Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Cystic fibrosis (CF), one of the major respiratory diseases, is caused by mutations in a gene encoding for a chloride channel. Abnormal transepithelial ion transport leads to a reduced volume of the airway surface liquid layer and reduced mucociliary clearance. AREAS COVERED IN THIS REVIEW: There is currently no cure for CF and CF lung disease remains a major contributor to morbidity and mortality. However, most current treatments for CF lung disease do not address the underlying pathology. We describe here new therapeutic developments aiming to identify or generate compounds that counteract the effects of cystic fibrosis transmembrane conductance regulator (CFTR) on the airway. WHAT THE READER WILL GAIN: This review summarizes the current state of new developments in the treatment of CF lung disease. These drugs include nebulized and inhaled osmotically active agents, but also modifiers of ion channels other than CFTR, such as activators of alternative chloride channels or inhibitors of sodium absorption, and compounds in development aim to correct or improve impaired CFTR function directly. First clinical trials with new drugs including ion channel modifiers and CFTR pharmacotherapeutics have revealed very promising results. TAKE HOME MESSAGE: CF drug therapy is moving rapidly from symptomatic therapy to treatment of the underlying pathophysiology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".