Complementary roles of KCa3.1 channels and β1‐integrin in the regulation of alveolar epithelial repair
Bibliographic record
Abstract
A common feature of acute lung injury is extensive damage and remodeling of alveolar epithelium. It has been shown that epithelial regeneration and secondary lung edema resorption are crucial to patient recovery. We previously reported that KvLQT1 and K ATP K + channels regulated alveolar epithelial repair processes, but the role of another candidate, the KCa3.1, has never been evaluated. We first demonstrated that the wound‐healing rates were higher in primary rat alveolar cell monolayers cultured on a fibronectin‐collagen (FC) matrix, than on non‐coated supports. KCa3.1 inhibition, with TRAM‐34, or activation, with 1‐EBIO, respectively down‐ and up‐regulated the alveolar wound‐healing rates on FC matrix, but were without effect in absence of FC coating. Accordingly, single cell migration rates were also dependent, at least in part, of FC matrix coating and KCa3.1 activity. A possible relationship between KCa3.1 and migratory cell proteins such as the integrin, activated by fibronectin, was then assessed. Co‐immunoprecipitation experiments thus revealed a physical interaction between KCa3.1 and the β‐1 integrin subunit. Altogether, our data showed for the first time a complementary role of KCa3.1 channels, extracellular matrix and β‐1 integrin, in the regulation of alveolar repair after injury. Project funded by CIHR, RHN of FRQS, and FESP of Université de Montréal.
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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 | 0.001 |
| 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".