분 및 타겟
각 약재에 대한 성분-타겟 기록을 수집하였다. 각 약재별 타겟 수는 부자 178개, 복령 21개, 백출 18개, 백작약 86개, 생강 127개, 택사 96개, 복분자 4개, 계지 45개였다. 심볼 표준화 및 중복 제거 후, 통합된 처방 관련 세트에는 314개의 비중복 타겟 유전자가 포함되었다. dihydrocapsaicin의 약재 할당은 식물학적 기원의 확인이 아닌 데이터베이스 할당으로 보고되었다.

CHF 관련 질환 타겟 획득
6개의 질환 리소스를 통해 총 3,563개의 CHF 원시 타겟 레코드를 수집하였습니다. 분포는 다음과 같습니다: GeneCards에서 2,000개, OMIM에서 973개, PharmGKB에서 17개, TTD에서 83개, DisGeNET에서 459개, 그리고 DrugBank에서 31개의 타겟을 수집하였습니다. 중복 제거 후 2,5...

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토론

CHF는 장기적인 임상 관리가 필요하며 종종 복잡한 약물 요법을 수반합니다. Wang JX와 Zhang S11 등의 연구 및 기타 연구들에 따르면, 장기적인 기존의 병용 약물 치료는 복약 순응도를 감소시킬 수 있습니다. 낮은 순응도는 재입원 위험과 부정적인 정서적 결과를 증가시킬 수 있습니다. 현재 사용 가능한 약물들은 주로 증상 조절과 심실 리모델링 완화에 집중되어 있습니다. 2023년 중국 심부전 관리 국가 가이드라인12에서는 최근 심부전이 악화된 환자의 GDMT에 수용성 구아닐릴 사이클라제 자극제인 vericiguat를 추가할 것을 권고합니다. 그럼에도 불구하고, 심부전 관리에는 여전히 몇 가지 과제가 남아 있습니다. 심근세포는 최종 분화된 상태이며 재생 능력이 제한적이어서, 심근 손상은 종종 가역적이지 않게 진행됩니다. 기존의 약물들은 확립된 구조적 손상을 완전히 되돌리기보다는 주로 심근 섬유화와 심실 리모델링을 늦추는 역할을 합니다.<...

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공개 사항

저자들은 본 연구와 관련하여 이해관계의 충돌이 없음을 밝힙니다.

재료

이 논문에 사용된 재료 목록
이름회사카탈로그 번호댓글
AutoDock VinaCCSB-Scrippsv1.2.6사용된 커맨드 라인 버전; 현재 공식 릴리스 v1.2.7; 사용된 설정은 CLI 기본값인 exhaustiveness 8 및 num_modes 9입니다. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockTools스크립스 연구소(The Scripps Research Institute)v1.5.7수용체/리간드 준비; 분석에 사용된 버전. https://autodock.scripps.edu/
BioconductorBioconductor 프로젝트3.16 호환성 릴리스R 4.2 호환 환경; 현재 릴리스 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape 데스크톱Cytoscape 컨소시엄v3.7.1사용된 네트워크 시각화/분석 버전; 현재 릴리스 v3.10.4. https://cytoscape.org/download.html
DisGeNETMedBioinformatics Solutionsv25.4CHF 관련 유전자 검색; 2026-02-10 검색. https://disgenet.com/
DrugBankOMx 개인 건강 분석v6.0CHF 관련 유전자 검색; 2026-02-12 검색. https://go.drugbank.com/
GeneCards와이즈만 과학 연구소웹 데이터베이스CHF 관련 유전자 검색; 2026-02-10 검색. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406; GPL96RMA 정규화된 인간 좌심실 마이크로어레이 시리즈. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu133a.dbBioconductorBioconductor 3.16 환경Affymetrix Human Genome U133A 프로브 어노테이션; 정확한 패치 버전은 유지되지 않음. https://bioconductor.org/packages/hgu133a.db/
limmaBioconductorBioconductor 3.16 환경차등 발현 분석; 정확한 패치 버전은 유지되지 않음. https://bioconductor.org/packages/limma/
MacBook사과M4 프로세서로컬 컴퓨팅 워크스테이션; 메모리 사양 미기재. https://www.apple.com/mac/
MetascapeMetascapev3.5GO/KEGG 농축 분석; 정확한 접속 시간 기록 없음. https://metascape.org/
Microsoft Excel마이크로소프트데스크톱 애플리케이션데이터 큐레이션/내보내기; 애플리케이션 빌드는 유지되지 않음. https://www.microsoft.com/microsoft-365/excel
온라인 멘델 유전학 정보(OMIM)존스 홉킨스 대학교웹 데이터베이스CHF 관련 유전자 검색; 2026-02-10 검색. https://www.omim.org/
Open BabelOpen Babel 프로젝트버전이 유지되지 않음도킹 워크플로우에서의 구조 변환. https://openbabel.org/
PharmGKBPharmGKB웹 데이터베이스CHF 관련 유전자 검색; 2026-02-12 검색. https://www.pharmgkb.org/
PubChemNCBI웹 데이터베이스SMILES 및 구조 완성. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrö딩거버전이 유지되지 않음단백질 전처리 및 포즈 시각화. https://www.pymol.org/
RR 재단4.2.0+분석을 위해 기록된 버전 제품군; 현재 릴리스 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDB웹 아카이브단백질 결정 구조. https://www.rcsb.org/
RStudio가정하다2026.04.0+526분석 IDE가 수정 노트에 기록되었습니다. https://posit.co/download/rstudio-desktop/
제공된 텍스트가 없습니다. 번역할 소스 텍스트를 입력해 주십시오.STRING 컨소시엄웹 서비스호모 사피엔스; 신뢰도 >0.9; 릴리스/세션 메타데이터가 유지되지 않음. https://string-db.org/
SwissADMESIB 스위스 생물정보학 연구소웹 서비스ADME 및 Lipinski 법칙 스크리닝; 로컬 버전 해당 없음. https://www.swissadme.ch/
SwissTargetPredictionSIB 스위스 생물정보학 연구소2019 주요 업데이트호모 사피엔스; 확률 >0.02; 2026-02-10 검색. https://www.swisstargetprediction.ch/
TCMSP노스웨스트 A&F 대학교v2.3화합물 검색; 2026-02-07 검색. https://tcmsp-e.com/
TTD치료 표적 데이터베이스2026년 출시CHF 관련 유전자 검색; 2026-02-12 검색. https://ttd.idrblab.cn/
UniProtKBUniProt 컨소시엄릴리스 2026_01검토된 Homo sapiens 유전자 기호 표준화. https://www.uniprot.org/
VennyCNB-CSICv2.1.0타겟 세트 교집합 시각화. https://bioinfogp.cnb.csic.es/tools/venny/

참고문헌

  1. McDonagh TA, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021;42(36):3599-3726.
  2. Yang X, et al. The evolving burden of heart failure in China: a 34-year subnational analysis of trends and causes from the Global Burden of Disease Study 2023. Mil Med Res. 2025;12(1):65.
  3. Tang KH. Study on Zhenwu Decoction combined with Wuling Powder in the treatment of heart failure with yang deficiency and water flooding syndrome. Chin Med Guide. 2024;22(14):153-155.
  4. National Pharmacopoeia Commission. Pharmacopoeia of the People's Republic of China. 2025 ed. China Medical Science Press; Beijing; 2025.
  5. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717.
  6. Ru J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.
  7. Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357-W364.
  8. Zhou Y, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.
  9. Hannenhalli S, et al. Transcriptional genomics associates FOX transcription factors with human heart failure. Circulation. 2006;114(12):1269-1276.
  10. Shahzadi Z, et al. Network pharmacology and molecular docking: combined computational approaches to explore the antihypertensive potential of Fabaceae species. Bioresour Bioprocess. 2024;11:53.
  11. Wang JX, Zhang S. Correlation between medication adherence, coping styles and depression in patients with chronic heart failure. J Int Psychiatry. 2026;53(1):225-228.
  12. National Center for Cardiovascular Diseases, Heart Failure Professional Committee of National Cardiovascular Experts Committee, Heart Failure Committee of Chinese Medical Doctor Association, et al. 2023 National guidelines for heart failure. Chin J Heart Fail Cardiomyopathies. 2023;7(4):215-311.
  13. Dong GJ, Wei Y. Expert consensus on TCM disease nomenclature of heart failure. Chin Arch Tradit Chin Med. 2025;43(7):253-258.
  14. Zhang ZJ. Treatise on Febrile Diseases. Qian CC, collator. People's Medical Publishing House; Beijing; 2005. p. 22-23.
  15. Zheng MH, Guo D. Research progress on active ingredients and mechanism of Zhenwu Decoction in the treatment of chronic heart failure. China J Tradit Chin Med Pharm. 2024;39(11):6007-6011.
  16. Chen L, Yu D, Ling S, Xu JW. Mechanism of tonifying-kidney Chinese herbal medicine in the treatment of chronic heart failure. Front Cardiovasc Med. 2022;9:988360.
  17. Wang Y, et al. A review of Chinese herbal medicine for the treatment of chronic heart failure. Curr Pharm Des. 2017;23(34):5115-5124.
  18. He Y, et al. Clinical evidence and potential mechanisms of traditional Chinese medicine for the treatment of chronic gastritis: an updated systematic review and meta-analysis. J Herb Med. 2023;42:100761.
  19. Wang H, et al. β-Sitosterol as a promising anticancer agent for chemoprevention and chemotherapy: mechanisms of action and future prospects. Adv Nutr. 2023;14(5):1085-1110.
  20. Dwivedi J, et al. Aspects of β-sitosterol's pharmacology, nutrition and analysis. Curr Pharm Biotechnol. 2025;26(14):2234-2256.
  21. Khan Z, et al. Multifunctional roles and pharmacological potential of β-sitosterol: emerging evidence toward clinical applications. Chem Biol Interact. 2022;365:110117.
  22. Jittiwat J, Suksamrarn A, Tocharus C, Tocharus J. Dihydrocapsaicin effectively mitigates cerebral ischemia-induced pathological changes in vivo, partly via antioxidant and anti-apoptotic pathways. Life Sci. 2021;283:119842.
  23. Lai Y, et al. Dihydrocapsaicin suppresses the STING-mediated accumulation of ROS and NLRP3 inflammasome and alleviates apoptosis after ischemia-reperfusion injury of perforator skin flap. Phytother Res. 2024;38(5):2539-2559.
  24. Chen Y, et al. Dihydrocapsaicin attenuates oxidative stress and apoptosis in acute myocardial infarction via promoting Raf-1/ASK1 complex formation. Phytomedicine. 2025;146:157126.
  25. Li YX, Liu SY. Kaempferol alleviates myocardial injury in rats with coronary heart disease via regulating the PI3K/AKT/mTOR pathway. Chin J Histochem Cytochem. 2025;34(5):439-445.
  26. Wu JP, Chen JJ, Yuan Q, et al. Effects and mechanisms of kaempferol on cardiomyocyte apoptosis induced by hypoxia/reoxygenation injury. Shaanxi Med J. 2024;53(1):3-7,18.
  27. Chen MJ, Li XD, Wang NN. Effects and mechanisms of kaempferol on cardiac function in rat models of chronic heart failure. Prog Anat Sci. 2023;29(1):18-20,24.
  28. Zhou WM, Lang SK, Ge X, et al. Benzoylaconine alleviates oxygen-glucose deprivation/reoxygenation-induced cardiomyocyte injury via the phosphatidylinositol 3-kinase/protein kinase B pathway. Chin J Geriatr Heart Brain Vessel Dis. 2025;27(2):211-216.
  29. Brand CS, Lighthouse JK, Trembley MA. Protective transcriptional mechanisms in cardiomyocytes and cardiac fibroblasts. J Mol Cell Cardiol. 2019;132:1-12.
  30. Wang X, Chen ZQ, Li L, et al. Effects of Zhenwu Decoction on cardiomyocyte apoptosis and PI3K-AKT pathway in heart failure rats. Chin J Comp Med. 2022;32(7):27-33,57.
  31. Lou WZ, Huang SS, Chen BS. Mechanism analysis of Ginkgo biloba leaves against chronic heart failure. Zhejiang Clin Med J. 2026;28(1):32-37.
  32. Hanna A, Frangogiannis NG. Inflammatory cytokines and chemokines as therapeutic targets in heart failure. Cardiovasc Drugs Ther. 2020;34(6):849-863.
  33. Berger M, et al. IL-6 and hsCRP predict cardiovascular mortality in patients with heart failure with preserved ejection fraction. ESC Heart Fail. 2024;11(6):3607-3615.
  34. Zhang H, Dhalla NS. The role of pro-inflammatory cytokines in the pathogenesis of cardiovascular disease. Int J Mol Sci. 2024;25(2):1082.
  35. Wu PL, Wei M, Zhu W. Src signaling pathway and its role in the pathophysiology of heart failure. Int J Cardiovasc Dis. 2013;40(1):6-8,15.
  36. Li W, et al. Src tyrosine kinase promotes cardiac remodeling induced by chronic sympathetic activation. Biosci Rep. 2023;43(10):BSR20231097.
  37. Zhai Y, et al. Src-family protein tyrosine kinases: a promising target for treating cardiovascular diseases. Int J Med Sci. 2021;18(5):1216-1224.
  38. Qian C, Liu J, Liu H. Targeting estrogen receptor signaling for treating heart failure. Heart Fail Rev. 2024;29(1):125-131.
  39. Fukuma N, et al. Estrogen receptor-α non-nuclear signaling confers cardioprotection and is essential to cGMP-PDE5 inhibition efficacy. JACC Basic Transl Sci. 2020;5(3):282-295.
  40. Sun Y, et al. Signaling pathway of MAPK/ERK in cell proliferation, differentiation, migration, senescence and apoptosis. J Recept Signal Transduct Res. 2015;35(6):600-604.
  41. Zhang Z, et al. Targeting MAPK-ERK/JNK pathway: a potential intervention mechanism of myocardial fibrosis in heart failure. Biomed Pharmacother. 2024;173:116413.
  42. Deng S, et al. PI3K/AKT signaling tips the balance of cytoskeletal forces for cancer progression. Cancers (Basel). 2022;14(7):1652.
  43. Ghafouri-Fard S, et al. Interplay between PI3K/AKT pathway and heart disorders. Mol Biol Rep. 2022;49(10):9767-9781.
  44. Cardiovascular Branch of Chinese Medical Association, Cardiovascular Physicians Branch of Chinese Medical Doctor Association, Heart Failure Professional Committee of Chinese Medical Doctor Association, et al. 2024 Chinese guidelines for the diagnosis and treatment of heart failure. Chin J Cardiovasc Dis. 2024;52(3):235-275.
  45. Li TT, et al. Correlations of BNP, Hcy and blood lipid levels with cardiac function and prognosis in elderly patients with chronic heart failure. Chin J Evid Based Cardiovasc Med. 2021;13(9):1050-1053.
  46. Zhou L. Clinical study on TCM syndrome types, blood lipid levels and cardiac function in 150 patients with chronic heart failure. Guide China Med. 2024;22(12):77-79.
  47. Müller-Ehmsen J, Schwinger RH. TNF and congestive heart failure: therapeutic possibilities. Expert Opin Ther Targets. 2004;8(3):203-209.
  48. Arvunescu AM, et al. Inflammation in heart failure—future perspectives. J Clin Med. 2023;12(24):7738.
  49. Yu L, Feng Z. The role of Toll-like receptor signaling in the progression of heart failure. Mediators Inflamm. 2018;2018:9874109.
  50. Ye S, et al. Toll-like receptor 2 signaling deficiency in cardiac cells ameliorates Ang II-induced cardiac inflammation and remodeling. Transl Res. 2021;233:62-76.
  51. Vianello E, et al. The advanced glycation end-products (AGE)-receptor for AGE system (RAGE): an inflammatory pathway linking obesity and cardiovascular diseases. Int J Mol Sci. 2025;26(8):3707.

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