Vascular Nitric Oxide and Oxidative Stress: Determinants of Endothelial Adaptations to Cardiovascular Disease and to Physical Activity
Bibliographic record
Abstract
Cardiovascular disease is the single leading cause of death and morbidity for Canadians. A universal feature of cardiovascular disease is dysfunction of the vascular endothelium, thus disrupting control of vasodilation, tissue perfusion, hemostasis, and thrombosis. Nitric oxide bioavailability, crucial for maintaining vascular endothelial health and function, depends on the processes controlling synthesis and destruction of nitric oxide as well as on the sensitivity of target tissue to nitric oxide. Evidence supports a major contribution by oxidative stress-induced destruction of nitric oxide to the endothelial dysfunction that accompanies a number of cardiovascular disease states including hypertension, diabetes, chronic heart failure, and atherosclerosis. Regular physical activity (exercise training) reduces cardiovascular disease risk. Numerous studies support the hypothesis that exercise training improves vascular endothelial function, especially when it has been impaired by preexisting risk factors. Evidence is emerging to support a role for improved nitric oxide bioavailability with training as a result of enhanced synthesis and reduced oxidative stress-mediated destruction. Molecular targets sensitive to the exercise training effect include the endothelial nitric oxide synthase and the antioxidant enzyme superoxide dismutase. However, many fundamental details of the cellular and molecular mechanisms linking exercise to altered molecular and functional endothelial phenotypes have yet to be discovered. The working hypothesis is that some of the cellular mechanisms contributing to endothelial dysfunction in cardiovascular disease can be targeted and reversed by signals associated with regular increases in physical activity. The capacity for exercise training to regulate vascular endothelial function, nitric oxide bioavailability, and oxidative stress is an example of how lifestyle can complement medicine and pharmacology in the prevention and management of cardiovascular disease.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".