Evidence map›Paper›PMID 32912711›Full record

ReviewPrimary care diabetes2021

COVID-19 and diabetes; Possible role of polymorphism and rise of telemedicine.

Shomoita Sayed

Abstract readReview
In one paragraph

Review in Primary care diabetes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–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

14 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Impact of the COVID-19 Pandemic on Type 2 Diabetes Care Delivery.Clinical diabetes : a publication of the American Diabetes Association · 2022
    Article
  8. Review
  9. Review
  10. COVID-19 Pandemic Influence on Diabetes Management in Croatia.Frontiers in clinical diabetes and healthcare · 2021
    Article
  11. Review
  12. Article
  13. Article
  14. Review
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.

Shomoita SayedDepartment of Mathematics and Natural Science, Brac University, 66 Mohakhali, Dhaka-1212, Bangladesh. Electronic address: shomoita.sayed@bracu.ac.bd.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes has been found to be one of the leading comorbidities associated with fatality in COVID-19 patients. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry is facilitated by interaction with Angiotensin Converting Enzyme-2 (ACE2) and possible polymorphisms in ACE2 can be a determining factor in host-viral protein interaction. A significant shift of healthcare towards 'Telemedicine' is also on the rise. In this review, the possible effects of ACE2 polymorphisms on SARS-CoV-2 entry along with the escalation of 'telemedicine' is discussed.

methodAn expansive literature search using keywords: "COVID-19", "SARS-CoV-2", "diabetes", "type 2 diabetes'', "type 1 diabetes", "ACE2", "polymorphism", "DPP4" and "telemedicine" was conducted on Pubmed and EMBASE till 7th August 2020.

resultPossible polymorphisms in ACE2 gene can play a role in influencing the virus entry in host body. Telemedicine can bring a new revolution for medical sector.

conclusionCOVID-19 severity is more heinous among diabetic population. So far, the in-silico studies involving human ACE2-viral Spike (S) interaction showed inconsistent predictions regarding some SNPs. But without actual in-vivo studies, a holistic understanding can't be established.

Indexed as

PandemicsPolymorphism, GeneticAngiotensin-Converting Enzyme 2ComorbidityCOVID-19Diabetes Mellitus, Type 2DNAHumansSARS-CoV-2TelemedicineACE2 protein, humanAngiotensin-Converting Enzyme 2DNAACE2COVID-19DiabetesPolymorphismSNPTelemedicine

Identifiers

PMID32912711
PMCPMC7457905

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.