Evidence map›Paper›PMID 40114921›Full record

ArticleFrontiers in immunology2025

Heterogeneity and longitudinal transcriptomic characteristics of Tregs in COVID-19 patients.

Yanling Wen, Juanjuan Zhao, Zheng Zhang

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Yanling Wen *Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Juanjuan ZhaoInstitute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Zheng ZhangInstitute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Regulatory T cells (Tregs) play a crucial role in maintaining immune tolerance by suppressing immune responses against pathogens. The fluctuation of Treg proportions in COVID-19 remains a topic of debate, and the mechanisms triggering Treg activation in COVID-19 are still unclear. Understanding these issues is essential for better managing immune responses in COVID-19 patients. Methods: We collected a cohort of COVID-19 patients with varying disease severity and stage to explore the transcriptomic and functional traits of Tregs in these individuals. Using transcriptomic analysis, we evaluated the proportion and functionality of different Treg subsets, specifically HLA_DR Results: Our analysis revealed that the proportion of Discussion: Our research delineates the diversity of dynamic alterations in Tregs and sheds light on potential mechanisms underlying Treg activation, providing a theoretical foundation and offering treatment strategies for managing COVID-19 patients.

Indexed as

COVID-19SARS-CoV-2T-Lymphocytes, RegulatoryTranscriptomeAdultAgedFemaleGene Expression ProfilingHLA-DR AntigensHumansLongitudinal StudiesMaleMiddle AgedReceptors, CXCR3Severity of Illness IndexHLA-DR AntigensReceptors, CXCR3COVID-19dynamic changeheterogeneityscRNA-seqTregs

Identifiers

PMID40114921
PMCPMC11922936

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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.