Proglycogen, macroglycogen, glucose, and glucose-6-phosphate concentrations in skeletal muscles of horses with polysaccharide storage myopathy performing light exercise
Bibliographic record
Abstract
OBJECTIVE: To determine concentrations of proglycogen (PG), macroglycogen (MG), glucose, and glucose-6-phosphate (G-6-P) in skeletal muscle of horses with polysaccharide storage myopathy (PSSM) before and after performing light submaximal exercise. ANIMALS: 6 horses with PSSM and 4 control horses. PROCEDURES: Horses with PSSM completed repeated intervals of 2 minutes of walking followed by 2 minutes of trotting on a treadmill until muscle cramping developed. Four untrained control horses performed a similar exercise test for up to 20 minutes. Serum creatine kinase (CK) activity was measured before and 4 hours after exercise. Concentrations of total glycogen (G(t)), PG, MG, G-6-P, free glucose, and lactate were measured in biopsy specimens of gluteal muscle obtained before and after exercise. RESULTS: Mean serum CK activity was 26 times higher in PSSM horses than in control horses after exercise. Before exercise, muscle glycogen concentrations were 1.5, 2.2, and 1.7 times higher for PG, MG, and G(t), respectively, in PSSM horses, compared with concentrations in control horses. No significant changes in G(t), PG, MG, G-6-P, and lactate concentrations were detected after exercise. However, free glucose concentrations in skeletal muscle increased significantly in PSSM horses after exercise. CONCLUSIONS AND CLINICAL RELEVANCE: Analysis of the results suggests that glucose uptake in skeletal muscle is augmented in horses with PSSM after light exercise. There is excessive storage of PG and MG in horses with PSSM, and high concentrations of the 2 glycogen fractions may affect functional interactions between glycogenolytic and glycogen synthetic enzymes and glycosomes.
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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 | 0.001 |
| 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".