Conditioning Increases the Gain of Contraction-Induced Sarcolemmal Substrate Transport in Ultra-Endurance Racing Sled Dogs
Bibliographic record
Abstract
Endurance exercise relies on transsarcolemmal flux of substrates in order to avoid depletion of intramuscular reserves. Previous studies of endurance trained sled dogs have shown a remarkable capacity of these dogs to adapt rapidly to endurance exercise by decreasing the utilization of intramuscular reserves. The current study tested the hypothesis that the dogs' glycogen-sparing phenotype is due to increased sarcolemmal transport of glucose and fatty acids. Basal and exercise-induced transport of glucose and fatty acids into sarcolemmal vesicles was evaluated in racing sled dogs prior to and after 7 months of exercise conditioning. Sarcolemmal substrate transport capacity was measured using sarcolemmal vesicles and radiolabelled substrates, and transporter abundance was measured using Western blot quantification in whole muscle homogenates and the sarcolemmal vesicle preparations. Conditioning resulted in increased basal and exercise-induced transport of both glucose and palmitate. Neither acute exercise nor conditioning resulted in changes in muscle content of GLUT4 or FAT/CD36, but conditioning did result in decreased abundance of both transporters in the sarcolemmal vesicles used for the basal transport assays, and this decrease was further amplified in the vesicles used for the exercise-induced transport assays. These results demonstrate conditioning-induced increases in sarcolemmal transport of oxidizable substrates, as well as increased gain of exercise-induced sarcolemmal transport of these substrates. These results further indicate that increased sarcolemmal transport of oxidizable substrates may be due to either an increased intrinsic capacity of the existing transporters or to a different population of transporters from those investigated.
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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 | 0.001 |
| 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".