Evidence map›Paper›PMID 40746087›Full record

ArticleThe clinical respiratory journal2025

Association of T-Cell Profiles With Disease Severity, Drug-Induced Liver Injury, and Treatment Completion in Tuberculosis.

Yifan He, Xubin Zheng, Zihan Dang, Xiaohui Hao, Yidian Liu, Peng Wang, Yingying Chen, Ying Wang, Wei Sha

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The clinical respiratory journal, 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

9 authors.

Yifan HeClinic and Research Center of Tuberculosis, Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0002-2227-6847
Xubin ZhengClinic and Research Center of Tuberculosis, Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Zihan DangDepartment of Health Studies and Applied Educational Psychology, Columbia University, New York, New York, USA.
Xiaohui HaoClinic and Research Center of Tuberculosis, Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Yidian LiuClinic and Research Center of Tuberculosis, Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Peng WangClinic and Research Center of Tuberculosis, Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Yingying ChenDepartment of Microbiology and Immunology, Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ying WangDepartment of Microbiology and Immunology, Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID https://orcid.org/0000-0003-1787-0515
Wei ShaClinic and Research Center of Tuberculosis, Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.

Funding

Multi-Dimensional Diagnostic Strategies for Tuberculosis and R&D of Innovative Products Program 2018ZX10301Shanghai three-year (2023-2025) action plan to strengthen the public health system GWVI-11.2-YQ06Shanghai three-year (2023-2025) action plan to strengthen the public health system GWVI-9 and GWVI-11.1-05the National Key R&D Program of China 2023YFC2307300
6 · The paper itself

Abstract

backgroundTuberculosis (TB) treatment is challenged by a long duration, poor adherence, and the high risk of drug-induced liver injury (DILI). T-cell immunity is essential for anti-mycobacterial defense, but current immune-monitoring methods poorly reflect disease severity and treatment response. Correlations of immune subpopulations with TB severity, DILI, and treatment prognosis remain poorly understood.

methodsPeripheral blood mononuclear cells were collected from confirmed TB patients (n = 40). Multiparameter flow cytometry analysis was used to assess previously defined TB-associated T-cell phenotypes based on the co-expression of cytokines and immune checkpoint molecules following stimulation with two Mycobacterium tuberculosis peptides: culture filtrate protein 10 and early secreted antigenic target 6. Patients were subgrouped by disease severity, DILI, and treatment regimen (16-week short course vs. 24-week standard).

resultsSpecific subsets (14/124) were found to be associated with disease severity. Notably, six of 14 subsets were positive for programmed death-ligand 1 (PD-L1), indicating its potential role in disease progression. DILI was associated with three interleukin (IL)-21

conclusionsThis study identifies specific T-cell phenotypes associated with TB severity, DILI, and treatment dynamics, highlighting potential immune markers for disease monitoring and DILI prediction.

Indexed as

Antitubercular AgentsChemical and Drug Induced Liver InjuryT-LymphocytesT-Lymphocyte SubsetsTuberculosisAdultCytokinesFemaleFlow CytometryHumansMaleMiddle AgedMycobacterium tuberculosisPrognosisSeverity of Illness IndexAntitubercular AgentsCytokinesdrug‐induced liver injuryimmune checkpoint moleculesT‐cell phenotypestreatment responsetuberculosis

Identifiers

PMID40746087
PMCPMC12314307

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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.