Abstracts from the Conjoint Annual Meeting of L’Association des Pneumologues de la Province de Québec, la Société de Thoracologie du Québec and le Réseau en Santé Respiratoire du FRSQ
Bibliographic record
Abstract
Several respiratory diseases are associated with extensive damage of lung epithelia.The regulatory mechanisms involved in alveolar epithelia regeneration after injury are not clearly defined.Growth factors released by epithelial cells or fibroblasts from damaged lungs are important regulators of alveolar repair by stimulating cell motility, proliferation and differentiation.In addition, K+ channels have been shown to regulate cell proliferation and migration and to be coupled with growth factor signalling in several tissues.We decided to explore the hypothesis that K+ could play a prominent role in lung epithelia repair.This hypothesis has never been investigated before.We employed a model of mechanical wounding of alveolar epithelia to study the response to injury.Wound-healing was inhibited by half upon EGF titration with an EGF antibody, whereas the addition of exogenous EGF slightly stimulated the wound-healing of alveolar monolayers.EGF addition also stimulated, by up to 5 times, alveolar cell migration.The impact of K+ channel modulators was examined in basal and EGF-stimulated conditions.Wound-healing was stimulated by pinacidil, a K ATP activator, which also increased alveolar cell migration, by 2-fold, in basal conditions and potentiated the stimulatory effect of EGF.In the presence of K ATP or KvLQT1 inhibitors (glibenclamide, clofilium), wound-healing and cell migration were reduced by 45 to 75%.Finally, acute and long-term treatment with EGF stimulated K ATP and KvLQT1 channel activity and expression.In summary, stimulation of K+ channels through autocrine activation of EGF receptors plays 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.350 | 0.083 |
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".