Does sympathetic vasoconstriction contribute to metabolism: Perfusion matching in exercising skeletal muscle?
Bibliographic record
Abstract
The process of matching skeletal muscle blood flow to metabolism is complex and multi-factorial. In response to exercise, increases in cardiac output, perfusion pressure and local vasodilation facilitate an intensity-dependent increase in muscle blood flow. Concomitantly, sympathetic nerve activity directed to both exercising and non-active muscles increases as a function of exercise intensity. Several studies have reported the presence of tonic sympathetic vasoconstriction in the vasculature of exercising muscle at the onset of exercise that persists through prolonged exercise bouts, though it is blunted in an exercise-intensity dependent manner (functional sympatholysis). The collective evidence has resulted in the current dogma that vasoactive molecules released from skeletal muscle, the vascular endothelium, and possibly red blood cells produce local vasodilation, while sympathetic vasoconstriction restrains vasodilation to direct blood flow to the most metabolically active muscles/fibers. Vascular smooth muscle is assumed to integrate a host of vasoactive signals resulting in a precise matching of muscle blood flow to metabolism. Unfortunately, a critical review of the available literature reveals that published studies have largely focused on bulk blood flow and existing experimental approaches with limited ability to reveal the matching of perfusion with metabolism, particularly between and within muscles. This paper will review our current understanding of the regulation of sympathetic vasoconstriction in contracting skeletal muscle and highlight areas where further investigation is necessary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".