Impact of Airway Inflammation on the Efficacy of CFTR Modulators
Bibliographic record
Abstract
Defective CFTR biogenesis and activity in cystic fibrosis airways leads to airway dehydration and impaired mucociliary clearance, resulting in chronic airway infection and inflammation. Most cystic fibrosis patients have at least one copy of the F508del CFTR mutation, which results in a protein retained in the endoplasmic reticulum and degraded by the proteosomal pathway. CFTR modulators, e.g., correctors, promote the transfer of F508del to the apical membrane, while potentiators increase CFTR activity. Corrector and potentiator double therapies modestly improve lung function, whereas triple therapies with two correctors and one potentiator indicate improved outcomes. Enhanced F508del rescue by CFTR modulators is achieved by exposing F508del/F508del primary cultures of human bronchial epithelia to relevant inflammatory stimuli, i.e., supernatant from mucopurulent material or bronchoalveolar lavage fluid from human cystic fibrosis airways. Inflammation enhances the biochemical and functional rescue of F508del by double or triple CFTR modulator therapy and overcomes abrogation of CFTR correction by chronic VX-770 treatment in vitro. Furthermore, the impact of inflammation on clinical outcomes linked to CFTR rescue has been recently suggested. This review discusses these data and possible mechanisms for airway inflammation-enhanced F508del rescue. Expanding the understanding of how airway inflammation improves CFTR rescue may benefit cystic fibrosis patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".