Evidence map›Paper›PMID 35990842›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2022

Exploring the Mechanism of Aspirin in the Treatment of Kawasaki Disease Based on Molecular Docking and Molecular Dynamics.

Li Xiong, Junfeng Cao, Yixin Qiu, Yinyin Fu, Siyi Chen, Mengjia He, Shengyan Chen, Wei Xie, Xingyu Yang, Chaochao Wang and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2022. 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
0.7field-weighted citation impact, top 31% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

14 authors at 1 institution in 1 country.

Li XiongClinical Medicine, Chengdu Medical College, Chengdu, China.
Junfeng CaoClinical Medicine, Chengdu Medical College, Chengdu, China.ORCID https://orcid.org/0000-0002-4417-1771
Yixin QiuClinical Medicine, Chengdu Medical College, Chengdu, China.
Yinyin FuClinical Medicine, Chengdu Medical College, Chengdu, China.
Siyi ChenClinical Medicine, Chengdu Medical College, Chengdu, China.
Mengjia HeClinical Medicine, Chengdu Medical College, Chengdu, China.
Shengyan ChenClinical Medicine, Chengdu Medical College, Chengdu, China.
Wei XieClinical Medicine, Chengdu Medical College, Chengdu, China.
Xingyu YangClinical Medicine, Chengdu Medical College, Chengdu, China.
Chaochao WangClinical Medicine, Chengdu Medical College, Chengdu, China.
Mei WuClinical Medicine, Chengdu Medical College, Chengdu, China.
Hengxiang XuClinical Medicine, Chengdu Medical College, Chengdu, China.
Yijun ChenClinical Medicine, Chengdu Medical College, Chengdu, China.
Xiao ZhangCenter for Experimental Technology of Preclinical Medicine, Chengdu Medical College, Chengdu, China.ORCID https://orcid.org/0000-0002-4485-9752
Chengdu Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The research aims to investigate the mechanism of action of aspirin in the treatment of Kawasaki disease. Methods: We predicted the targets of aspirin with the help of the Drugbank and PharmMapper databases, the target genes of Kawasaki disease were mined in the GeneCards and Disgenet databases, the intersection targets were processed in the Venny database, and the gene expression differences were observed in the GEO database. The Drugbank and PharmMapper databases were used to predict the target of aspirin, and the target genes of Kawasaki disease were explored in the GeneCards and Disgenet databases, and the Venny was used for intersection processing. We observed the gene expression differences in the GEO database. The disease-core gene target-drug network was established and molecular docking was used for verification. Molecular dynamics simulation verification was carried out to combine the active ingredient and the target with a stable combination. The supercomputer platform was used to measure and analyze the binding free energy, the number of hydrogen bonds, the stability of the protein target at the residue level, the radius of gyration, and the solvent accessible surface area. Results: Aspirin had 294 gene targets, Kawasaki disease had 416 gene targets, 42 intersecting targets were obtained, we screened 13 core targets by PPI; In the GO analysis, we learned that the biological process of Kawasaki disease involved the positive regulation of chemokine biosynthesis and inflammatory response; pathway enrichment involved PI3K-AKT signaling pathway, tumor necrosis factor signaling pathway, etc. After molecular docking, the data showed that CTSG, ELANE, and FGF1 had the best binding degree to aspirin. Molecular dynamics was used to prove and analyze the binding stability of active ingredients and protein targets, and Aspirin/ELANE combination has the strongest binding energy. Conclusion: In the treatment of Kawasaki disease, aspirin may regulate inflammatory response and vascular remodeling through CTSG, ELANE, and FGF1.

Identifiers

PMID35990842
PMCPMC9391120
OpenAlexW4290964031

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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.