Divergent response of metabolite transport proteins in human skeletal muscle after sprint interval training and detraining
Bibliographic record
Abstract
Skeletal muscle primarily relies on carbohydrate (CHO) for energy provision during high-intensity exercise. We hypothesized that sprint interval training (SIT), or repeated sessions of high-intensity exercise, would induce rapid changes in transport proteins associated with CHO metabolism, whereas changes in skeletal muscle fatty acid transporters would occur more slowly. Eight active men (22 +/- 1 yr; peak oxygen uptake = 50 +/- 2 ml.kg(-1).min(-1)) performed 4-6 x 30 s all-out cycling efforts with 4-min recovery, 3 days/wk for 6 wk. Needle muscle biopsy samples (vastus lateralis) were obtained before training (Pre), after 1 and 6 wk of SIT, and after 1 and 6 wk of detraining. Muscle oxidative capacity, as reflected by the protein content of cytochrome c oxidase subunit 4 (COX4), increased by approximately 35% after 1 wk of SIT and remained higher compared with Pre, even after 6 wk of detraining (P < 0.05). Muscle GLUT4 content increased after 1 wk of SIT and remained approximately 20% higher compared with baseline during detraining (P < 0.05). The monocarboxylate tranporter (MCT) 4 was higher after 1 and 6 wk of SIT compared with Pre, whereas MCT1 increased after 6 wk of training and remained higher after 1 wk of detraining (P < 0.05). There was no effect of training or detraining on the muscle content of fatty acid translocase (FAT/CD36) or plasma membrane associated fatty acid binding protein (FABPpm) (P > 0.05). We conclude that short-term SIT induces rapid increases in skeletal muscle oxidative capacity but has divergent effects on proteins associated with glucose, lactate, and 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 | 0.001 |
| 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) | 0.001 | 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".