Local control of blood flow during active hyperaemia: what kinds of integration are important?
Bibliographic record
Abstract
The focus of this review is on local mechanisms modifying arteriolar resistance to match blood flow to metabolism. In skeletal muscle many local mediators are known, including K(+) , nitric oxide (NO), purines and prostaglandins. Each accounts for about 30% of the response; it is widely held that these act redundantly: this concept awaits systematic testing. Understanding signal integration also requires consideration of microvascular network morphology in relation to local communication pathways between endothelial and smooth muscle cells (which are critical for many local responses, including dilatation to skeletal muscle contraction) and in relation to the spread of vasodilator signals up- and downstream throughout the network. Mechanisms mediating the spread of dilatation from local to remote sites have been well studied using acetylcholine (ACh), but remote dilatations to contraction of skeletal muscle fibres also occur. Importantly, these mechanisms clearly differ from those initiated by ACh, but much remains undefined. Furthermore, capillaries contribute to metabolic dilatation as they dilate arterioles directly upstream in response to vasoactive agents or contraction of adjacent muscle fibres. Given the dispersed arrangement of motor units, precise matching of flow to metabolism is not attainable unless signals are initiated only by 'active' capillaries. As motor units are recruited, signals that direct blood flow towards these active fibres will eventually be supported by local and spreading responses in the arterioles associated with those fibres. Thus, mechanisms of integration of vasodilator signalling across elements of the microvasculature remain an important area of focus for new studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".