Targeting Proteases in Cystic Fibrosis Lung Disease. Paradigms, Progress, and Potential
Bibliographic record
Abstract
Cystic fibrosis (CF) is the most common life-limiting hereditary condition of Caucasian populations and is characterised by chronic airways inflammation driving progressive structural lung damage.Despite tremendous advances in the treatment of CF and concomitant increased life expectancy for patients, chronic lung disease remains the main cause of morbidity and mortality among CF patients.While universal restoration of cystic fibrosis transmembrane conductance regulator activity remains a future hope, novel therapies aimed at reducing or preventing chronic airways inflammation and progressive structural lung damage are required.It is well-established that proteolytic enzymes are important in the CF lung beyond the basic turnover of proteins and intracellular degradation of pathogens.When secreted, these enzymes play key roles in extracellular substrate modification implicated in important biological processes such as matrix and airway remodelling, goblet cell metaplasia and mucus hypersecretion, immune cell recruitment and dysregulation of epithelial ion channels.Importantly, the burden of proteases in the CF lung is significantly elevated, overwhelming the endogenous antiprotease shield.Indeed, free protease activity has emerged as a major risk factor of the onset and progression of bronchiectasis and lung function decline in patients with CF.Recent research has highlighted the importance of new players such as cathepsin S and matrix metalloprotease-12, as well as the membrane-associated activity of key proteases such as neutrophil elastase on the surface of neutrophils.Here, we review the current knowledge and emerging concepts of the role of host proteases in the pathogenesis of CF lung disease and their potential as therapeutic targets.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| 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".