Evidence map›Paper›PMID 34262564›Full record

Observational studyFrontiers in immunology2021

Differential Cytokine Responses in Hospitalized COVID-19 Patients Limit Efficacy of Remdesivir.

Yi-Hao Chan, Barnaby E Young, Siew-Wai Fong, Ying Ding, Yun Shan Goh, Rhonda Sin-Ling Chee, Seow-Yen Tan, Shirin Kalimuddin, Paul A Tambyah, Yee-Sin Leo and 3 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Differential Cytokine Responses of APOE3 and APOE4 Blood-brain Barrier Cell Types to SARS-CoV-2 Spike Proteins.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2024
    Article
  3. Review
  4. Article
  5. Article
  6. Cytokine Storm Signature in Patients with Moderate and Severe COVID-19.International journal of molecular sciences · 2022
    Article
  7. 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 8 institutions in 2 countries.

Yi-Hao ChanASTAR Infectious Diseases Labs (A*STAR ID Labs), Agency for Science, Technology and Research, Singapore, Singapore.
Barnaby E YoungNational Centre for Infectious Diseases, Singapore, Singapore.
Siew-Wai FongASTAR Infectious Diseases Labs (A*STAR ID Labs), Agency for Science, Technology and Research, Singapore, Singapore.
Ying DingNational Centre for Infectious Diseases, Singapore, Singapore.
Yun Shan GohASTAR Infectious Diseases Labs (A*STAR ID Labs), Agency for Science, Technology and Research, Singapore, Singapore.
Rhonda Sin-Ling CheeASTAR Infectious Diseases Labs (A*STAR ID Labs), Agency for Science, Technology and Research, Singapore, Singapore.
Seow-Yen TanDepartment of Infectious Diseases, Changi General Hospital, Singapore, Singapore.
Shirin KalimuddinDepartment of Infectious Diseases, Singapore General Hospital, Singapore, Singapore.
Paul A TambyahDepartment of Medicine, National University Hospital, Singapore, Singapore.
Yee-Sin LeoNational Centre for Infectious Diseases, Singapore, Singapore.
Lisa F P NgASTAR Infectious Diseases Labs (A*STAR ID Labs), Agency for Science, Technology and Research, Singapore, Singapore.
David Chien LyeNational Centre for Infectious Diseases, Singapore, Singapore.
Laurent ReniaASTAR Infectious Diseases Labs (A*STAR ID Labs), Agency for Science, Technology and Research, Singapore, Singapore.
Agency for Science, Technology and Research · SGNational Centre for Infectious Diseases · SGChangi General Hospital · SGDuke-NUS Medical School · SGNational University Health System · SGNational University Hospital · SGNational University of Singapore · SGSingapore Immunology Network · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A significant proportion of COVID-19 patients will progress to critical illness requiring invasive mechanical ventilation. This accentuates the need for a therapy that can reduce the severity of COVID-19. Clinical trials have shown the effectiveness of remdesivir in shortening recovery time and decreasing progression to respiratory failure and mechanical ventilation. However, some studies have highlighted its lack of efficacy in patients on high-flow oxygen and mechanical ventilation. This study uncovers some underlying immune response differences between responders and non-responders to remdesivir treatment. Immunological analyses revealed an upregulation of tissue repair factors BDNF, PDGF-BB and PIGF-1, as well as an increase in ratio of Th2-associated cytokine IL-4 to Th1-associated cytokine IFN-γ. Serological profiling of IgG subclasses corroborated this observation, with significantly higher magnitude of increase in Th2-associated IgG2 and IgG4 responses. These findings help to identify the mechanisms of immune regulation accompanying successful remdesivir treatment in severe COVID-19 patients.

Indexed as

COVID-19 Drug TreatmentHospitalizationAdenosine MonophosphateAdultAgedAlanineAntibodies, ViralAntiviral AgentsBecaplerminBrain-Derived Neurotrophic FactorCase-Control StudiesCOVID-19CytokinesFemaleHumansImmunoglobulin GAdenosine MonophosphateAlanineAntibodies, ViralAntiviral AgentsBDNF protein, humanBecaplerminBrain-Derived Neurotrophic FactorCytokinesImmunoglobulin GMembrane ProteinsPIGF protein, humanremdesivirSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2COVID-19disease progressionremdesivir (GS-5734)T-cellstissue repair

Identifiers

PMID34262564
PMCPMC8275132
OpenAlexW3174110895

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.