Evidence map›Paper›PMID 41262627›Full record

ArticleInfection and drug resistance2025

Novel LAM Assay Shown Satisfactory Results in the Detection of Tuberculosis.

Xiaolu Luo, Keke Xin, Feie Lai, Zhouhua Xie, Xiaolu Pang, Yuliang Huang, Qiuying Ma, Yanqing Zheng, Xiaocheng Luo, Huaying Liu

Abstract read
In one paragraph

Article in Infection and drug resistance, 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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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Xiaolu Luo *Department of Infectious Disease Research Laboratory, the Fourth People's Hospital of Nanning, Nanning, People's Republic of China.
Keke Xin *Department of Medicine, GuangXi Health Science College, Nanning, 530021, People's Republic of China.
Feie Lai *Department of Teacher Development, GuangXi Health Science College, Nanning, 530021, People's Republic of China.
Zhouhua Xie *Department of Tuberculosis, the Fourth People's Hospital of Nanning, Nanning, People's Republic of China.
Xiaolu PangDepartment of Infectious Disease Research Laboratory, the Fourth People's Hospital of Nanning, Nanning, People's Republic of China.
Yuliang HuangDepartment of Infectious Disease Research Laboratory, the Fourth People's Hospital of Nanning, Nanning, People's Republic of China.
Qiuying MaDepartment of Infectious Disease Research Laboratory, the Fourth People's Hospital of Nanning, Nanning, People's Republic of China.
Yanqing ZhengDepartment of Infectious Disease Research Laboratory, the Fourth People's Hospital of Nanning, Nanning, People's Republic of China.
Xiaocheng LuoDepartment of Clinical Laboratory, the Fourth People's Hospital of Nanning, Nanning, People's Republic of China.
Huaying LiuDepartment of Medicine, GuangXi Health Science College, Nanning, 530021, People's Republic of China.ORCID 0000-0003-0230-537X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diagnosing tuberculosis (TB) infection remains a challenge for clinicians. According to recommendations from the World Health Organization (WHO), lipoarabinomannan (LAM) testing can be used to diagnose TB infection in individuals. A new generation of urinary LAM testing is now available. However, studies on its accuracy are limited and warrant further exploration. Objective: This study aims to evaluate the accuracy of a new urinary LAM testing using LAM for diagnosing TB infection. Methods: A cross-sectional study was conducted at the Fourth People's Hospital of Nanning, China. Participants were enrolled from December 2023 to May 2024. Fresh urine samples were collected from the participants and tested using LAM. The diagnostic accuracy of the LAM was compared with other tests for Mycobacterium tuberculosis (sputum culture, sputum smear or molecular biology testing). We compared the positive rates of different TB detection methods to evaluate the effectiveness of LAM. In the comparative analysis, the 95% confidence interval was calculated using the Wilson score method. The kappa value was computed, and the corresponding P-value was reported. Results: The positive agreement rate of LAM was 64.37%, the negative agreement rate was 94.29%, and the overall agreement rate was 80.73%. The Kappa value was 0.6013, indicating good consistency between the test reagent and the reference method. Conclusion: LAM can be used for the diagnosis of TB. It provides diagnostic information quickly, easily, and cost-effectively, particularly showing good performance in diagnosing TB.

Indexed as

diagnostic accuracylipoarabinomannantuberculosis

Identifiers

PMID41262627
PMCPMC12625757

What Socratic holds

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LicenceCC BY-NC
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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.