Does oscillation size matter? Impact of added resistance on the cerebral pressure‐flow Relationship in females and males
Bibliographic record
Abstract
Sinusoidal squat-stand maneuvers (SSM) without resistance have been shown to produce ~30-50 mmHg swings in mean arterial pressure which are largely buffered in the brain via dynamic cerebral autoregulation (dCA). This study aimed to further elucidate how this regulatory mechanism is affected during SSM with added resistance (~20% bodyweight). Twenty-five participants (sex/gender: 13 females/12 males) completed two bouts of 5-min SSM for both bodyweight and resistance conditions (10% bodyweight in each arm) at frequencies of 0.05 Hz (20-s squat/stand cycles) and 0.10 Hz (10-s squat/stand cycles). Middle and posterior cerebral artery (MCA/PCA) cerebral blood velocities were indexed with transcranial Doppler ultrasound. Beat-to-beat blood pressure (BP) was quantified via finger photoplesmography. Transfer function analysis was employed to quantify dCA in both cerebral arteries across the cardiac cycle (diastole, mean, and systole). Two-by-two Analysis of Variance with generalized eta squared effect sizes were utilized to determine differences between resistance vs. bodyweight squats and between sexes/genders. Absolute mean and diastolic BP were elevated during the resistance squats (p < 0.001); however, only the BP point-estimate power spectrum densities were augmented at 0.10 Hz (p < 0.048). No differences were noted for phase and gain metrics between bodyweight and resistance SSM (p > 0.067); however, females displayed attenuated systolic regulation (p < 0.003). Despite augmented systemic BP during resistance SSM, the brain was effective at buffering the additional stress to mitigate overperfusion/pressure. Females displayed less dCA regulation within the systolic aspect of the cardiac cycle, which may be associated with physiological underpinnings related to various clinical conditions/presentations.
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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) | 0.005 | 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".