Evidence map›Paper›PMID 35799708›Full record

ArticleImmune network2022

Integration and Reanalysis of Four RNA-Seq Datasets Including BALF, Nasopharyngeal Swabs, Lung Biopsy, and Mouse Models Reveals Common Immune Features of COVID-19.

Rudi Alberts, Sze Chun Chan, Qian-Fang Meng, Shan He, Lang Rao, Xindong Liu, Yongliang Zhang

Open access · diamondAbstract read
In one paragraph

Article in Immune network, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-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

5 citing papers in PubMed, 9 citations in OpenAlex.

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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

7 authors at 4 institutions in 2 countries.

Rudi AlbertsDepartment of Microbiology and Immunology, NUSMED Immunology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.ORCID https://orcid.org/0000-0002-3654-4873
Sze Chun ChanDepartment of Microbiology and Immunology, NUSMED Immunology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.ORCID https://orcid.org/0000-0003-0674-8629
Qian-Fang MengShengzhen Bay Laboratory, Shenzhen 518132, P. R. China.
Shan HeSchool of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore.
Lang RaoShengzhen Bay Laboratory, Shenzhen 518132, P. R. China.ORCID https://orcid.org/0000-0001-5010-0729
Xindong LiuInstitute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, P. R. China.ORCID https://orcid.org/0000-0002-2465-0337
Yongliang ZhangDepartment of Microbiology and Immunology, NUSMED Immunology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.ORCID https://orcid.org/0000-0002-4964-1285
National University of Singapore · SGShenzhen Bay Laboratory · CNArmy Medical University · CNNanyang Technological University · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has spread over the world causing a pandemic which is still ongoing since its emergence in late 2019. A great amount of effort has been devoted to understanding the pathogenesis of COVID-19 with the hope of developing better therapeutic strategies. Transcriptome analysis using technologies such as RNA sequencing became a commonly used approach in study of host immune responses to SARS-CoV-2. Although substantial amount of information can be gathered from transcriptome analysis, different analysis tools used in these studies may lead to conclusions that differ dramatically from each other. Here, we re-analyzed four RNA-sequencing datasets of COVID-19 samples including human bronchoalveolar lavage fluid, nasopharyngeal swabs, lung biopsy and hACE2 transgenic mice using the same standardized method. The results showed that common features of COVID-19 include upregulation of chemokines including

Indexed as

COVID-19CytokinesHost-virus interactionsImmune responseInnate immunityRNA-seq datasets

Identifiers

PMID35799708
PMCPMC9250867
OpenAlexW4284963828

What OpenQuestion holds

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LicenceCC BY-NC
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.