Evidence map›Paper›PMID 32714405›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2020

A Network Pharmacology Approach to Investigate the Active Compounds and Mechanisms of Musk for Ischemic Stroke.

Changlin Zhang, Yingdi Liao, Lingling Liu, Yifan Sun, Shaoqin Lin, Jiaying Lan, Hui Mao, Haoxuan Chen, Yuanqi Zhao

Abstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. The Mechanism ofEvidence-based complementary and alternative medicine : eCAM · 2022
    Article
  10. Exploring Molecular Mechanisms ofEvidence-based complementary and alternative medicine : eCAM · 2022
    Article
  11. Article
  12. Article
  13. Musk (Frontiers in behavioral neuroscience · 2021
    Article
  14. Article
  15. 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

9 authors.

Changlin ZhangThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.ORCID https://orcid.org/0000-0001-6289-2639
Yingdi LiaoThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Lingling LiuThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yifan SunThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Shaoqin LinThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Jiaying LanThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Hui MaoThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Haoxuan ChenThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yuanqi ZhaoThe Second Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China.ORCID https://orcid.org/0000-0003-1254-6732

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aims to study the material basis and effective mechanism of musk for ischemic stroke (IS) based on the network pharmacology approach.

methodsWe collected the chemical components and target gene of musk from the BATMAN-TCM analytical platform and identified ischemic stroke-related targets from the following databases: DisGeNET, NCBI-Gene, HPO, OMIM, DrugBank, and TTD. The targets of musk and IS were uploaded to the String database to construct the protein-protein interaction (PPI) network, and then, the key targets were analyzed by topological methods. At last, the function biological process and signaling pathways of key targets were carried out by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and cluster analysis by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server and Metascape platform.

resultsA total of 29 active compounds involving 1081 predicted targets were identified in musk and there were 1104 IS-related targets. And 88 key targets of musk for IS were obtained including AKT1, MAPK1/3, TP53, TNF, SRC, FOS, CASP3, JUN, NOS3, and IL1B. The GO and KEGG enrichment analysis suggested that these key targets are mainly involved in multiple pathways which participated in TNF signaling pathway, estrogen signaling pathway, prolactin signaling pathway, neurotrophin signaling pathway, T-cell receptor signaling pathway, cAMP signaling pathway, FoxO signaling pathway, and HIF1 signaling pathway.

conclusionThis study revealed that the effective mechanisms of musk against IS would be associated with the regulation of apoptosis, inflammatory response, and gene transcription.

Identifiers

PMID32714405
PMCPMC7354650

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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.