Evidence map›Paper›PMID 37274904›Full record

ArticleArchives of Razi Institute2022

A Non-Structural Protein of Severe Acute Respiratory Syndrome Coronavirus 2 with a Potential Ability to Reduce Blood Glucose for Use in Controlling Type 2 Diabetes Mellitus.

M Danaeifar

Open access · greenAbstract read
In one paragraph

Article in Archives of Razi Institute, 2022. 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
0.2field-weighted citation impact, top 46% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

1 author at 1 institution in 1 country.

M DanaeifarDepartment of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Tehran University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is estimated that more than 400 million people worldwide are suffering from diabetes. There are two types of diabetes. Type 1 diabetes is the result of insufficient insulin secretion into the bloodstream, most often due to an autoimmune attack on the pancreas glands. Type 2 diabetes is caused by the inability of the surface ligands to adsorb the insulin from the bloodstream. The conventional medicines for diabetes mellitus include sulfonylureas, biguanide, thiazolidinediones, alpha-glucosidase inhibitors, and meglitinide. By February 2022, Severe acute respiratory syndrome coronavirus 2 (SARSCoV2) had infected more than 391 million people worldwide, claiming 5.7 million lives and imposing heavy costs on the healthcare system. The present study aimed to assess the potential use of this non-structural SARS-CoV-2 protein in the treatment of type 2 diabetes mellitus. The nsp10 was structurally aligned with GoDrugBank therapeutic agents, and lixisenatide was found to have the most similar chemical structure. This drug is a glucagon-like peptide-1 (GLP1) receptor agonist used for the treatment of type 2 diabetes mellitus. The best molecular docking energy score for these two proteins was -301.47, and the ligand root mean square deviation was calculated to be 107.93 Å. The molecular dynamics for the stability of the nsp10 and GLP1R binding in triplicate for 150 ns demonstrated that the nsp10-GLP1R remained bound for more than 80 ns. This study indicated that the nsp10 protein can be further studied to be used as an antidiabetic medication.

Indexed as

COVID-19Diabetes Mellitus, Type 2AnimalsBlood GlucoseHumansMolecular Docking SimulationRatsRNA, ViralSARS-CoV-2Blood GlucoseRNA, Viraldiabetes mellitusnsp10protein dockingSARS-CoV-2

Identifiers

PMID37274904
PMCPMC10237550
OpenAlexW4379508385

What OpenQuestion holds

Textmetadata
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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.