New Therapies to Correct the Cystic Fibrosis Basic Defect
Bibliographic record
Abstract
Rare diseases affect 400 million individuals worldwide and cause significant morbidity and mortality. Finding solutions for rare diseases can be very challenging for physicians and researchers. Cystic fibrosis (CF), a genetic, autosomal recessive, multisystemic, life-limiting disease does not escape this sad reality. Despite phenomenal progress in our understanding of this disease, treatment remains difficult. Until recently, therapies for CF individuals were focused on symptom management. The discovery of the cystic fibrosis transmembrane conductance regulator (CFTR) gene and its product, a protein present at the apical surface of epithelial cells regulating ion transport, allowed the scientific community to learn about the basic defect in CF and to study potential therapies targeting the dysfunctional protein. In the past few years, promising therapies with the goal to restore CFTR function became available and changed the lives of several CF patients. These medications, called CFTR modulators, aim to correct, potentialize, stabilize or amplify CFTR function. Furthermore, research is ongoing to develop other targeted therapies that could be more efficient and benefit a larger proportion of the CF community. The purpose of this review is to summarize our current knowledge of CF genetics and therapies restoring CFTR function, particularly CFTR modulators and gene therapy.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".