The Effect of Prior Exercise on the Slow Component of VO2, Leg Blood Flow and Muscle Deoxygenation
Bibliographic record
Abstract
PURPOSE: The effect of prior exercise on the slow component of pulmonary O2 uptake (VO2), and associated leg blood flow (LBF) and muscle deoxygenation during heavy-intensity (HVY; Δ 50%) alternate-leg knee-extension (KE) exercise was examined. METHODS: Eight subjects (27 ± 5 yrs; mean ± SD) performed step transitions (n=3; 8 min) to HVY KE from a baseline of passive KE preceded by either no warm-up (CON) or an identical bout of HVY KE (8 min; HWU). VO2p was measured breath-by-breath; LBF was measured by Doppler ultrasound at the femoral artery; and oxy-(HbO2), deoxy-(HHb) and total (Hbtot) hemog lob in/my oglobin of the vastus lateralis muscle were measured continuously by near-infrared spectroscopy (NIRS; Hamamatsu NIRO-300). Phase II VO2p data were fit with a mono-exponential model and the phase II-III transition determined by procedures of Rossiter et al. (J. Physiol., 2002). The VO2p slow component amplitude was calculated as the VO2p difference between the phase II-III transition and end-exercise. The associated changes in LBF and HHb were determined for the same time period. RESULTS: HWU did not affect the time of onset (CON: 226 ± 25 s; HWU: 223 ± 34 s) and amplitude (CON: 0.18 ±0.08 L/min; HWU: 0.18 ± 0.09 L/min) of the VO2p slow component. The VO2p slow component was accompanied by a slow component like increase in LBF and HHb during both CON and HWU. However the increase in LBF (CON: 0.29 ± 0.08 L/min; HWU: 0.07 ± 0.09 L/min) and HHb (CON: 6.5 ± 2.4 μM; HWU: 3.6 ± 1.7 μM) during the slow component were smaller (p<0.05) in HWU. CONCLUSION: These results demonstrate that during KE exercise, the VO2p slow component was not affected by prior HVY exercise, but muscle deoxygenation during the slow component was smaller in HWU suggesting an improved matching of local O2 muscle delivery and muscle O2 utilization. (NSERC)
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) | 0.002 | 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".