Evidence map›Paper›PMID 41573742›Full record

ArticleFrontiers in molecular biosciences2025

Herbal monomer-based therapeutic insights from traditional Chinese medicine in myocardial infarction.

Nan Bian, Libo Xia, Xianqiu Xiong, Yingyu Chen, Ying Chen

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nan BianChangchun University of Chinese Medicine, Changchun, Jilin, China.
Libo XiaThe First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, Jilin, China.
Xianqiu XiongGuizhou Institute of Precision Medicine, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Yingyu ChenSchool of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, China.
Ying ChenChangchun University of Chinese Medicine, Changchun, Jilin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myocardial infarction (MI) is a leading cause of morbidity and mortality globally, often resulting in heart failure due to adverse cardiac remodeling triggered by inflammation and fibrosis. Traditional Chinese Medicine (TCM), particularly compounds like Quercetin from Licorice and Peony, has shown promise in modulating inflammation and oxidative stress in cardiovascular diseases. This study integrates bioinformatics and experimental validation to explore the therapeutic potential of Quercetin in MI. Using Mendelian Randomization (MR) and colocalization analysis, we identified key MI-related genes, such as VEGFA, PTK2, and GGT1, whose expression is influenced by Quercetin. Bioinformatics tools predicted these genes as targets of Quercetin, with molecular docking revealing stable interactions between the compound and these genes. Single-cell RNA-sequencing of MI samples confirmed the expression of these genes in cardiac muscle cells (CMs) and macrophages, highlighting their role in tissue repair and inflammation. In experimental models, Quercetin treatment significantly altered the expression of these genes, enhancing myocardial cell recovery and reducing infarct size. This study provides molecular insights into how Quercetin and other TCM compounds could modulate critical pathways involved in MI recovery, supporting their potential as adjunct therapies. The findings bridge traditional medicine with modern bioinformatics, opening new avenues for therapeutic strategies to improve cardiac function and patient outcomes in MI.

Indexed as

Mendelian randomizationmyocardial infarctionQuercetinsingle-cell RNA-sequencetraditional Chinese medicine

Identifiers

PMID41573742
PMCPMC12819195

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.