Evidence map›Paper›PMID 39376569›Full record

ArticleFrontiers in immunology2024

Distinct soluble immune checkpoint profiles characterize COVID-19 severity, mortality and SARS-CoV-2 variant infections.

Tudorita Gabriela Paranga, Mariana Pavel-Tanasa, Daniela Constantinescu, Elena Iftimi, Claudia Elena Plesca, Ionela-Larisa Miftode, Petru Cianga, Egidia Miftode

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

  1. Impact of Gestational Maternal SARS-CoV-2 Infection on Neonatal Inflammatory Biomarkers.American journal of reproductive immunology (New York, N.Y. : 1989) · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. 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

8 authors.

Tudorita Gabriela ParangaDepartment of Infectious Diseases (Internal Medicine II), Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
Mariana Pavel-TanasaDepartment of Immunology, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
Daniela ConstantinescuDepartment of Immunology, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
Elena IftimiDepartment of Immunology, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
Claudia Elena PlescaDepartment of Infectious Diseases (Internal Medicine II), Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
Ionela-Larisa MiftodeDepartment of Infectious Diseases (Internal Medicine II), Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
Petru CiangaDepartment of Immunology, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
Egidia MiftodeDepartment of Infectious Diseases (Internal Medicine II), Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Over the past four years, the COVID-19 pandemic has posed serious global health challenges. The severe form of disease and death resulted from the failure of immune regulatory mechanisms, closely highlighted by the dual proinflammatory cytokine and soluble immune checkpoint (sICP) storm. Identifying the individual factors impacting on disease severity, evolution and outcome, as well as any additional interconnections, have become of high scientific interest. Methods: In this study, we evaluated a novel panel composed of ten sICPs for the predictive values of COVID-19 disease severity, mortality and Delta vs. Omicron variant infections in relation to hyperinflammatory biomarkers. The serum levels of sICPs from confirmed SARS-CoV-2 infected patients at hospital admission were determined by Luminex, and artificial neural network analysis was applied for defining the distinct patterns of molecular associations with each form of disease: mild, moderate, and severe. Results: Notably, distinct sICP profiles characterized various stages of disease and Delta infections: while sCD40 played a central role in all defined diagrams, the differences emerged from the distribution levels of four molecules recently found and relatively less investigated (sCD30, s4-1BB, sTIM-1, sB7-H3), and their associations with various hematological and biochemical inflammatory biomarkers. The artificial neural network analysis revealed the prominent role of serum sTIM-1 and Galectin-9 levels at hospital admission in discriminating between survivors and non-survivors, as well as the role of specific anti-interleukin therapy (Tocilizumab, Anakinra) in improving survival for patients with initially high sTIM-1 levels. Furthermore, strong associations between sCD40 and Galectin-9 with suPAR defined the Omicron variant infections, while the positive match of sCD40 with sTREM-1 serum levels characterized the Delta-infected patients. Conclusions: Of importance, this study provides a comprehensive analysis of circulatory immune factors governing the COVID-19 pathology, and identifies key roles of sCD40, sTIM-1, and Galectin-9 in predicting mortality.

Indexed as

BiomarkersCOVID-19SARS-CoV-2Severity of Illness IndexAdultAgedCytokine Release SyndromeFemaleHumansImmune Checkpoint ProteinsMaleMiddle AgedBiomarkersImmune Checkpoint ProteinsCOVID-19galectin-9mortalityneural networksCD40soluble immune checkpointsSTIM-1

Identifiers

PMID39376569
PMCPMC11456479

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.