Assessment of baroreflex sensitivity during isometric handgrip exercise and oscillatory lower body negative pressure
Bibliographic record
Abstract
Objectives: Baroreflex sensitivity (BRS) is an estimate of autonomic control of cardiovascular system via the baroreflex arc. It has been suggested that exercise pressure reflex and muscle metaboreflex override baroreflex during exercise to decrease baroreflex gain, which facilitates the simultaneous rise in blood pressure (BP) and heart rate during the exercise. This study investigated the effects of isometric handgrip exercise (IHE) on baroreflex gain and frequency dependence of baroreflex sensitivity while fluctuations in arterial BP were generated. Methods: Thirteen healthy men performed IHE at 20% and 30% of their maximum voluntary contraction (MVC), while oscillatory lower body negative pressure (OLBNP) of 40 mmHg was applied in 0.1 and 0.25 Hz frequencies. Results: Compared to the OLBNP at 0.25 Hz frequency alone, the baroreflex gain for diastolic BP (DBP) was significantly reduced with the addition of IHE at 20% and 30% of MVC in the high frequency band. At rest (without IHE and OLBNP) the baroreflex gain was significantly more in the high frequency band for DBP, but the baroreflex gain for DBP was not significantly different when IHE + OLBNP were applied at 20% and 30% of MVC in both frequencies. Conclusions: The significant reduction of DBP baroreflex gain with the addition of graded IHE might indicate that exercise pressure reflex and muscle metaboreflex override baroreflex during exercise to decrease baroreflex gain at a high frequency band (0.25 Hz). The frequency-dependent phenomenon of BRS was altered when IHE and OLBNP were applied, meaning that the frequency dependence of BRS was nullified during IHE.
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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) | 0.001 | 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".