Microbiota, Mucus, and Modulators: Cystic Fibrosis Infection Pathogenesis in the Cystic Fibrosis Transmembrane Conductance Regulator Modulator Era
Bibliographic record
Abstract
Cystic fibrosis (CF) lung disease is a result of defective CF transmembrane conductance regulator (CFTR)-mediated ion transport, producing dehydrated mucus, impaired mucociliary clearance and an opportune environment for chronic airway infection. CF airway infections are polymicrobial airway ecosystems often dominated by CF pathogens such as Pseudomonas aeruginosa, Staphylococcus aureus, Burkholderia, Stenotrophomonas, Achromobacter, and nontuberculous mycobacteria that drive cycles of infection, inflammation, and bronchiectasis. Highly effective CFTR modulators, including elexacaftor/tezacaftor/ivacaftor, improve airway hydration and mucociliary clearance and reduce pathogen CF acquisition and density. However, even with CFTR modulator treatment, most individuals with established infection remain chronically infected, and long-term impacts of CFTR modulators on airway infection dynamics and associated clinical outcomes remain unclear. In this review, we address key gaps in understanding chronic infection in the CFTR modulator era, including changes in infection-related lung disease pathogenesis, airway-gut microbiome interactions, approaches to airway infection sampling, and implications for infection management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".