The emerging potential of autophagy-based therapies in the treatment of cystic fibrosis lung infections
Bibliographic record
Abstract
Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator (CFTR), a channel that normally transports anions across epithelial cell membranes. The most common manifestation of CF is buildup of mucus in the airways and bacterial colonization of the lower respiratory tract, accompanied by chronic inflammation. Antibiotics are used to control CF-associated opportunistic infections, but lengthy antibiotic treatment risks the emergence of multiple-drug resistant (MDR) strains. New antimicrobial strategies are needed to prevent and treat infections in these high-risk individuals. Autophagy contributes to the control of a variety of microbial infections. For this reason, the recent discovery of functional impairment of autophagy in CF provides a new basis for understanding susceptibility to severe infections. Here, we review the role of autophagy in host defense against CF-associated bacterial and fungal pathogens, and survey pharmacologic approaches to restore normal autophagy function in these individuals. Autophagy restoration therapy may improve pathogen clearance and mitigate lung inflammation in CF airways.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".