Neutrophil proteins as potential biomarkers for a sputum-based tuberculosis screening test
Bibliographic record
Abstract
Introduction: The development of a rapid and affordable assay to screen participants for Q12 additional testing could streamline TB screening in resource-limited settings and for community-wide health screens. Sputum remains the primary testing sample, making it potentially ideal for a screening testing. Neutrophils are highly expanded in sputum from individuals with pulmonary TB with high specificity and have potential as a biomarker for TB. Methods: Three neutrophil associated proteins, neutrophil gelatinase associated-lipocalin (NGAL), the protein heterodimer S100A8/A9 and the protein death ligand-1 (PDL-1), were measured in presumptive TB cases from participants attending a primary healthcare clinic in Durban, South Africa, using commercially available ELISAs on a total of 79 participants from a 109-participant cohort. Participants with microbiologically confirmed TB were sampled after 1 month of treatment. Proteins were also measured in tongue swab samples in participants from this cohort at baseline. Baseline results were confirmed in a second TB cohort which recruited a total of 51 participants with presumptive TB from the Western Cape. Finally, we investigate sputum neutrophil protein levels in individuals with community diagnosed asymptomatic TB. Results and discussion: Significant increases in all proteins were detectable in sputum from clinic-diagnosed TB participants relative to symptomatic controls. Performance approached the WHO target product profile for a TB triage test, with ROC AUCs reaching 0.866 (with a 95% confidence interval of 0.7683 - 0.9633) in the case of S100A8/A9. Sputum protein levels did not correlate with bacterial burden and did not consistently decrease following one month of drug therapy. Only PDL-1 was detectable in mouth swab samples. Sputum neutrophil proteins tended to be elevated in participants with asymptomatic community diagnosed TB, as compared to asymptomatic community controls within the Vukuzazi cohort using a sample size of 42 participants, although this was not significant. This study provides a proof of principle that neutrophil proteins can be easily measured in standard sputum samples and have potential as a screening test for TB. However, more work is needed to explore whether this approach, using these three neutrophil proteins, can meet the WHO target product profile for a triage test worth developing further.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".