New and emerging targeted therapies for cystic fibrosis
Bibliographic record
Abstract
Cystic fibrosis (CF) is a monogenic autosomal recessive disorder that affects about 70,000 people worldwide. The clinical manifestations of the disease are caused by defects in the cystic fibrosis transmembrane conductance regulator (CFTR) protein. The discovery of the CFTR gene in 1989 has led to a sophisticated understanding of how thousands of mutations in the CFTR gene affect the structure and function of the CFTR protein. Much progress has been made over the past decade with the development of orally bioavailable small molecule drugs that target defective CFTR proteins caused by specific mutations. Furthermore, there is considerable optimism about the prospect of gene replacement or editing therapies to correct all mutations in cystic fibrosis. The recent approvals of ivacaftor and lumacaftor represent the genesis of a new era of precision medicine in the treatment of this condition. These drugs are having a positive impact on the lives of people with cystic fibrosis and are potentially disease modifying. This review provides an update on advances in our understanding of the structure and function of the CFTR, with a focus on state of the art targeted drugs that are in development.
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 | 0.001 | 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".