Vascular conductance and muscle blood flow during exercise are altered by inspired oxygen fraction and arterial perfusion pressure
Bibliographic record
Abstract
Abstract We tested the hypothesis during the combined challenges of altered inspired O2 fraction (FIO2) and posture changes at lower power output regardless of body position that the vascular conductance (VC) recruitment to the exercising muscle would not limit muscle perfusion and estimated O2 delivery (DO2est). However, in head‐down tilt at the higher power output exercise in hypoxia, the recruitment of VC would have a functional limitation which would restrict muscle blood flow (MBF) leading to a limitation in DO2est with consequent increases in metabolic stress. Ten healthy volunteers repeated plantar flexion contractions at 20% (low power output = LPO) and 30% (higher power output = HPO) of their maximal voluntary contraction in horizontal (HOR), 35° head‐down‐tilt (HDT) and 45° head‐up‐tilt (HUT). Popliteal diameter and muscle blood flow velocity were measured by ultrasound determining MBF. VC was estimated by dividing MBF flow by MPP, and DO2est was estimated by MBF times saturation. LPOHUT in hypoxia was associated with no changes in VC and MBF leading to reduced DO2est. In LPOHDT under hypoxia, despite no apparent functional limitation in the VC recruitment, rise in MBF to maintain DO2est was associated with marked increase in muscle electromyographic activity, indicating greater metabolic stress. In HPOHDT under hypoxia, a functional limitation for the recruitment of VC constrained MBF and DO2est. Elevated muscle electromyographic signal in HPOHDT under hypoxia was consistent with challenged aerobic metabolisms which contributed to a greater increase in the relative stress of the exercise challenge and advance the onset of muscle fatigue.
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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) | 0.001 | 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".