Evidence map›Paper›PMID 37228247›Full record

ArticleArabian journal of chemistry2023

Screening compounds for treating the diabetes and COVID-19 from Miao medicine by molecular docking and bioinformatics.

Feng Xu, Xulong Huang, Hongmei Wu, Xiangpei Wang

Open access · goldAbstract read
In one paragraph

Article in Arabian journal of chemistry, 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
1.2field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Feng XuDepartment of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang 550025, Guizhou Province, PR China.
Xulong HuangDepartment of Pharmacy, Chengdu University of Traditional Chinese Medicine, Guiyang 550025, Guizhou Province, PR China.
Hongmei WuDepartment of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang 550025, Guizhou Province, PR China.
Xiangpei WangSchool of Chinese Ethnic Medicine, Guizhou Minzu University, Guiyang 550025, Guizhou Province, PR China.
Guizhou University · CNChengdu University of Traditional Chinese Medicine · CNGuizhou Minzu University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Both diabetes and Corona Virus Disease 2019 (COVID-19) are seriously harmful to human health, and they are closely related. It is of great significance to find drugs that can simultaneously treat diabetes and COVID-19. Based on the theory of traditional Chinese medicine for treating COVID-19, this study first sorted out the compounds of Guizhou Miao medicine with "return to the lung channel" and "clear heat and detoxify" effects in China. The active components against COVID-19 were screened by molecular docking with SARS-CoV-2 PLpro and angiotensin-converting enzyme II as targets. Furthermore, the common target dipeptidyl peptidase 4 (DPP4) of diabetes and COVID-19 was used as a screening protein, and molecular docking was used to obtain potential components for the treatment of diabetes and COVID-19. Finally, the mechanism of potential ingredients in the treatment of diabetes and COVID-19 was explored with bioinformatics. More than 80 kinds of Miao medicine were obtained, and 584 compounds were obtained. Further, 110 compounds against COVID-19 were screened, and top 6 potential ingredients for the treatment of diabetes and COVID-19 were screened, including 3-O-β-D-Xylopyranosyl-(1-6)-β-D-glucopyranosyl-(1-6)-β-D-glucopyranosyl oleanolic acid 28-O-β-D-glucopyranosyl ester, Glycyrrhizic acid, Sequoiaflavone, 2-O-Caffeoyl maslinic acid, Pholidotin, and Ambewelamide A. Bioinformatics analysis found that their mechanism of action in treating diabetes and COVID-19 may be related to regulating the expression of DPP4, angiotensin II type 1 receptor, vitamin D receptor, plasminogen, chemokine C-C-motif receptor 6, and interleukin 2. We believe that Guizhou Miao medicine is rich in potential ingredients for the treatment of diabetes and COVID-19.

Indexed as

Angiotensin-converting enzyme IICorona Virus Disease 2019DiabetesDipeptidyl peptidase 4Miao MedicineMolecular dockingSARS-CoV-2 3CL hydrolase protein

Identifiers

PMID37228247
PMCPMC10191702
OpenAlexW4376959700

What OpenQuestion holds

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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.