Effects of estrogen therapy and aerobic training on sympathetic activity and hemodynamics in healthy postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the isolated and associated effects of aerobic training and estrogen therapy on sympathetic nerve activity and hemodynamics in healthy postmenopausal women. METHODS: Forty-five postmenopausal women (mean [SD] age, 51 [3] y) were randomly divided into four groups: sedentary-placebo (SED-PLA; n = 11), sedentary-estrogen therapy (SED-ET; n = 14), aerobic training-placebo (AT-PLA; n = 12), and aerobic training-estrogen therapy (AT-ET; n = 8). The ET groups received oral estradiol valerate (1 mg/d), whereas the PLA groups received placebo. The AT groups performed aerobic exercise three times a week on a cycle ergometer for 50 minutes, whereas the SED groups remained sedentary. All participants were evaluated before and after 6 months. Muscle sympathetic nerve activity (MSNA; microneurography), forearm blood flow (plethysmography), blood pressure (oscillometry), and heart rate (HR) were measured at rest for 10 minutes. Data were analyzed by three-way analysis of variance. RESULTS: Estrogen administration itself did not change any of the studied parameters. AT improved forearm blood flow (AT-PLA, 2.02 [0.85] vs 2.92 [1.65] mL min(-1) 100 mL(-1), P = 0.03; AT-ET, 1.68 [1.11] vs 2.27 [0.76] mL min(-1) 100 mL(-1), P = 0.03), reduced MSNA in the AT-PLA group (39 [6] vs 34 [5] bursts/min(-1), P = 0.01), and decreased HR in the AT-ET group (65 [8] vs 62 [7] beats/min, P = 0.01). CONCLUSIONS: AT reduces sympathetic nerve activity and improves muscle blood flow in healthy hysterectomized postmenopausal women. Moreover, AT decreases HR when combined with ET. However, ET abolishes the reducing effect of AT on MSNA.
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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".