Evidence of K<sup>+</sup>channel function in epithelial cell migration, proliferation, and repair
Bibliographic record
Abstract
Efficient repair of epithelial tissue, which is frequently exposed to insults, is necessary to maintain its functional integrity. It is therefore necessary to better understand the biological and molecular determinants of tissue regeneration and to develop new strategies to promote epithelial repair. Interestingly, a growing body of evidence indicates that many members of the large and widely expressed family of K(+) channels are involved in regulation of cell migration and proliferation, key processes of epithelial repair. First, we briefly summarize the complex mechanisms, including cell migration, proliferation, and differentiation, engaged after epithelial injury. We then present evidence implicating K(+) channels in the regulation of these key repair processes. We also describe the mechanisms whereby K(+) channels may control epithelial repair processes. In particular, changes in membrane potential, K(+) concentration, cell volume, intracellular Ca(2+), and signaling pathways following modulation of K(+) channel activity, as well as physical interaction of K(+) channels with the cytoskeleton or integrins are presented. Finally, we discuss the challenges to efficient, specific, and safe targeting of K(+) channels for therapeutic applications to improve epithelial repair in vivo.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".