CFTR modulator therapy does not alter breath profiles in cystic fibrosis
Bibliographic record
Abstract
Background: Electronic nose (eNose) technology can be used to detect volatile organic compounds (VOCs) in exhaled breath. eNose-generated breath profiles in people with cystic fibrosis (CF) differ from those of healthy controls, but the underlying reasons for these differences are incompletely understood. Aims and objectives: To study the effect of CFTR modulator therapy elexacaftor/tezacaftor/ivacaftor (ETI) on eNose breath profiles in CF children. Methods: In this longitudinal observational study, eNose-generated breath profiles were obtained from CF children at stable clinic visits before and after initiation of ETI therapy. A cloud-connected eNose (SpiroNose, de Vries et al. 2018 ERJ) was used for VOC measurements. Data-analysis involved advanced signal processing and ambient correction. The breath profiles were compared between the study visits by means of independent sample t-tests internally validated by 1000 iterations of bootstrap. Results: Fifty-four children with CF (F508del/F508del n=35; F508del/minimal function n=19) were included (median age 13.6 (IQR 10.1-16.5) years, percent predicted (pp)FEV1 92.0 (IQR 79.3-101.3), BMI 18.5 (IQR 16.4-20.5) m2/kg). ETI therapy (median follow-up time 7.4 (IQR 4.4-11.6) months) resulted in improvement of ppFEV1 (+10.2%; p<0.001) and BMI (+0.94 kg/m2; p<0.001), but no change in VOC breath profiles. Subgroup analysis by CFTR genotype showed no differences in breath profiles. Conclusions: CFTR modulator therapy with ETI, while improving pulmonary function and nutrition status in CF children, does not result in eNose-detectable changes of VOC composition in breath. Further studies are needed to identify factors contributing to the unique VOC breath profile of people with CF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".