Evidence map›Paper›PMID 41888771›Full record

ArticleBMC biology2026

PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes.

Changchun Wu, Xueqin Xie, Ziru Huang, Yuwei Zhou, Yushu Gou, Mengze Du, Hao Lin, Jian Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC biology, 2026. 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. Review
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.

Changchun WuThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Xueqin XieThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Ziru HuangThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Yuwei ZhouThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Yushu GouThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Mengze DuSchool of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China. DuMengze@nsu.edu.cn.
Hao LinThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China. hlin@uestc.edu.cn.
Jian HuangThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China. hj@uestc.edu.cn.

Funding

Incubation Program for Innovative Science and Technology in UESTC Y03023206100209Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China ZYGX2022YGRH004National Natural Science Foundation of China 62371112National Natural Science Foundation of China 62501110
6 · The paper itself

Abstract

backgroundAccurate and rapid diagnosis is essential for controlling pulmonary tuberculosis (PTB) by enabling timely intervention and reducing disease transmission. Existing diagnostic methods for PTB are limited by sensitivity, specificity, or practicality.

resultsHere, we present a non-invasive blood transcriptome-based diagnostic model that integrates top-scoring pair with machine learning, identifying novel and robust transcriptomic biomarkers for diverse PTB patients. Using 2,792 peripheral blood transcriptome samples, the proposed model (PTBD) effectively distinguishes PTB from healthy individuals, latent tuberculosis infection, pneumonia, lung cancer, and pulmonary nodules, reflecting its robustness against the complex and heterogeneous negative sample background typical of real-world clinical settings, and achieving AUCs of 0.869 in the test set and 0.909 in an independent external validation set. Its performance is consistent across different geographic regions, age groups, and special conditions, including Bacillus Calmette-Guérin vaccination, HIV infection, diabetes, and drug resistance, meeting WHO requirements for community-based triage, children, and PTB with HIV infection. Furthermore, PTBD also enables diagnosis of extrapulmonary tuberculosis and prediction of treatment outcomes, with feature scores serving as molecular biomarkers reflecting disease progression and prognosis.

conclusionsThis study provides a broadly applicable tool for early PTB diagnosis, facilitating timely intervention and potentially reducing global PTB burden. Moreover, PTBD uncovers novel transcriptomic biomarkers, represented by five diagnostic gene-pair expression patterns that embody the molecular hallmarks of PTB.

Indexed as

Machine LearningTranscriptomeTuberculosis, PulmonaryBiomarkersGene Expression ProfilingHumansBiomarkersMachine learningMolecular biomarkerNon-invasive diagnostic modelPulmonary tuberculosisTop-scoring pairTreatment outcome prediction

Identifiers

PMID41888771
PMCPMC13147809

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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.