Cerebral hemodynamics and resistance exercise
Bibliographic record
Abstract
PURPOSE: Repetitive resistance exercise with large muscle mass causes rapid fluctuations in mean arterial blood pressure (MAP). We sought to determine the effect of these fluctuations on the cerebrovasculature response determined by mean flow velocity (Vmean) of the middle cerebral artery. METHODS: Nine subjects performed 10-repetition maximum leg press exercise. MAP was estimated by finger photoplethysmography, Vmean by Doppler ultrasound, and end-tidal CO2 (PETCO2) by mass spectrometry. RESULTS: Vmean fluctuated with MAP with each repetition however averaged over the 10 repetitions, Vmean was unchanged from resting baseline values (66.9 +/- 10.8 vs 67.7 +/- 12.3 cm.s-1, baseline vs exercise, P > 0.05) despite an increased MAP (89.5 +/- 8.4 vs 105.0 +/- 4.9 Torr, P < 0.05). PETCO2 also remained unchanged from rest to exercise (37.7 +/- 2.8 vs 36.6 +/- 2.7 Torr, P > 0.05). Vmean decreased below resting levels for the first 5 s of recovery (59.8 +/- 9.1 cm.s-1, P < 0.05) as MAP returned rapidly to slightly below baseline (83.3 +/- 6.1, P > 0.05). MAP/Vmean, an index of cerebrovascular resistance, was elevated during exercise and returned to baseline after exercise. An increase in Vmean at 30 s post exercise (78.4 +/- 10.6 cm.s-1, P < 0.05) corresponded with elevated PETCO2 (43.0 +/- 4.8 Torr, P > 0.05). CONCLUSION: The results suggest that fluctuations in MAP with individual muscle contractions during resistance exercise appear to be too rapid to be countered by cerebrovascular autoregulation. However, the progressive increase in MAP over a number of contractions was effectively countered to maintain Vmean near baseline values before a decrease in Vmean immediately after 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) | 0.000 | 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".