Evidence map›Paper›PMID 34817280›Full record

ArticleMicrobiology spectrum2021

Plasma gp96 is a Novel Predictive Biomarker for Severe COVID-19.

Rongguo Wei, Biyan Zhou, Shaohua Li, Debin Zhong, Boan Li, Jianqiu Qin, Liping Zhao, Lixian Qin, Jun Hu, Jiuru Wang and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Microbiology spectrum, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 50% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. SARS-CoV-2 Infection and Candidate Biomarkers.The Eurasian journal of medicine · 2022
    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

13 authors at 3 institutions in 1 country.

Rongguo Wei *CAS Key Laboratory of Pathogenic Microbiology and Immunology, Center for Biosafety Mega-Science, Institute of Microbiology, Chinese Academy of Sciencesgrid.9227.e, Beijing, China.
Biyan Zhou *Department of Clinical Laboratory, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China.
Shaohua Li *Department of Pathology and Hepatology, The 5th Medical Centre, Chinese PLA General Hospital, Beijing, China.
Debin ZhongDepartment of Clinical Laboratory, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China.
Boan LiDepartment of Clinical Laboratory, The 5th Medical Centre, Chinese PLA General Hospital, Beijing, China.
Jianqiu QinNanning Municipal Center for Disease Control and Prevention, Nanning, China.
Liping ZhaoDepartment of Clinical Laboratory, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China.
Lixian QinDepartment of Clinical Laboratory, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China.
Jun HuCAS Key Laboratory of Pathogenic Microbiology and Immunology, Center for Biosafety Mega-Science, Institute of Microbiology, Chinese Academy of Sciencesgrid.9227.e, Beijing, China.
Jiuru WangCAS Key Laboratory of Pathogenic Microbiology and Immunology, Center for Biosafety Mega-Science, Institute of Microbiology, Chinese Academy of Sciencesgrid.9227.e, Beijing, China.
Shixiong YangNanning Municipal Center for Disease Control and Prevention, Nanning, China.
Jingming ZhaoDepartment of Pathology and Hepatology, The 5th Medical Centre, Chinese PLA General Hospital, Beijing, China.
Songdong MengCAS Key Laboratory of Pathogenic Microbiology and Immunology, Center for Biosafety Mega-Science, Institute of Microbiology, Chinese Academy of Sciencesgrid.9227.e, Beijing, China.ORCID 0000-0001-5670-1533
Guangxi Medical University · CNChinese Academy of Sciences · CNChinese PLA General Hospital · CN

Funding

Beijing Natural Science Foundation-Haidian Primitive Innovation Joint Fund L202025Guangxi Medical and Health Key Research Project| Guangxi Zhuang Autonomous Region Health and Family Planning Commission () Z20200208National Natural Science Foundation of China (NSFC) 32070163Strategic Priority Research Program of the Chinese Academy of science XDB29040000
6 · The paper itself

Abstract

Early and effective identification of severe coronavirus disease 2019 (COVID-19) may allow us to improve the outcomes of associated severe acute respiratory illness with fever and respiratory symptoms. This study analyzed plasma concentrations of heat shock protein gp96 in nonsevere (including mild and typical) and severe (including severe and critical) patients with COVID-19 to evaluate its potential as a predictive and prognostic biomarker for disease severity. Plasma gp96 levels that were positively correlated with interleukin-6 (IL-6) levels were significantly elevated in COVID-19 patients admitted to the hospital but not in non-COVID-19 patients with less severe respiratory impairment. Meanwhile, significantly higher gp96 levels were observed in severe than nonsevere patients. Moreover, the continuous decline of plasma gp96 levels predicted disease remission and recovery, whereas its persistently high levels indicated poor prognosis in COVID-19 patients during hospitalization. Finally, monocytes were identified as the major IL-6 producers under exogenous gp96 stimulation. Our results demonstrate that plasma gp96 may be a useful predictive and prognostic biomarker for disease severity and outcome of COVID-19.

Indexed as

Severity of Illness IndexAdolescentAdultAgedAged, 80 and overBiomarkersCohort StudiesCOVID-19COVID-19 TestingCytokinesFemaleHumansInterleukin-6MaleMembrane GlycoproteinsMiddle AgedBiomarkersCytokinesendoplasminInterleukin-6Membrane GlycoproteinsCOVID‐19IL-6plasma gp96predictive biomarker

Identifiers

PMID34817280
PMCPMC8612155
OpenAlexW3216868219

What OpenQuestion holds

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LicenceCC BY
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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.