Improvement of Endurance Based on Muscle Fiber-Type Composition by Treatment with Dietary Apple Polyphenols in Rats
Bibliographic record
Abstract
A recent study demonstrated a positive effect of apple polyphenol (APP) intake on muscle endurance of young-adult animals. While an enhancement of lipid metabolism may be responsible, in part, for the improvement, the contributing mechanisms still need clarification. Here we show that an 8-week intake of 5% (w/w) APP in the diet, up-regulates two features related to fiber type: the ratio of myosin heavy chain (MyHC) type IIx/IIb and myoglobin protein expression in plantaris muscle of 9-week-old male Fischer F344 rats compared to pair-fed controls (P < 0.05). Results were demonstrated by our SDS-PAGE system specialized for MyHC isoform separation and western blotting of whole muscles. Animal-growth profiles (food intake, body-weight gain, and internal-organ weights) did not differ between the control and 5% APP-fed animals (n = 9/group). Findings may account for the increase in fatigue resistance of lower hind limb muscles, as evidenced by a slower decline in the maximum isometric planter-flexion torque generated by a 100-s train of electrical stimulation of the tibial nerve. Additionally, the fatigue resistance was lower after 8 weeks of a 0.5% APP diet than after 5% APP, supporting an APP-dose dependency of the shift in fiber-type composition. Therefore, the present study highlights a promising contribution of dietary APP intake to increasing endurance based on fiber-type composition in rat muscle. Results may help in developing a novel strategy for application in animal sciences, and human sports and age-related health sciences.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".