Markers of muscle atrophy and impact of treatment with pergolide in horses with pituitary pars intermedia dysfunction and muscle atrophy.
Bibliographic record
Abstract
Abstract Pituitary pars intermedia dysfunction (PPID) is a common endocrine disorder of aged horses, with muscle atrophy as one of the clinical signs. We sought to compare muscle mass and regulation of skeletal muscle proteolysis between horses with PPID and muscle atrophy to older horses without PPID, and to assess the impact of treatment with pergolide (dopaminergic agonist) on PPID horses. We hypothesized that PPID-associated muscle atrophy is a result of increased proteolysis, and that markers of muscle atrophy and proteolysis would improve over time with pergolide treatment. Markers of muscle atrophy, adiposity, insulin regulation, skeletal muscle composition, and proteolysis (muscle atrophy F- box/atrogin 1 [MAFbx1], muscle RING finger 1 [MuRF1], Bcl2/adenovirus EIV 19kD interacting protein 3 [Bnip3], and microtubule-associated light chain 3 [LC3]) were compared between PPID and control horses. PPID horses were treated for 12 weeks with either pergolide or placebo. Dose of pergolide was adjusted based upon monthly measurement of adrenocorticotropin, and markers of muscle atrophy, adiposity, insulin regulation, skeletal muscle composition, and proteolysis were compared after 12 weeks of treatment. Horses with PPID exhibited increased transcript abundance of MuRF1 (P= 0.04) compared to control. However, no difference was observed in transcript abundance of markers of proteolysis with treatment (P ≥ 0.25). Pergolide treated horses lost weight (P = 0.02) and improved fasting insulin (P = 0.02), while placebo treated horses gained weight and rump fat thickness (P = 0.02). Findings from this study suggest that treatment with pergolide may promote weight loss and improve insulin regulation in horses with PPID, but does not impact muscle mass or markers of muscle proteolysis.
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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 | 0.001 | 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".