Evidence map›Paper›PMID 33263093›Full record

ArticleGene reports2021

Gene expression profiling of corona virus microarray datasets to identify crucial targets in COVID-19 patients.

Priyanka Ramesh, Shanthi Veerappapillai, Ramanathan Karuppasamy

Open access · greenAbstract read
In one paragraph

Article in Gene reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.3field-weighted citation impact, top 28% 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

10 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Classification of COVID19 Patients Using Robust Logistic Regression.Journal of statistical theory and practice · 2022
    Article
  8. Alterations in Immune-Related Defensin Alpha 4 (International journal of inflammation · 2022
    Review
  9. Article
  10. Network-Based Analysis of Fatal Comorbidities of COVID-19 and Potential Therapeutics.IEEE/ACM transactions on computational biology and bioinformatics
    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

3 authors at 1 institution in 1 country.

Priyanka RameshDepartment of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Shanthi VeerappapillaiDepartment of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Ramanathan KaruppasamyDepartment of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Vellore Institute of Technology University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current outbreak of coronavirus disease (COVID-19) has been affecting millions of people and has caused devastating mortality worldwide. Moreover, it is to be noted that cytokine storm has become an important cause for the rising mortality. However, the efforts for the development of drugs, vaccines and treatment has also been intervened due to poor understanding of host's defense mechanism and also due to the development of cytokine storm against this viral infection. Thus, a deeper understanding of the mechanism behind the immune dysregulation and cytokine storm development might give us clues for the clinical management of the severe cases. Hence, we have implemented differential gene expression analysis together with protein-protein interaction and Gene Ontology (GO) studies with the help of Severe Acute respiratory syndrome coronavirus (SARS-CoV) data sets such as GSE1739 and GSE33267 to give us more knowledge on the host immune response for the pathogenic coronavirus which in turn reduces the mortality. A total of 79 differentially-expressed genes (DEGs) were identified in our data set using the filters such as

Indexed as

Cytokine stormHub gene identificationMicroarray dataSARS-CoV-2STRING database

Identifiers

PMID33263093
PMCPMC7691848
OpenAlexW3108622358

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.