Effect of altered arterial perfusion pressure on vascular conductance and muscle blood flow dynamic response during exercise in humans
Bibliographic record
Abstract
Changes in vascular conductance (VC) are required to counter changes in muscle perfusion pressure (MPP) to maintain muscle blood flow (MBF) during exercise. We investigated the recruitment of VC as a function of peak VC measured in three body positions at two different work rates to test the hypothesis that adaptations in VC compensated changes in MPP at low-power output (LPO), but not at high-power output (HPO). Eleven healthy volunteers exercised at LPO and HPO (repeated plantar flexion contractions at 20-30% maximal voluntary contraction, respectively) in horizontal (HOR), 35° head-down tilt (HDT), and 45° head-up tilt (HUT). Muscle blood flow velocity and popliteal diameter were measured by ultrasound to determine MBF, and VC was estimated by dividing MBF flow by MPP. Peak VC was unaffected by body position. The rates of increase in MBF and VC were significantly faster in HUT and slower in HDT than HOR, and rates were faster in LPO than HPO. During LPO exercise, the increase in, and steady-state values of, MBF were less for HUT and HDT than HOR; the increase in VC was less in HUT than HOR and HDT. During HPO exercise, MBF in the HDT was reduced compared with HOR and HUT, even though VC reached 92% VC peak, which was greater than HOR, which was, in turn, greater than HUT. Reduced MBF during HPO HDT exercise had the functional consequence of a significant increase in muscle electromyographic index, revealing the effects of MPP on O2 delivery during exercise.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".