Impact of <i>P.aeruginosa</i> bacterial filtrate on CFTR channels and bronchial epithelial repair.
Bibliographic record
Abstract
Cystic fibrosis (CF) pathology, due to mutations in the CFTR, is characterized by bacterial colonisation and chronic inflammatory response leading to a progressive damage of the airways. It then becomes crucial to better understand the impact of these deleterious phenomena on the repair processes in attempt to restore the CF airways integrity. First, we confirmed, as in our previous studies, that the repair rates of airway epithelial CFBE‐ΔF508 (CF) monolayers were slower than that of CFBE‐wt (non‐CF) monolayers, which was, at least in part, dependent on CFTR function. We now observed that P. aeruginosa bacterial filtrate (PAF) inhibited wound closure of both non‐CF and CF CFBE monolayers. We also observed that wt‐ and ΔF508‐CFTR protein expressions, as well as Cl − currents through CFTR channels in CFBE‐wt monolayers were decreased after PAF exposure. Finally, CFTR corrector VRT‐325, initially developed to promote CFTR folding and function, was shown to be able to also improve the repair capacity of CF cell monolayers. However, its beneficial effect was abolished in the presence of PAF. This study showed that exoproducts from P. aeruginosa alter CFTR expression/function and also exhibit a deleterious effect on airway repair capacity, in basal condition or after CFTR correction. Project supported by Cystic Fibrosis Canada, the Canadian Lung Association, the CORAMH and Fondation Go.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".