Volatile organic compound detection of Buruli ulcer disease: Headspace analysis of Mycobacterium ulcerans and used gauzes of Buruli-compatible ulcers
Bibliographic record
Abstract
Diagnosing Buruli ulcer (BU) is complicated by limited access to the sensitive IS2404 qPCR. Experienced clinicians report a distinct odour of Buruli ulcers. We explored the potential of headspace analysis by thermal desorption-gas chromatography-mass spectrometry to detect volatile organic compounds (VOCs) from Mycobacterium ulcerans both in vitro and clinically. This study was conducted in two phases: a discovery and validation phase. During the discovery phase, VOCs that enable identification of M. ulcerans cultures were determined. During the validation phase, these VOCs were evaluated in clinical samples for which we used gauzes from patients with skin ulcerations in the Democratic Republic of Congo. Seven M. ulcerans headspace samples were compared with four from sterile growth medium and laboratory environmental air. The univariate analysis resulted in the selection of 24 retained VOC fragments and a perfect differentiation between cultures and controls. Sixteen of 24 fragments were identified, resulting in eleven unique compounds, mainly alkanes. Methylcyclohexane was the best performing compound. Based on these 24 fragments, headspace samples originating from gauzes of 50 open skin lesions (12 qPCR positive and 38 negative) were analysed and an AUC of 0.740 (95%-CI 0.583-0.897) was obtained. As this is an experimental study, future research has to confirm whether the identified compounds can serve as novel biomarkers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".