Evidence map›Paper›PMID 40909925›Full record

ArticleFrontiers in microbiology2025

Value of urinary lipoarabinomannan levels for tuberculosis diagnosis and monitoring of therapy.

Yiqun Xiong, Zhihong Shen, Bo Dong, Ying Wang, Ying Zhu, Hongxia Wei, Dongliang Zhang, Yang Che

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.

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0citing papers in PubMed
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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.

Yiqun Xiong *Department of Infection, Ningbo Yinzhou No.2 Hospital, Ningbo, China.
Zhihong Shen *Department of Infection, Ningbo Yinzhou No.2 Hospital, Ningbo, China.
Bo DongDepartment of Infection, Ningbo Yinzhou No.2 Hospital, Ningbo, China.
Ying WangDepartment of Infection, Ningbo Yinzhou No.2 Hospital, Ningbo, China.
Ying ZhuDepartment of Infection, Ningbo Yinzhou No.2 Hospital, Ningbo, China.
Hongxia WeiGraduate School, Lyceum of the Philippines University - Batangas, Batangas City, Philippines.
Dongliang ZhangInstitute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, Ningbo, China.
Yang CheInstitute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, Ningbo, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The urinary lipoarabinomannan (LAM) assay has emerged as a promising tool for tuberculosis (TB) diagnosis and treatment monitoring. This study aimed to evaluate the diagnostic and monitoring performance of LAM compared to Acid-fast bacilli (AFB), Mycobacteria Growth Indicator Tube (MGIT), and GeneXpert, and to establish its clinical utility in a stratified TB population. Methods: A prospective cohort study included TB patients stratified by AFB/MGIT status into three groups. Diagnostic accuracy was tested against composite reference standard (CRS). Early monitoring performance was assessed via serial LAM measurements during 12-week treatment. ROC/KM/Cox analyses determined optimal thresholds and predictors of LAM conversion. Results: Against CRS, LAM demonstrated a sensitivity of 58.75%, which was numerically higher than AFB smear (45.00%, Conclusion: Urinary LAM serves as a dual-role biomarker, providing moderate diagnostic sensitivity and dynamic monitoring signals reflecting early bacillary response to therapy. The PreLAM+ΔLAM model enables early treatment response assessment for personalized therapy.

Indexed as

diagnostic accuracylipoarabinomannanpredictive modelingtreatment monitoringtuberculosis

Identifiers

PMID40909925
PMCPMC12405177

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.