Evidence map›Paper›PMID 34109148›Full record

ArticleFrontiers in public health2021

Features of Cytokine Storm Identified by Distinguishing Clinical Manifestations in COVID-19.

Wei-Xi Shen, Rong-Cheng Luo, Jing-Quan Wang, Zhe-Sheng Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.5field-weighted citation impact, top 4% 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

23 citing papers in PubMed, 52 citations in OpenAlex.

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  14. Cytokines and microRNAs in SARS-CoV-2: What do we know?Molecular therapy. Nucleic acids · 2022
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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

4 authors at 2 institutions in 2 countries.

Wei-Xi ShenShenzhen Hospital, Southern Medical University, Shenzhen, China.
Rong-Cheng LuoShenzhen Hospital, Southern Medical University, Shenzhen, China.
Jing-Quan WangCollege of Pharmacy and Health Science, St. John's University, New York, NY, United States.
Zhe-Sheng ChenCollege of Pharmacy and Health Science, St. John's University, New York, NY, United States.
Shenzhen Genoimmune Medical Institute · CNSt. John's University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) is caused by a new coronavirus, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently spreading all over the world. In this paper, we developed a practical model for identifying the features of cytokine storm, which is common in acute infectious diseases and harmful manifestation of COVID-19, by distinguishing major and minor clinical events. This model is particularly suitable for identifying febrile and infectious diseases like COVID-19. Based on this model, features of cytokine storm and pathogenesis of COVID-19 have been proposed to be a consequence of the disequilibrated cytokine network resulting from increased biological activity of transforming growth factor-β (TGF-β), which induces certain clinical manifestations such as fatigue, fever, dry cough, pneumonia, abatement and losing of olfactory, and taste senses in some patients. Research and clarification of the pathogenesis of COVID-19 will contribute to precision treatment. Various anti-TGF-β therapies may be explored as potential COVID-19 treatment. This novel model will be helpful in reducing the widespread mortality of COVID-19.

Indexed as

Coronavirus InfectionsCOVID-19 Drug TreatmentCytokine Release SyndromeHumansSARS-CoV-2clinical manifestationcoronavirus disease 2019 (COVID-19)cytokine stormpathogenesistransforming growth factor-β (TGF-β)

Identifiers

PMID34109148
PMCPMC8180556
OpenAlexW3164519583

What OpenQuestion holds

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Registered trials

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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.