Potassium channels as operators of alveolar epithelial repair
Bibliographic record
Abstract
A common feature of inflammatory lung diseases such as acute lung injury and acute respiratory distress syndrome (ARDS) is extensive damage and remodeling of alveolar epithelium. A better understanding of determinants of alveolar regeneration is thus necessary to develop strategies able to restore alveolar integrity. Recent studies have shown that K + channels (KCh) are key components of cell proliferation and migration processes, necessary for tissues repair. We therefore postulated that KCh play a role in respiratory epithelial repair. Using a wound‐healing assay, we first showed that glibenclamide and clofilium, inhibitors of K ATP and KvLQT1 channels, decrease repair rates of A549 and primary alveolar ATII cell monolayers and their inhibitory effects are additive. The role of KvLQT1 and K ATP was then confirmed by a molecular approach using specific siRNAs. On the contrary, KvLQT1 and K ATP activators significantly stimulate the wound repair. We also evaluated the influence of KCh on cell growth and we showed that clofilium dose‐dependently inhibits alveolar cell growth and induces an accumulation of cells arrested in G0/G1 phase. In conclusion, we demonstrated that KCh, particularly KvLQT1 and K ATP , are involved in epithelial repair processes and should be identify as novel targets to promote alveolar repair necessary to ARDS resolution. Project funded by Canadian Institute of Health Research.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".