Bibliographic record
Abstract
Cystic fibrosis (CF) is the most common genetic disease affecting Canadian newborns (1 in 3,850) and is caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. This gene encodes for CFTR, a phosphorylation-dependent ion channel localized at the apical membrane. Phosphorylation of CFTR by the cyclic adenosine monophosphate (cAMP)-dependent enzyme protein kinase A activates its activity, facilitating the transport of chloride and bicarbonate ions across the epithelial membrane. CFTR contributes to ion and airway surface liquid regulation, crucial for maintaining host defenses. The inheritance of CFTR mutations leads to a variety of respiratory complications, including impaired mucociliary clearance, excessive mucus production, persistent airway infections, and heightened inflammation, ultimately causing lung damage. While there is currently no cure for CF, the development of CFTR modulators, targeting the defective CFTR protein directly, has significantly improved the quality of life for many CF patients. Despite these advancements, many patients remain unresponsive to current treatment options. It has been well-established that combination therapies outperform monotherapies, emphasizing the need for alternative or complementary therapeutic strategies for CF management. Furthermore, CFTR dysfunction extends beyond CF and has been implicated in other respiratory diseases, such as chronic obstructive pulmonary disease, which is primarily linked to tobacco smoke exposure. This Ph.D. thesis explores a complementary therapeutic approach, targeting proteins within the CFTR-containing macromolecular signaling complex to elevate intracellular cAMP levels, thereby enhancing CFTR function. We hypothesized that synergistic use of cAMP modulators, alongside CFTR modulators, will serve as an effective therapeutic strategy for CF and other respiratory diseases. Collectively, our studies highlight the potential of cAMP and CFTR modulation as a therapeutic strategy for improving the treatment of CF and other respiratory diseases, warranting further investigation, offering insights for future studies, and contributes to the ongoing pursuit of improved combination treatments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".