Abstracting and Indexing

  • PubMed NLM
  • Google Scholar
  • Semantic Scholar
  • Scilit
  • CrossRef
  • WorldCat
  • ResearchGate
  • Academic Keys
  • DRJI
  • Microsoft Academic
  • Academia.edu
  • OpenAIRE
  • Scribd
  • Baidu Scholar

Network Meta-Analysis on the Mechanisms underlying Type 2 Diabetes Augmentation of COVID-19 Pathologies

Author(s): Ryan J. Kim, Mohammed AS Khan, Maryam Khan, Sulie L. Chang

Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. SARS-CoV-2 virus is internalized by surface receptors, e.g., angiotensin-converting enzyme-2 (ACE2). Clinical reports suggest that non-insulin dependent diabetes mellitus (DM-II) may enhance COVID-19. This bioinformatics study investigated how DM-II augments COVID-19 complications through molecular interactions with cytokines/chemokines, using QIAGEN Ingenuity Pathway Analysis (IPA) and CLC Genomics Workbench 22 (CLCG-22). “(Iβ-CG) RNA-sequencing of (Iβ-CG) through CLCG-22 (SRA SRP287500) were analyzed to identify differential expression of (Iβ-CG). IPA’s QIAGEN Knowledge Base (QKB) was also used to retrieve 88 total molecules shared between DM-II and SARS-CoV-2 infection to characterize and identify Iβ-CG, due to close association with DM-II. Molecules directly associated with ACE2 and cytokines/chemokines were also identified for their association with SARS-CoV-2 infection. Using IPA, it was found that 3 Ib-CG (SCL2A2, PPARγ, and CPLX8) are common in both diseases that were downregulated by DM-II. Their downregulation occurred due to increased activity of cytokines/chemokines and ACE2. Collectively, this network meta-analysis demonstrated that interaction of SARS-CoV-2 with ACE2 could primarily induce endothelial cell dysfunction. Identification of common molecules and signaling pathways between DM-II and SARS-CoV-2 infection in this study may lead to further discovery of therapeutic measures to simultaneously combat both diseases.

Journal Statistics

Impact Factor: * 3.1

CiteScore: 2.9

Acceptance Rate: 71.36%

Time to first decision: 10.4 days

Time from article received to acceptance: 2-3 weeks

Discover More: Recent Articles

Grant Support Articles

    Editor In Chief

    Masashi Emoto

  • Professor of Laboratory of Immunology
    Department of Laboratory Sciences
    Gunma University Graduate School of Health Sciences
    Gunma, Japan

© 2016-2024, Copyrights Fortune Journals. All Rights Reserved!