Metabolic Control of Muscle Blood Flow During Exercise in Humans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During muscle contraction, several mechanisms regulate blood flow to ensure a close coupling between muscle oxygen delivery and metabolic demand. No single factor has been identified to constitute the primary metabolic regulator, yet there are signal transduction pathways between skeletal muscle and the vasculature that induce vasodilation. A link between muscle metabolic events and microvascular control of blood flow is illustrated by local dilation of terminal arterioles during contraction of muscle fibers and conduction of vasodilation upstream. Endothelial-derived vasodilator mechanisms are known to exert control of muscle vasodilation. Adenosine, nitric oxide (NO), prostacyclin (PGI2), and endothelial-derived hyperpolarization factor (EDHF) are possible mediators of muscle vasodilation during exercise. In humans, adenosine has been shown to contribute to functional hyperemia as blood flow is reduced under nonselective adenosine-receptor blockade. No clear role has been demonstrated for either NO or PGI2(2), based on studies employing selective inhibition of these substances individually, suggesting a redundancy of vasodilator mechanisms. This is supported by recent work demonstrating that combined blockade of NOS and PGI2, and NOS and cytochrome P450, both attenuate exercise-induced hyperemia in humans. Combined vasodilator blockade studies offer the potential to uncover important interactions and compensatory vasodilator responses. The signaling pathways that link metabolic events evoked by muscle contraction to vasodilatory signals in the local vascular bed remains an important area of study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it