Consumption of a high-fat-high-sucrose diet partly diminishes mechanical and structural adaptations of cardiac muscle following resistance training
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the effects of a high-fat high-sucrose (HFHS) diet on previously reported adaptations of cardiac morphological and contractile properties to resistance training. METHODS: Twelve-week-old rats participated in 12-weeks of resistance exercise training and consumed an HFHS diet. Echocardiography and skinned cardiac muscle fiber bundle testing were performed to determine the structural and mechanical adaptations. RESULTS: Compared to chow-fed sedentary animals, both HFHS- and chow-fed resistance-trained animals had thicker left ventricular walls. Isolated trabecular fiber bundles from chow-fed resistance-trained animals had greater force output, shortening velocities, and calcium sensitivities than those of chow-fed sedentary controls. However, trabeculae from the HFHS resistance-trained animals had greater force output but no change in unloaded shortening velocity or calcium sensitivity than those of the chow-fed sedentary group animals. CONCLUSION: Resistance exercise training led to positive structural and mechanical adaptations of the heart, which were partly offset by the HFHS diet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".