High-intensity interval training increases intrinsic rates of mitochondrial fatty acid oxidation in rat red and white skeletal muscle
Bibliographic record
Abstract
High-intensity interval training (HIIT) can increase mitochondrial volume in skeletal muscle. However, it is unclear whether HIIT alters the intrinsic capacity of mitochondrial fatty acid oxidation, or whether such changes are associated with changes in mitochondrial FAT/CD36, a regulator of fatty acid oxidation, or with reciprocal changes in the nuclear receptor coactivator (peroxisome proliferator-activated receptor gamma coactivator-1alpha (PGC-1α)) and the corepressor (receptor-interacting protein 140 (RIP140)). We examined whether HIIT alters fatty acid oxidation rates in the isolated subsarcolemmal (SS) and intermyofibrillar (IMF) mitochondria of red and white skeletal muscle and (or) induces changes in muscle PGC-1α and RIP140 proteins and mitochondrial FAT/CD36 protein content. Rats were divided into untrained or HIIT-trained groups. HIIT animals performed 10 bouts of 1-min high-intensity treadmill running (30-55 m·min(-1)), separated by 2 min of rest, for 5 days a week for 4 weeks. As expected, after the training period, HIIT increased mitochondrial enzymes (citrate synthase, COXIV, and β-hydroxyacyl CoA dehydrogenase) in red and white muscle, indicating that muscle mitochondrial volume had increased. HIIT also increased the rates of palmitate oxidation in mitochondria of red (37% for SS and 19% for IMF) and white (36% for SS and 12% for IMF) muscle. No changes occurred in SS and IMF mitochondrial FAT/CD36 proteins, despite increasing FAT/CD36 at the whole-muscle level (27% for red and 22% for white). Concurrently, muscle PGC-1α protein was increased in red (22%) and white (16%) muscle, but RIP140 was not altered. These results indicate that increases in SS and IMF mitochondrial fatty acid oxidation induced by HIIT are accompanied by an increase in PGC-1α, but not RIP140 or FAT/CD36.
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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".