Vasoregulatory mechanism response speed to step increases or decreases in exercise from steady state
Bibliographic record
Abstract
We tested the hypothesis that muscle vasoregulatory mechanisms respond rapidly to step increases and decreases in exercise intensity from steady state. 10 men and 9 women (21.8 ±3.6 yrs) performed repeated step‐transitions between 4 min bouts of low (L) and moderate (M) forearm handgrip exercise intensities. Forearm blood flow (FBF; Doppler ultrasound), mean arterial pressure (MAP, finometer), and heart rate (HR; ECG) were measured beat by beat. A cardiac cycle during each relaxation of the duty cycle (1s contract/2s relax) was used to quantify forearm hemodynamics (FBF relax ml/min; forearm vascular conductance, FVC relax ml/min/100 mmHg). Data mean ± SD. FBF relax increased rapidly following a step increase in exercise intensity (L steady state 243.1 ±103.6 vs. 1 st M relaxation 262.0 ±78.1, P = 0.075 vs. 2 nd M relaxation 289.8 ±82.2, P<0.001). This was due to a rapid vasodilation (FVC relax L steady state 258.8 ±93.0 vs. 1 st M relaxation 279.6 ± 71.8, P = 0.058; vs. 2 nd M relaxation 304.7 ±74.0, P<0.001). The same rapid responses were observed with decreases in exercise intensity (FVC relax M steady state 462.3 ± 120.5 vs. 1 st L relaxation 435.8 ±103.3, P<0.001; vs. 2 nd L relaxation 420.4 ±99.0, P<0.001). We conclude that vasoregulatory mechanisms are able to alter muscle blood flow rapidly and with little delay in response to sudden increases and decreases in exercise intensity from steady state. Supported by NSERC Canada.
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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 | 0.000 |
| 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".