ArticleBMC pulmonary medicine2024
Improving the diagnosis of active tuberculosis: a novel approach using magnetic particle-based chemiluminescence LAM assay.
Article in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- What is new in tuberculosis?Clinical medicine (London, England) · 2026Review
- Detection of Mycobacterium tuberculosis antigens in urinary extracellular vesicles using real-time immuno-PCR: A non-invasive diagnostic approach for pulmonary tuberculosis.Scientific reports · 2026Article
- Peroxidase-Mimicking Nanozymes for Rapid Detection of Infectious Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- The value of LAM and LprG in extracellular vesicles in the diagnostic and therapeutic field of renal tuberculosis.Frontiers in tuberculosis · 2025Review
- Value of urinary lipoarabinomannan levels for tuberculosis diagnosis and monitoring of therapy.Frontiers in microbiology · 2025Article
- Humoral correlate of vaccine-mediated protection from tuberculosis identified in humans and non-human primates.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTuberculosis (TB) is a significant global health concern, given its high rates of morbidity and mortality. The diagnosis using urine lipoarabinomannan (LAM) primarily benefits HIV co-infected TB patients with low CD4 counts. The focus of this study was to develop an ultra-sensitive LAM assay intended for diagnosing tuberculosis across a wider spectrum of TB patients. DESIGN &
methodsTo heighten the sensitivity of the LAM assay, we employed high-affinity rabbit monoclonal antibodies and selected a highly sensitive chemiluminescence LAM assay (CLIA-LAM) for development. The clinical diagnostic criteria for active TB (ATB) were used as a control. A two-step sample collection process was implemented, with the cutoff determined initially through a ROC curve. Subsequently, additional clinical samples were utilized for the validation of the assay.
resultsIn the assay validation phase, a total of 87 confirmed active TB patients, 19 latent TB infection (LTBI) patients, and 104 healthy control samples were included. Applying a cutoff of 1.043 (pg/mL), the CLIA-LAM assay demonstrated a sensitivity of 55.2% [95%CI (44.13%~65.85%)], and a specificity of 100% [95%CI (96.52%~100.00%)], validated against clinical diagnostic results using the Mann-Whitney U test. Among 11 hematogenous disseminated TB patients, the positive rate was 81.8%. Importantly, the CLIA-LAM assay consistently yielded negative results in the 19 LTBI patients.
conclusionOverall, the combination of high-affinity antibodies and the CLIA method significantly improved the sensitivity and specificity of the LAM assay. It can be used for the diagnosis of active TB, particularly hematogenous disseminated TB.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.