Evidence map›Paper›PMID 40406513›Full record

ReviewFrontiers in cellular and infection microbiology2025

Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies.

Zewei Yang, Jingjing Li, Jiawen Shen, Huiru Cao, Yuhan Wang, Sensen Hu, Yulu Du, Yange Wang, Zhongyi Yan, Longxiang Xie and 5 more

Abstract readReview
In one paragraph

Review in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

15 authors.

Zewei Yang *School of Basic Medical Sciences, Henan University, Kaifeng, China.
Jingjing Li *School of Basic Medical Sciences, Henan University, Kaifeng, China.
Jiawen Shen *School of Basic Medical Sciences, Henan University, Kaifeng, China.
Huiru CaoSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Yuhan WangSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Sensen HuSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Yulu DuSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Yange WangSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Zhongyi YanSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Longxiang XieSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Qiming LiSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Salwa E GomaaSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Shejuan LiuSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Xianghui LiSchool of Basic Medical Sciences, Henan University, Kaifeng, China.
Jicheng LiSchool of Basic Medical Sciences, Henan University, Kaifeng, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. Recent advancements in the field of TB diagnostics have focused on the identification and utilization of blood-based biomarkers, offering a non-invasive, rapid, and scalable approach to disease detection. This review provides a comprehensive overview of the latest progress in blood-based biomarkers for TB, highlighting their potential to revolutionize diagnostic strategies. Furthermore, we explore emerging technologies such as NGS, PET-CT, Xpert and line probe assays, which have enhanced the sensitivity, specificity, and accessibility of biomarker-based diagnostics. The integration of artificial intelligence (AI) and machine learning (ML) in biomarker analysis is also examined, showcasing its potential to improve diagnostic accuracy and predictive capabilities. This review underscores the need for multidisciplinary collaboration and continued innovation to translate these promising technologies into practical, point-of-care solutions. By addressing these challenges, blood-based biomarkers and emerging technologies hold the potential to significantly improve TB diagnosis, ultimately contributing to global efforts to eradicate this devastating disease.

Indexed as

BiomarkersTuberculosisArtificial IntelligenceHumansMachine LearningMolecular Diagnostic TechniquesMycobacterium tuberculosisSensitivity and SpecificityBiomarkersartificial intelligenceblood-based biomarkersdiagnostic technologiesglobal healthtuberculosis

Identifiers

PMID40406513
PMCPMC12094917

What Socratic 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 Socratic graph.