Vascular nitric oxide: effects of exercise training in animals
Bibliographic record
Abstract
Exercise training is known to induce several adaptations in the cardiovascular system, one of which is increased skeletal muscle blood flow at maximal exercise. Improved muscle blood flow, in turn, could in part be accounted for by augmented endothelium-dependent, nitric oxide (NO)-mediated vasodilation. Studies have indeed demonstrated that endothelium-dependent, NO-mediated dilation of conductance-type vessels is augmented after endurance exercise training; recently, this adaptation has been extended into resistance-type vessels within rodent skeletal muscle. With the latter, however, it appears that only resistance vessels supplying muscle active during training sessions exhibit this adaptation. These findings in rats are in contrast to those from human studies, in which increased endothelium-dependent dilation has been observed in vasculatures not associated with elevated blood flow during exercise. Increased expression of endothelial NO synthase (eNOS) appears to underlie enhanced endothelium-dependent, NO-mediated dilation of both conductance and resistance vessels. Greater eNOS expression may also underlie the preventive and (or) rehabilitative effect(s) of exercise training on atherosclerosis, given that NO inhibits several steps of the atherosclerotic disease process. Thus, exercise training may induce adaptations that benefit both vasodilation and vascular health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".