Involvement of K<sub>ATP</sub>and KvLQT1 K<sup>+</sup>channels in EGF-stimulated alveolar epithelial cell repair processes
Bibliographic record
Abstract
Several respiratory diseases are associated with extensive damage of lung epithelia, and the regulatory mechanisms involved in their regeneration are not clearly defined. Growth factors released by epithelial cells or fibroblasts from injured lungs are important regulators of alveolar repair by stimulating cell motility, proliferation, and differentiation. In addition, K(+) channels regulate cell proliferation/migration and are coupled with growth factor signaling in several tissues. We decided to explore the hypothesis, never investigated before, that K(+) could play a prominent role in alveolar repair. We employed a model of mechanical wounding of rat alveolar type II epithelia, in primary culture, to study their response to injury. Wound healing was suppressed by one-half upon epidermal growth factor (EGF) titration with EGF-antibody (Ab) or erbB1/erbB2 tyrosine-kinase inhibition with AG-1478/AG-825. The addition of exogenous EGF slightly stimulated the alveolar wound healing and enhanced, by up to five times, alveolar cell migration measured in a Boyden-type chamber. Conditioned medium collected from injured alveolar monolayers also stimulated cell migration; this effect was abolished in the presence of EGF-Ab. The impact of K(+) channel modulators was examined in basal and EGF-stimulated conditions. Wound healing was stimulated by pinacidil, an ATP-dependent K(+) channel (K(ATP)) activator, which also increased cell migration, by twofold, in basal conditions and potentiated the stimulatory effect of EGF. K(ATP) or KvLQT1 inhibitors (glibenclamide, clofilium) reduced EGF-stimulated wound healing, cell migration, and proliferation. Finally, EGF stimulated K(ATP) and KvLQT1 currents and channel expression. In summary, stimulation of K(+) channels through autocrine activation of EGF receptors could play a crucial role in lung epithelia repair processes.
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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 | 0.001 |
| 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".