Fixing cystic fibrosis CFTR with correctors and potentiators. Off to a good start
Bibliographic record
Abstract
Since the cloning of the cystic fibrosis transmembrane regulator (CFTR) protein in the late 1980s and subsequent significant advances in understanding the pathophysiology of cystic fibrosis (CF) there have been high expectations that transformative, disease-modifying therapies could be developed.1 2 Dysfunction of the CFTR protein results in significant reduction or absence of chloride transport and dysregulation of sodium transport.3–5 Other cationic ions such as bicarbonate may also be transported by CFTR and the interaction with the epithelial sodium channel ENaC still remains to be elucidated.6 7 However, a significant number of in vitro and animal studies strongly support the premise that correction of CFTR function is possible and will make a significant impact on the CF phenotype, particularly in the lung, which accounts for most of the morbidity and mortality from this condition.7 Two approaches to correction of CFTR function are currently being pursued: one based on gene transfer therapy and the other on small molecule modulators. Gene therapy attempts to correct CFTR dysfunction in CF by transfection of airway cells with the wild-type CFTR gene to enable cells to express functional CFTR.8 Progress with this approach has been slow and methodical. Early studies suggest that it is possible to transfect nasal and bronchial epithelial cells and this results in a modest electrophysiological improvement.9 10 To date, there are no clinical studies in humans to determine if sufficient gene transfer can be achieved to result in a clinically meaningful benefit. A two-centre, double-blind, placebo-controlled trial of CF gene therapy is due to commence in the UK in 2012 using a specifically designed plasmid and a GL67 liposome complex. The results of this proof of concept study will help determine if gene therapy has the potential to provide a treatment which will benefit all mutation …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".