Blood viscosity and hemodynamics during exercise
Bibliographic record
Abstract
We tested the effects of submaximal exercise on blood viscosity (η(b)), nitric oxide production (NO) and hemodynamics. Relationships between the exercise-induced changes that occurred in these parameters were investigated. Nine subjects performed exercise for 15 min at 105% of the first ventilatory threshold. Mean arterial pressure (MAP) and cardiac output (Qc) were measured, allowing the determination of systemic vascular resistance (SVR). Blood was sampled at rest and at the end of exercise. The η(b) was determined at high shear rate and was used to calculate systemic vascular hindrance (VH). NO production was estimated by measuring plasma concentrations of NO stable end products (NOx). Qc, MAP, η(b) and NOx, increased with exercise, whereas SVR and VH decreased. The changes between rest and exercise were calculated and tested for correlations. We observed: 1) a positive correlation between the increase in η(b) and the increase in NOx; 2) a negative correlation between the increase in NOx and the decrease in VH; 3) a negative correlation between the increase in η(b) and the decrease in SVR. Although the increase in Qc and blood flow during exercise probably promoted NO production due to shear dependent stimulation of the endothelium, the present results also support that the rise in η(b) during exercise may be necessary for NO production and adequate vasodilation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".