Evidence map›Paper›PMID 33847347›Full record

ArticleBriefings in bioinformatics2021

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.

S M Hasan Mahmud, Md Al-Mustanjid, Farzana Akter, Md Shazzadur Rahman, Kawsar Ahmed, Md Habibur Rahman, Wenyu Chen, Mohammad Ali Moni

Open access · hybridAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 1 pooled it
12.8field-weighted citation impact, top 1% 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

62 citing papers in PubMed, 1 synthesis or guideline pooled it, 96 citations in OpenAlex.

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2 more citing papers are in PubMed but not listed here.

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

8 authors at 5 institutions in 3 countries.

S M Hasan MahmudComputer Science and Technology from the University of Electronic Science and Technology of China, China.
Md Al-MustanjidDaffodil International University, Bangladesh.
Farzana AkterComputer Science and Engineering from Daffodil International University, Bangladesh.
Md Shazzadur RahmanDaffodil International University, Bangladesh.
Kawsar AhmedInformation and Communication Technology (ICT) at Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
Md Habibur RahmanInstitute of Automation, Chinese Academy of Sciences, Beijing, China.
Wenyu ChenUniversity of Electronic Science and Technology of China, China.
Mohammad Ali MoniUniversity of New South Wales, Australia.
Daffodil International University · BDUniversity of Electronic Science and Technology of China · CNChinese Academy of Sciences · CNMawlana Bhashani Science and Technology University · BDUNSW Sydney · AU

Funding

National Natural Science Foundation of China 61772115
6 · The paper itself

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

Indexed as

Computational BiologyCOVID-19HumansIdiopathic Pulmonary FibrosisProtein Interaction MapsPulmonary Disease, Chronic ObstructiveSARS-CoV-2Systems Biologychronic obstructive pulmonary diseasedifferentially expressed genesdrug moleculegene ontologyhub geneidiopathic pulmonary fibrosisprotein–protein interaction (PPI)SARS-CoV-2

Identifiers

PMID33847347
PMCPMC8083324
OpenAlexW3154144457

What Socratic 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 Socratic graph.