Mechanistic basis of differential conduction in skeletal muscle arteries
Bibliographic record
Abstract
The goal of this investigation was to probe intercellular conduction in skeletal muscle feed arteries and to address why smooth muscle-initiated responses fail to robustly spread like their endothelial counterpart. Using computational and experimental approaches, two interrelated rationales were developed to explain this apparent discrepancy in cell-to-cell communication. The first rationale stressed that smooth muscle electrical responses, if initiated, will be actively dissipated as they spread from cell-to-cell along the arterial wall. Charge dissipation is promoted within arteries by the structural and connectivity properties of vascular cells. The second rationale centred on the idea that when agents other than KCl stimulate a limited number of smooth muscle cells, they fail to generate the currents required to elicit a localized membrane potential (V(M)) response. This insufficiency results in part from charge loss, via gap junctions, to neighbouring unstimulated cells. Experiments confirmed the latter rationale by showing that focal phenylephrine application: (1) elicited a localized constriction insensitive to L-type Ca(2+) channel blockade; and (2) failed to substantially depolarize vascular smooth muscle cells. Further investigation revealed that while focal phenylephrine-induced constriction was V(M) independent, it was reliant on internal Ca(2+) mobilization and the activation of inositol 1,4,5-trisphosphate (IP(3)) receptors. The preceding findings illustrate that by using computational modelling and experimentation in a complementary manner, one can isolate key cellular properties and rationally examine their role in limiting the conduction of smooth muscle-initiated responses. Functionally, these observations enable investigators to assign the concept of 'local and global' blood flow control to the electrical and/or non-electrical behaviour of specific cell types.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
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".