Evidence map›Paper›PMID 41674538›Full record

ArticleQuantitative biology (Beijing, China)2023

Network pharmacology research of mechanism of Maxing Shigan Decoction in treating COVID-19.

Xian-Fang Wang, Zhi-Yong Du, Qi-Meng Li, Yi-Feng Liu, Shao-Hui Ma, Jui-Wei Cui

Abstract read
In one paragraph

Article in Quantitative biology (Beijing, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Xian-Fang WangSchool of Computer Science and Technology Henan Institute of Technology Xinxiang 453003 China.
Zhi-Yong DuSchool of Computer Science and Technology Henan Institute of Technology Xinxiang 453003 China.
Qi-Meng LiSchool of Computer and Information Engineering Henan Normal University Xinxiang 453003 China.
Yi-Feng LiuSchool of Computer Science and Technology Henan Institute of Technology Xinxiang 453003 China.
Shao-Hui MaSchool of Computer Science and Technology Henan Institute of Technology Xinxiang 453003 China.
Jui-Wei CuiSchool of the First Clinical Xinxiang Medical University Xinxiang 453003 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The COVID-19 has a huge negative impact on people's health. Traditional Chinese Medicine (TCM) has a good effect on viral pneumonia. It is of great practical significance to study its pharmacology. Methods: The ingredients and targets of each herb in Maxing Shigan Decoction which obtained from Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and the related targets of COVID-19 were screened by GeneCards database based on the network pharmacology. Venn was used to analyze the intersection target between active ingredients and diseases. Cytoscape software was used to construct an active ingredient-disease target network. The Protein-Protein Interaction network was constructed by STRING database and Cytohubba was used to screen out the key targets. Gene Ontology (GO) functional enrichment analysis and KEGG pathway analysis were performed by David database. Results: In this study, a total of 134 active ingredients and 229 related targets, 198 targets of COVID-19 and 48 common targets of drug-disease were chosen. Enrichment items and pathways were obtained through GO and KEGG pathway analysis. The predicted active ingredients were quercetin, kaempferol, luteolin, naringenin, glycyrol, and the key targets involved IL6, MAPK3, MAPK8, CASP3, IL10, Conclusions: The main contribution of this paper is to use data to mine the principles of the treatment of COVID-19 from the pharmacology of these prescriptions, and the results can be provided theoretical reference for medical workers.

Indexed as

COVID‐19Maxing Shigan Decoctionnetwork pharmacologyPPItarget

Identifiers

PMID41674538
PMCPMC12807097

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.