Why are Smooth Muscle Responses Unable to Conduct Along Skeletal Muscle Arteries?
Bibliographic record
Abstract
Electrical responses initiated in endothelial but not smooth muscle cells conduct robustly along skeletal muscle resistance arteries. The origin of this dichotomy is uncertain and was the focus of this investigation. Computational models based on physical and electrical properties of vascular cells provide two rationalizations for the inability of smooth muscle responses to conduct. First, given the differential orientation of vascular cells and the known variability in coupling resistance, it is conceivable that a resistance artery is inherently designed to limit the conduction of smooth muscle‐initiated responses. This view is consistent with the inability of a high K + solution, discretely applied to stimulate smooth muscle, to elicit a conducted response. Second, given the passive nature of an artery's electrical properties, some smooth muscle agonists may fail to elicit the local electrical response required for conduction. This was confirmed by focally applying phenylephrine and monitoring vasomotor and electrical responses in hamster retractor muscle feed arteries. Further experiments revealed no evidence that the electrical feedback mechanisms in smooth muscle or endothelial cells limit the conduction of smooth muscle responses. In closing, this study highlights that the inability of smooth muscle responses to conduct is due to the inherent biophysical properties of resistance arteries. Supported by the Heart and Stroke Foundation of Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".