Nanoparticle capture of urinary lipoarabinomannan for diagnosing childhood tuberculosis

Urinary assays detecting lipoarabinomannan (LAM) as a diagnostic test for tuberculosis (TB) have limited sensitivity in pediatric populations. We aimed to evaluate the Ceres TB-Nanotrap to concentrate LAM and augment detection in urine using the Alere lateral flow assay (LF-LAM). This case-control study recruited 32 children with and without TB aged 1-18 years in Karachi, Pakistan, with banked urine specimens. TB case participants were classified as confirmed or unconfirmed TB using modified National Institutes of Health (NIH) consensus criteria. The LF-LAM test was performed before and after concentrating 5 mL of urine with TB-Nanotrap particles. Band intensity was measured via visual grading and digital quantification. Urine LAM sensitivity by visual grading was 4.5% (95% confidence interval [CI] 0.3-18.5) pre-concentration and 50.0% (95% CI 30.0-70.0) post-concentration. Sensitivity by digital quantification was 0.0% (95% CI 0.0-15.4) pre-concentration and 63.9% (95% CI 42.8-81.4) post-concentration. Specificity was high with both methods (visual grading: 90.0% [95% CI 62.8-99.4] pre-concentration and 90.0% [95% CI 62.8-99.4] post-concentration; digital analysis: 80.0% [95% CI 44.4-97.5] and 90.0% [95% CI 62.8-99.4], respectively). For cases, digital analysis showed an increase in median LF-LAM band intensity from 0.0 arbitrary units (AU) (interquartile range [IQR] 0.00-32.5) in unconcentrated samples to 98.4 AU (IQR 34.9-212.2) in concentrated samples. Concentration with TB-Nanotrap greatly increased sensitivity of urine LAM detection without a change in specificity. Use of TB-Nanotrap and digital quantitative analysis of urine LAM improved the diagnostic accuracy of the LF-LAM assay in this pediatric population and should be validated in larger studies.IMPORTANCEDiagnosing TB in children is challenging due to a lack of accurate and accessible diagnostic tests. In this study, we evaluated two methods of improving detection of TB in children using a commercially available urine test. First, we processed the urine to concentrate any available TB antigens. Second, we included a digital reader to interpret the test results. We found that concentrating urine and using a digital reader led to an increase in TB detection among children who had been diagnosed with TB, while minimizing false-positive results among children who did not have TB.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Microbiology
Published
2026-10-12
DOI
https://doi.org/10.1128/jcm.01559-25
Primary Topic
Tuberculosis Research and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nanoparticle capture of urinary lipoarabinomannan for diagnosing childhood tuberculosis

Kumail Ahmed, Najeeha Talat Iqbal, Tania A. Thomas, B. Mullen
Journal of Clinical Microbiology
Tuberculosis Research and Epidemiology
article

Nanoparticle capture of urinary lipoarabinomannan for diagnosing childhood tuberculosis

Kumail Ahmed, Najeeha Talat Iqbal, Tania A. Thomas, B. Mullen
article en

Abstract

Urinary assays detecting lipoarabinomannan (LAM) as a diagnostic test for tuberculosis (TB) have limited sensitivity in pediatric populations. We aimed to evaluate the Ceres TB-Nanotrap to concentrate LAM and augment detection in urine using the Alere lateral flow assay (LF-LAM). This case-control study recruited 32 children with and without TB aged 1-18 years in Karachi, Pakistan, with banked urine specimens. TB case participants were classified as confirmed or unconfirmed TB using modified National Institutes of Health (NIH) consensus criteria. The LF-LAM test was performed before and after concentrating 5 mL of urine with TB-Nanotrap particles. Band intensity was measured via visual grading and digital quantification. Urine LAM sensitivity by visual grading was 4.5% (95% confidence interval [CI] 0.3-18.5) pre-concentration and 50.0% (95% CI 30.0-70.0) post-concentration. Sensitivity by digital quantification was 0.0% (95% CI 0.0-15.4) pre-concentration and 63.9% (95% CI 42.8-81.4) post-concentration. Specificity was high with both methods (visual grading: 90.0% [95% CI 62.8-99.4] pre-concentration and 90.0% [95% CI 62.8-99.4] post-concentration; digital analysis: 80.0% [95% CI 44.4-97.5] and 90.0% [95% CI 62.8-99.4], respectively). For cases, digital analysis showed an increase in median LF-LAM band intensity from 0.0 arbitrary units (AU) (interquartile range [IQR] 0.00-32.5) in unconcentrated samples to 98.4 AU (IQR 34.9-212.2) in concentrated samples. Concentration with TB-Nanotrap greatly increased sensitivity of urine LAM detection without a change in specificity. Use of TB-Nanotrap and digital quantitative analysis of urine LAM improved the diagnostic accuracy of the LF-LAM assay in this pediatric population and should be validated in larger studies.IMPORTANCEDiagnosing TB in children is challenging due to a lack of accurate and accessible diagnostic tests. In this study, we evaluated two methods of improving detection of TB in children using a commercially available urine test. First, we processed the urine to concentrate any available TB antigens. Second, we included a digital reader to interpret the test results. We found that concentrating urine and using a digital reader led to an increase in TB detection among children who had been diagnosed with TB, while minimizing false-positive results among children who did not have TB.

Journal of Clinical Microbiology
Aga Khan University (PK), University of Washington (US), University of Virginia (US)
Openalex Percentile: Top 11%
Tuberculosis Research and Epidemiology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.