Evidence map›Paper›PMID 40007160›Full record

ArticleBriefings in bioinformatics2024

Revealing the antimicrobial potential of traditional Chinese medicine through text mining and molecular computation.

Meng-Chi Chung, Li-Jen Su, Chien-Lin Chen, Li-Ching Wu

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

  1. Article
  2. Review
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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

4 authors.

Meng-Chi ChungDepartment of Biomedical Science and Engineering, National Central University (NCU), Jhong-Li City, Taiwan, (ROC).ORCID 0009-0005-4857-5852
Li-Jen SuDepartment of Biomedical Science and Engineering, National Central University (NCU), Jhong-Li City, Taiwan, (ROC).
Chien-Lin ChenIIHMED Reproductive Center, Taipei, Taiwan, (ROC).
Li-Ching WuDepartment of Biomedical Science and Engineering, National Central University (NCU), Jhong-Li City, Taiwan, (ROC).

Funding

National Science and Technology Council 112-2221-E-008-079
6 · The paper itself

Abstract

Traditional Chinese Medicine (TCM), with its extensive knowledge base documented in ancient texts, offers a unique resource for contemporary drug discovery, particularly in combatting microbial infections. The success of antimalarial drugs like artemisinin and artesunate, derived from the TCM herb Artemisia annua L., exemplifies the potential of TCM-derived small molecules. This rich repository of natural products and intricate molecular structures could reveal novel compounds with unexplored mechanisms of action. Our study employs a multifaceted approach that combines text mining, detailed textual analysis, and modern antibacterial molecular prediction methodologies to unlock the potential of ancient TCM remedies. We use external knowledge maps, which include databases of known bioactive compounds and their targets, to identify promising TCM candidates. This approach leverages both historical texts and contemporary scientific data to explore the therapeutic potential of TCM. We discovered that herb patterns DiYu→ZeXie and Kushen→ShengJiang potentially combat both Grams-positive and Grams-negative bacteria. We utilized the AntiBac-Pred online tool to identify and analyze the chemical components of herbs, integrating data from ancient texts and TCMDB@Taiwan external knowledge graph. The DiYu→ZeXie groups showed antimicrobial potential against resistant Staphylococcus simulans, while the Kushen→ShengJiang groups exhibited dual antimicrobial effects against Bacillus subtilis. Exploring TCM's extensive repository offers numerous opportunities for discovering therapeutically active compounds. Our synergistic approach, which combines ancient wisdom with modern science, holds significant promise for enhancing our ability to combat infectious diseases. This method could pave the way for a new era of personalized medicine, addressing the urgent need for innovative treatments against multidrug-resistant bacteria and viruses.

Indexed as

Anti-Bacterial AgentsAnti-Infective AgentsData MiningDrugs, Chinese HerbalMedicine, Chinese TraditionalComputational BiologyDrug DiscoveryHumansAnti-Bacterial AgentsAnti-Infective AgentsDrugs, Chinese Herbalanti-microbial TCMassociation ruledrug discoveryTCMtext mining

Identifiers

PMID40007160
PMCPMC11859959

What OpenQuestion holds

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LicenceCC BY-NC
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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.