Cardiovascular dynamics during exercise are related to blood rheology
Bibliographic record
Abstract
BACKGROUND: The principal determinants of oxygen uptake (VO2) kinetics are controversial, with dynamic changes in central and peripheral factors mediating oxygen supply and utilisation suggested to be limiting. The aim of this study was to determine whether important parameters of blood rheology were related to the exercise-induced time-course changes in VO2 and cardiac output (Qc), or steady-state arteriovenous oxygen difference (a-vO2D) during submaximal cycling. METHODS AND RESULTS: Blood was collected from ten healthy, recreationally active males and females (age: 21.7 ± 1.3 yr; body mass index: 22.7 ± 2.0 kg · m(-2)), before each subject cycled at 105% of the first ventilatory threshold. Red blood cell aggregation was negatively correlated with steady-state VO2 during exercise and the a-vO2D at rest (r = -0.73, p < 0.05), and positively correlated to Qc at rest (r = 0.71, p < 0.05). Blood viscosity at various shear rates was negatively correlated with the time constant of VO2 (all p < 0.01) on-transient kinetics. Red blood cell deformability at various shear stress was positively correlated to the time constant of VO2 (all p < 0.05) on-transient kinetics. CONCLUSIONS: The findings of the present study suggest that the rheological properties of blood may modulate, at least in part, the rate of change in the uptake and/or utilisation of oxygen at the onset of exercise.
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.
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 | 0.000 |
| 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".