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Lipoarabinomannan is a glycolipid present in the mycobacterial wall, characterized by its structural variability and ability to modulate cytokine production and T-cell activity. Urinary assays detecting LAM have limited sensitivity in pediatric populations, so the researchers utilized the Ceres TB-Nanotrap to augment LAM detection in the Alere lateral flow assay, which is a rapid diagnostic test that uses a paper-based strip to detect a specific substance in a liquid sample. An accurate test to confirm TB in children does not exist, but with this research, we may deduce a urinary biomarker to help childhood TB diagnoses, especially since their symptoms are non-specific and extrapulmonary manifestations are common.

Issues regarding this preprint concern that even though LF-LAM has a rapid turnaround time, the subjective visual assay interpretation (they had 3 independent blind reviewers for each sample), the need for CD4 cell count results, and the narrow target population of adults (since this was a pediatric study) limit a more widespread uptake of this research.

Additionally, nontuberculous mycobacteria have been shown to cross-react with urinary LAM assays, indicating a falsely positive result in the presence of other skin/oral flora. Other concerns with this preprint include an extremely low sensitivity of 4% for a flow assay test, which is incredibly small for a standard test. This brings up questions regarding how the Nanotrap concentration increased the sensitivity from 4% to 50%. Due to this, the title “Nanoparticle capture of urinary lipoarabinomannan for diagnosing childhood tuberculosis” may not be accurate; I suggest rephrasing to “Nanoparticle capture of urinary lipoarabinomannan suggests increased sensitivity for diagnosing childhood tuberculosis.” Furthermore, the scientists concluded that “Overall, data presented here on diagnostic accuracy of urine LAM with nanoparticle concentration and digital analysis demonstrate improved accuracy in a pediatric population.” However, this statement is questionable because the initial sensitivity was so low.

However, this preprint did well in presenting the data without bias: the statistics were organized well in the figures, and it is significant that there are further implications for LAM grading.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.