Evidence map›Paper›PMID 34260780›Full record

SynthesisReviews in medical virology2021

A systematic meta-analysis of immune signatures in patients with COVID-19.

Kun Liu, Tong Yang, Xue-Fang Peng, Shou-Ming Lv, Xiao-Lei Ye, Tian-Shuo Zhao, Jia-Chen Li, Zhong-Jun Shao, Qing-Bin Lu, Jing-Yun Li and 1 more

Open access · bronzeAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Reviews in medical virology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 5 pooled it
1.5field-weighted citation impact, top 10% 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

38 citing papers in PubMed, 5 syntheses or guidelines pooled it, 72 citations in OpenAlex.

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  10. [Early prediction of severe COVID-19 in patients with Sjögren's syndrome].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2023
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  12. Trends in the Epidemiology ofJournal of fungi (Basel, Switzerland) · 2023
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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

11 authors at 3 institutions in 2 countries.

Kun LiuDepartment of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China.
Tong YangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.ORCID https://orcid.org/0000-0002-2276-8534
Xue-Fang PengState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.ORCID https://orcid.org/0000-0002-4817-3117
Shou-Ming LvState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Xiao-Lei YeState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Tian-Shuo ZhaoDepartment of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China.
Jia-Chen LiState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Zhong-Jun ShaoDepartment of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China.
Qing-Bin LuDepartment of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China.ORCID https://orcid.org/0000-0002-2804-0827
Jing-Yun LiState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Wei LiuState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.ORCID https://orcid.org/0000-0002-9302-8170
Institute of Microbiology · CNPeking University · CNMinistry of Education · KR

Funding

China Mega-Project on Infectious Disease Prevention 2017ZX10103004China Mega-Project on Infectious Disease Prevention 2018ZX10101003China Mega-Project on Infectious Disease Prevention 2018ZX10713002National Natural Science Foundation of China 81803289National Natural Science Foundation of China 81825019
6 · The paper itself

Abstract

Currently severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been on the rise worldwide. Predicting outcome in COVID-19 remains challenging, and the search for more robust predictors continues. We made a systematic meta-analysis on the current literature from 1 January 2020 to 15 August 2020 that independently evaluated 32 circulatory immunological signatures that were compared between patients with different disease severity was made. Their roles as predictors of disease severity were determined as well. A total of 149 distinct studies that evaluated ten cytokines, four antibodies, four T cells, B cells, NK cells, neutrophils, monocytes, eosinophils and basophils were included. Compared with the non-severe patients of COVID-19, serum levels of Interleukins (IL)-2, IL-2R, IL-4, IL-6, IL-8, IL-10 and tumor necrosis factor α were significantly up-regulated in severe patients, with the largest inter-group differences observed for IL-6 and IL-10. In contrast, IL-5, IL-1β and Interferon (IFN)-γ did not show significant inter-group difference. Four mediators of T cells count, including CD3

Indexed as

Antibodies, ViralB-LymphocytesCOVID-19CytokinesHumansKiller Cells, NaturalLeukocytesSARS-CoV-2Severity of Illness IndexT-LymphocytesAntibodies, ViralCytokinesantibodyCOVID-19cytokinesdisease severityinnate immunitymeta-analysis

Identifiers

PMID34260780
PMCPMC7744845
OpenAlexW3202299808

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.