Inward rectifying potassium channels facilitate cell-to-cell communication in hamster retractor muscle feed arteries
Bibliographic record
Abstract
This study examined whether inward rectifying K+ (KIR) channels facilitate cell-to-cell communication along skeletal muscle resistance arteries. With the use of feed arteries from the hamster retractor muscle, experiments examined whether KIR channels were functionally expressed and whether channel blockade attenuated the conduction of acetylcholine-induced vasodilation, an index of cell-to-cell communication. Consistent with KIR channel expression, this study observed the following: 1) a sustained Ba2+-sensitive, K+-induced dilation in preconstricted arteries; 2) a Ba2+-sensitive inwardly rectifying K+ current in arterial smooth muscle cells; and 3) KIR2.1 and KIR2.2 expression in the smooth muscle layer of these arteries. It was subsequently shown that the discrete application of acetylcholine elicits a vasodilation that conducts with limited decay along the feed artery wall. In the presence of 100 microM Ba2+, the local and conducted response to acetylcholine was attenuated, a finding consistent with a role for KIR in facilitating cell-to-cell communication. A computational model of vascular communication accurately predicted these observations. Control experiments revealed that in contrast to Ba2+, ATP-sensitive- and large-conductance Ca2+ activated-K+ channel inhibitors had no effect on the local or conducted vasodilatory response to acetylcholine. We conclude that smooth muscle KIR channels play a key role in facilitating cell-to-cell communication along skeletal muscle resistance arteries. We attribute this facilitation to the intrinsic property of negative slope conductance, a biophysical feature common to KIR2.1- and 2.2-containing channels, which enables them to increase their activity as a cell hyperpolarizes.
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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 | 0.000 |
| 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".