Exhaled breath volatile organic compounds: window into preterm lung disease?
Bibliographic record
Abstract
Background: Respiratory complications of preterm birth remain common, but current definitions do not include direct pathophysiological markers in the lung. We investigated the potential of exhaled breath volatile organic compounds (VOCs) in a preclinical and clinical setting, as they reflect lipid per oxidation originating from inflammation and oxidative stress. Methods: Exhaled breath was sampled in preterm neonates (with and without BPD) and preterm lambs via proton transfer reaction - high resolution - mass spectrometry (PTR-HRMS). In the ovine experiments, exhaled breath VOCs were quantified before and after exposure to both nasal CPAP and endotracheal ventilation. At euthanasia, lung tissue was collected to assess inflammation, oxidative stress and histological changes. Results: Exhaled breath collection was feasible and safe in the human and ovine setting. sPLS-DA showed good distinction between BPD patients and controls based on exhaled VOCs. Furthermore, it was possible to distinguish pre- versus post-nasal CPAP, pre- versus post- endotracheal ventilation and post-nasal CPAP versus post-endotracheal ventilation ovine breathprints. We identified significant correlations between exhaled VOCs and inflammation, oxidative stress and lung structure (e.g. m/z149.024 correlated positively with catalase, r=0.67, p<0.05). m/z47.09, m/z74.06 and m/z118.90 were identified as relevant exhaled VOCs in both the human and ovine experiments. Conclusion: Exhaled VOCs analysis can distinguish BPD patients from preterm controls and seems to truly reflect local inflammatory and oxidative stress as shown in our ovine model. This stresses the potential of VOCs as non-invasive markers for prematurity-related lung disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".