Effects of Exercise in Volume Overload
Bibliographic record
Abstract
BACKGROUND: Aortic valve regurgitation (AR) imposes a pathologic volume overload to the left ventricle (LV), whereas aerobic exercise causes physiologic volume overloading. The impact of combining both LV volume overloads (pathologic and physiologic) is unknown. Considering the known beneficial effects of aerobic training on the cardiovascular system, we hypothesized that the positive effects would outweigh the negative ones and that exercise would improve the tolerance of the LV to AR. METHODS: Forty female adult Wistar rats were randomly divided in the following groups: 1) sham sedentary (SS), 2) sham trained (ST), 3) AR sedentary (ARS), and 4) AR trained (ART). Training consisted in treadmill running for 30 min five times per week at 20 m x s(-1) for 24 wk. In vivo follow-up was made by echocardiography and invasive intracardiac pressure measurements. Hearts were harvested for tissue analysis. RESULTS: Echocardiography revealed less LV dilation and hypertrophy in ART versus ARS as well as improved myocardial performance index. LV ejection fractions remained similar and within normal range in ART versus ARS. Invasive cardiac pressures yielded improved dP/dt- in ART versus ARS but similar dP/dt+. beta(1)-Adrenergic receptor mRNA expression was improved in the ART group versus ARS. CONCLUSION: Our data suggest that a moderate aerobic exercise program helps minimize LV dilation and hypertrophy and improves diastolic cardiac performance in heart submitted to chronic volume overload due to severe aortic valve regurgitation in this animal model.
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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 | 0.000 |
| 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) | 0.002 | 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".