Evidence map›Paper›PMID 41669692›Full record

ArticleFrontiers in microbiology2025

A plasma proteomic signature of the actin-coagulation axis accurately predicts progression to active tuberculosis.

Peng Lu, Weiwei Gao, Xiaoyan Ding, Jingjing Pan, Hui Ding, Qiao Liu, Limei Zhu, Xiang Huo

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Peng Lu *Department of Chronic Communicable Disease, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Weiwei Gao *Department of Tuberculosis, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Xiaoyan DingDepartment of Chronic Communicable Disease, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Jingjing PanDepartment of Chronic Communicable Disease, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Hui DingDepartment of Chronic Communicable Disease, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Qiao LiuDepartment of Chronic Communicable Disease, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Limei ZhuDepartment of Chronic Communicable Disease, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Xiang HuoDepartment of Chronic Communicable Disease, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Predicting progression to active tuberculosis (TB) is a critical unmet need, as current immunological tests only detect infection but cannot discriminate those who will develop active disease. A reliable prognostic biomarker could enable targeted preventive therapy and transform TB control strategies. Methods: We performed deep plasma proteomics using data-independent acquisition mass spectrometry on a prospective cohort of 60 TB-exposed students in Jiangsu Province, China, comprising 40 individuals with Results: Over 2 years of follow-up, 21 of the 40 infected participants progressed to active disease, and a LASSO-Cox model with internal cross-validation for tuning the regularization parameter ( Conclusion: We identified a plasma protein signature that accurately predicts progression from latent to active tuberculosis, linking disease onset to dysregulation of the actin cytoskeleton and coagulation. This biomarker provides a basis for precision preventive therapy and identifies novel host-directed therapeutic targets.

Indexed as

actin cytoskeletoncoagulationprognostic biomarkerproteomicstuberculosis

Identifiers

PMID41669692
PMCPMC12883649

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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.