Acute endurance exercise increases plasma membrane fatty acid transport proteins in rat and human skeletal muscle
Bibliographic record
Abstract
Fatty acid transport proteins are present on the plasma membrane and are involved in the uptake of long-chain fatty acids into skeletal muscle. The present study determined whether acute endurance exercise increased the plasma membrane content of fatty acid transport proteins in rat and human skeletal muscle and whether the increase was accompanied by an increase in long-chain fatty acid transport in rat skeletal muscle. Sixteen subjects cycled for 120 min at ∼60 ± 2% Vo(2) peak. Two skeletal muscle biopsies were taken at rest and again following cycling. In a parallel study, eight Sprague-Dawley rats ran for 120 min at 20 m/min, whereas eight rats acted as nonrunning controls. Giant sarcolemmal vesicles were prepared, and protein content of FAT/CD36 and FABPpm was measured in human and rat vesicles and whole muscle homogenate. Palmitate uptake was measured in the rat vesicles. In human muscle, plasma membrane FAT/CD36 and FABPpm protein contents increased 75 and 20%, respectively, following 120 min of exercise. In rat muscle, plasma membrane FAT/CD36 and FABPpm increased 20 and 30%, respectively, and correlated with a 30% increase in palmitate transport following 120 min of running. These data suggest that the translocation of FAT/CD36 and FABPpm to the plasma membrane in rat skeletal muscle is related to the increase in fatty acid transport and oxidation that occurs with endurance running. This study is also the first to demonstrate that endurance cycling induces an increase in plasma membrane FAT/CD36 and FABPpm content in human skeletal muscle, which is predicted to increase fatty acid transport.
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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".