Airway nitric oxide production in patients with cystic fibrosis increases with ivacaftor therapy
Bibliographic record
Abstract
Introduction: Nitric oxide (NO) plays an important role in the regulation of airway caliber & host defense against certain bacteria. NO is reduced in cystic fibrosis (CF) airways & NO deficiency may contribute to CF lung disease. The mechanisms resulting in reduced airway NO formation in CF are incompletely understood but previous studies suggest a direct link between CFTR dysfunction & low NO production from NO synthases. Method: We studied the effect of ivacaftor, a new treatment targeting CFTR, on airway NO in people with CF. A total of 15 (7 pediatric and 8 adult) patients with CF were recruited. All carried one copy of a CFTR-gating mutation, and 11 were F508del compound heterozygote. Mean age at enrolment was 23 (range 6-58) years. Pulmonary function (spirometry) and fraction of exhaled NO (FENO50) were measured before and 4 weeks after initiation of ivacaftor treatment. Results: Both mean (±SD) forced vital capacity (90.7±12.2 vs 100.4±8.7 % of predicted, p<0.001) and forced expiratory volume in one second (FEV1) (69.7±16.7 vs 84.1±16.7 % of predicted, p<0.001) improved with treatment. FENO increased in all but one of the study participants (p=0.002, Wilcoxon test). Mean FENO doubled from 8.5±5.0 ppb before to 16.2±15.5 ppb after initiation of ivacaftor treatment. There was no correlation between the changes in pulmonary function test results and FENO. Conclusion: Ivacaftor treatment results in a significant increase in exhaled airway NO. These data suggest that CFTR-targeting therapies may result in reconstitution of impaired airway NO formation in patients with CF, and show that an increase in airway NO formation is associated with improved pulmonary function in patients 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 | 0.000 | 0.002 |
| 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".