Molecular identity and function in transepithelial transport of K<sub>ATP</sub> channels in alveolar epithelial cells
Bibliographic record
Abstract
K(+) channels play a crucial role in epithelia by repolarizing cells and maintaining electrochemical gradient for Na(+) absorption and Cl(-) secretion. In the airway epithelium, the most frequently studied K(+) channels are KvLQT1 and K(Ca). A functional role for K(ATP) channels has been also suggested in the lung, where K(ATP) channel openers activate alveolar clearance and attenuate ischemia-reperfusion injury. However, the molecular identity of this channel is unknown in airway and alveolar epithelial cells (AEC). We adopted an RT-PCR strategy to identify, in AEC, cDNA transcripts for Kir channels (Kir6.1 or 6.2) and sulfonylurea receptors (SUR1, 2A, or 2B) forming K(ATP) channels. Only Kir6.1 and SUR2B were detected in freshly isolated and cultured alveolar cells. To determine the physiological role of K(+) channels in the transepithelial transport of alveolar monolayers, we studied the effect, on total short-circuit currents (I(sc)), of basolateral application of glibenclamide, an inhibitor of K(ATP) channels, as well as clofilium, charybdotoxin, clotrimazole, and iberiotoxin, inhibitors of KvLQT1 and K(Ca) channels, respectively. Interestingly, activity of the three types of K(+) channels was detected, since all tested inhibitors decreased I(sc). Furthermore, these K(+) channel inhibitors reduced amiloride-sensitive Na(+) currents (mediated by ENaC) and completely abolished stimulation of Cl(-) currents by forskolin. Conversely, pinacidil, an activator of K(ATP) channels, increased Na(+) and Cl(-) transepithelial transport by 33-35%. These results suggest the presence, in AEC, of a K(ATP) channel, formed from Kir6.1 and SUR2B subunits, which plays a physiological role, with KvLQT1 and K(Ca) channels, in Na(+) and Cl(-) transepithelial transport.
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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 | 0.000 |
| 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".