K<sub>IR</sub> channels function as electrical amplifiers in rat vascular smooth muscle
Bibliographic record
Abstract
Strong inward rectifying K(+) (K(IR)) channels have been observed in vascular smooth muscle and can display negative slope conductance. In principle, this biophysical characteristic could enable K(IR) channels to 'amplify' responses initiated by other K(+) conductances. To test this, we have characterized the diversity of smooth muscle K(IR) properties in resistance arteries, confirmed the presence of negative slope conductance and then determined whether K(IR) inhibition alters the responsiveness of middle cerebral, coronary septal and third-order mesenteric arteries to K(+) channel activators. Our initial characterization revealed that smooth muscle K(IR) channels were highly expressed in cerebral and coronary, but not mesenteric arteries. These channels comprised K(IR)2.1 and 2.2 subunits and electrophysiological recordings demonstrated that they display negative slope conductance. Computational modelling predicted that a K(IR)-like current could amplify the hyperpolarization and dilatation initiated by a vascular K(+) conductance. This prediction was consistent with experimental observations which showed that 30 mum Ba(2+) attenuated the ability of K(+) channel activators to dilate cerebral and coronary arteries. This attenuation was absent in mesenteric arteries where smooth muscle K(IR) channels were poorly expressed. In summary, smooth muscle K(IR) expression varies among resistance arteries and when channel are expressed, their negative slope conductance amplifies responses initiated by smooth muscle and endothelial K(+) conductances. These findings highlight the fact that the subtle biophysical properties of K(IR) have a substantive, albeit indirect, role in enabling agonists to alter the electrical state of a multilayered artery.
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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".