Endurance training prevents, while high intensity interval training exacerbates, molecular markers of heart failure in cardiac muscle of hypertensive rats
Bibliographic record
Abstract
In recent years there has been considerable interest focused on investigating the metabolic effects of high intensity interval training (HIIT). HIIT is a time efficient alternative to classic endurance training (ET) that elicits similar metabolic responses in skeletal muscle. However, there is a lack of information regarding the impact of HIIT on cardiac muscle in disease states. Therefore, in a rodent model of hypertension-induced heart failure (HF), before overt HF developed, we determined the efficacy of ET and HIIT in ameliorating pathological remodeling. ET decreased left ventricle (LV) fibrosis by ~40% (P < 0.05), and promoted a 20% (P<0.05) increase in the LV capillary/fibre ratio, an increase in endothelial nitric oxide synthase protein (P<0.05), and a decrease in hypoxia inducible factor 1 alpha protein content (P<0.05). In contrast, HIIT did not decrease existing fibrosis, and HIIT animals developed a 20% (P<0.05) increase in LV mass in the absence of concomitant angiogenesis. These factors, along with a 50% increase (P<0.05) in the content of brain natriuretic peptide, strongly suggested pathological remodeling in response to HIIT. The current data support the longstanding belief in the effectiveness of ET in primary and secondary prevention in individuals with cardiovascular diseases. In contrast, HIIT exacerbated LV pathological remodeling LV in hypertensive rats, suggesting more investigation is required prior to commonplace application of HIIT in cardiovascular disease conditions.
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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) | 0.001 | 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 | 0.001 |
| 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".