Evidence map›Paper›PMID 33324213›Full record

ArticleFrontiers in pharmacology2020

Deciphering the Pharmacological Mechanisms of Ma Xing Shi Gan Decoction against COVID-19 through Integrating Network Pharmacology and Experimental Exploration.

Qianqian Li, Chen Bai, Ruocong Yang, Weiying Xing, Xiaohan Pang, Siying Wu, Shaoyang Liu, Jianxin Chen, Tiegang Liu, Xiaohong Gu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 22% of its field
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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Harnessing network pharmacology in drug discovery: an integrated approach.Naunyn-Schmiedeberg's archives of pharmacology · 2025
    Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
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  13. Article
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  15. A comprehensive review on efficient approaches for combating coronaviruses.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021
    Review
  16. The direct evidence and mechanism of traditional Chinese medicine treatment of COVID-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021
    Review
  17. Article
  18. Article
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

10 authors at 1 institution in 1 country.

Qianqian LiSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Chen BaiSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Ruocong YangSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Weiying XingSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Xiaohan PangSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Siying WuSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Shaoyang LiuSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Jianxin ChenSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Tiegang LiuSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Xiaohong GuSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Beijing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of new infectious pneumonia caused by SARS-CoV-2 has posed a significant threat to public health, but specific medicines and vaccines are still being developed. Traditional Chinese medicine (TCM) has thousands of years of experience in facing the epidemic disease, such as influenza and viral pneumonia. In this study, we revealed the efficacy and pharmacological mechanism of Ma Xing Shi Gan (MXSG) Decoction against COVID-19. First, we used liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) to analyze the chemical components in MXSG and identified a total of 97 components from MXSG. Then, the intervention pathway of MXSG based on these components was analyzed with network pharmacology, and it was found that the pathways related to the virus infection process were enriched in some of MXSG component targets. Simultaneously, through literature research, it was preliminarily determined that MXSG, which is an essential prescription for treating COVID-19, shared the feature of antiviral, improving clinical symptoms, regulating immune inflammation, and inhibiting lung injury. The regulatory mechanisms associated with its treatment of COVID-19 were proposed. That MXSG might directly inhibit the adsorption and replication of SARS-CoV-2 at the viral entry step. Besides, MXSG might play a critical role in inflammation and immune regulatory, that is, to prevent cytokine storm and relieve lung injury through toll-like receptors signaling pathway. Next, in this study, the regulatory effect of MXSG on inflammatory lung injury was validated through transcriptome results. In summary, MXSG is a relatively active and safe treatment for influenza and viral pneumonia, and its therapeutic effect may be attributed to its antiviral and anti-inflammatory effects.

Indexed as

antiviralCOVID-19immunomodulatoryMa Xing Shi Gan decoctionTraditional Chinese Medicine

Identifiers

PMID33324213
PMCPMC7725906
OpenAlexW3109054025

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.