Volatile organic compounds and the relevance of background correction in the early prediction of bronchopulmonary dysplasia in preterm infants
Bibliographic record
Abstract
Rationale: Volatile organic compounds (VOCs) in exhaled breath, reflecting inflammation and oxidative stress, shows potential for non-invasive monitoring of bronchopulmonary dysplasia (BPD). However, measured VOCs also include 'background VOCs' from the environment and medical devices. Despite their importance, this exposure source is mostly not considered in BPD research. Methods: Exhaled breath samples from BPD patients born ≤ 30 weeks’ gestation on non-invasive respiratory support (n=5) were analysed alongside preterm controls (n=5) on day 28 of life. Background samples from the set-up under similar conditions were obtained to account for VOCs specific to the ventilatory circuit. Samples were analysed using proton transfer reaction high-resolution mass spectrometry (PTR-HRMS). Results: Some VOCs were more abundant in background than in patient samples, but only significant compared to preterm controls: m/z164.0967; m/z163.0966; m/z91.0566 (methyl propyl sulfide); m/z74.04649 (N,N-dimethylformamide); m/z73.0463; m/z72.9368; m/z54.0338; m/z54.0089; m/z45.0339 (ethylene oxide); m/z44.979. VOC with m/z72.9368 was only present in the background. Conclusions: Some background VOCs are no longer abundant in breath samples, suggesting an exogenous origin. The different abundance of these VOCs in BPD breath samples and preterm controls might suggest metabolisation to a greater or lesser extent, yet their potential link to BPD development remains unclear. These findings highlight the importance of background sampling. Further research is needed to explore common exogenous VOCs and their impact on BPD development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".