Analysis of proglycogen and macroglycogen content in muscle biopsy specimens obtained from horses
Bibliographic record
Abstract
OBJECTIVE: To determine proglycogen (PG) and macroglycogen (MG) content in equine skeletal muscle and to compare 2 analytical methods (acid hydrolysis [AC] and PG plus MG determination) for measurement of total muscle glycogen content (Gly(tot)) in biopsy specimens. SAMPLE POPULATION: Muscle biopsy specimens obtained from 41 clinically normal horses. PROCEDURE: Forty-five muscle biopsy specimens obtained from the middle gluteal (n = 31) or triceps (14) muscle were analyzed, using AC and MG plus PG determination for Gly(tot). Variability within muscle biopsy specimens for each method was calculated from duplicate analyses of muscle specimens. In a second experiment, variation in MG and PG content between muscle biopsy specimens and the effect of sample collection depth on the concentration of MG and PG in the middle gluteal muscle was evaluated. RESULTS: There was a strong correlation (r = 0.99) between Gly(tot) values obtained by use of AC and MG plus PG determination. Coefficients of variation for within- and between-specimen variability of Gly(tot) were approximately 4% for each method. The PG fraction was always in excess of the MG fraction. Biopsy specimens obtained from the superficial part of the middle gluteal muscle contained significantly more Gly(tot) and PG than specimens obtained from deeper parts. CONCLUSIONS AND CLINICAL RELEVANCE: This study confirms that MG and PG exist in equine skeletal muscle and can be measured reliably in biopsy samples. This technique could be applied in future studies to investigate glycogen metabolism in exercising horses and horses with glycogen-storage diseases.
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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 | 0.001 | 0.001 |
| 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".