Serum myostatin decreases in exercising and aging Alaskan sled dogs, while growth and differentiation factor 15 remains unaltered
Bibliographic record
Abstract
OBJECTIVES: To evaluate the serum concentrations of myostatin and growth and differentiation factor 15 (GDF-15) in Alaskan Husky sled dogs participating in a 350-mile (560-km) race and in an older population, and to examine correlations between changes in serum concentrations and body condition scores (BCSs). ANIMALS: Dogs were recruited from 3 teams of Alaskan Huskies participating in the Alaskan-Yukon Quest sled-dog race and retirees from a research sled-dog colony. PROCEDURES: Serum samples and BCSs were collected prior to racing, midway, and postrace; and in an older cohort (13 to 14 years). Myostatin and GDF-15 concentrations were assessed using commercially available ELISA kits. RESULTS: The median myostatin prerace concentration (9,519 pg/mL) was significantly greater than the mid- and postrace concentrations (7,709 pg/mL and 3,247 pg/mL, respectively). The prerace concentration was also significantly greater than that of the retired sled group dogs at 6,134 pg/mL. GDF-15 median serum concentrations did not change significantly across any racing time point (approx 350 pg/mL) or in the older cohort. No significant correlations were observed between changes in BCS and myostatin or GDF-15 concentrations. CLINICAL RELEVANCE: Serum myostatin decreases dramatically, yet no correlations to loss of BCS could be found. Myostatin signaling may be involved in maintaining hypertrophic signaling during intense exercise. Neither racing distance nor geriatric/retirement status appears to have an effect on serum GDF-15 concentration. Myostatin was less in the older, retired sled dogs compared to the younger racing cohort. Such differences highlight the roles that fitness level and age play regarding myostatin levels.
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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) | 0.001 | 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".