EGF and K<sup>+</sup>channel activity control normal and cystic fibrosis bronchial epithelia repair
Bibliographic record
Abstract
Severe lesions of airway epithelia are observed in cystic fibrosis (CF) patients. The regulatory mechanisms of cell migration and proliferation processes, involved in the repair of injured epithelia, then need to be better understood. A model of mechanical wounding of non-CF (NuLi) and CF (CuFi) bronchial monolayers was employed to study the repair mechanisms. We first observed that wound repair, under paracrine and autocrine EGF control, was slower (up to 33%) in CuFi than in NuLi. Furthermore, EGF receptor (EGFR) activation, following wounding, was lower in CuFi than in NuLi monolayers. Cell proliferation and migration assays indicated a similar rate of proliferation in both cell lines but with reduced (by 25%) CuFi cell migration. In addition, cell migration experiments performed in the presence of conditioned medium, collected from NuLi and CuFi wounded bronchial monolayers, suggested a defect in EGF/EGFR signaling in CF cells. We (49) recently demonstrated coupling between the EGF response and K(+) channel function, which is crucial for EGF-stimulated alveolar repair. In CuFi cells, lower EGF/EGFR signaling was accompanied by a 40-70% reduction in K(+) currents and KvLQT1, ATP-sensitive potassium (K(ATP)), and Ca(2+)-activated K(+) (KCa3.1) channel expression. In addition, EGF-stimulated bronchial wound healing, cell migration, and proliferation were severely decreased by K(+) channel inhibitors. Finally, acute CFTR inhibition failed to reduce wound healing, EGF secretion, and K(+) channel expression in NuLi. In summary, the delay in CuFi wound healing could be due to diminished EGFR signaling coupled with lower K(+) channel function, which play a crucial role in bronchial repair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".