Glycogenin activity and mRNA expression in response to volitional exhaustion in human skeletal muscle
Bibliographic record
Abstract
Glycogenolysis results in the selective catabolism of individual glycogen granules by glycogen phosphorylase. However, once the carbohydrate portion of the granule is metabolized, the fate of glycogenin, the protein primer of granule formation, is not known. To examine this, male subjects (n = 6) exercised to volitional exhaustion (Exh) on a cycle ergometer at 75% maximal O2 uptake. Muscle biopsies were obtained at rest, 30 min, and Exh (99 +/- 10 min). At rest, total glycogen concentration was 497 +/- 41 and declined to 378 +/- 51 mmol glucosyl units/kg dry wt following 30 min of exercise (P < 0.05). There were no significant changes in proglycogen, macroglycogen, glycogenin activity, or mRNA in this period (P > or = 0.05). Exh resulted in decreases in total glycogen, proglycogen, and macroglycogen as well as glycogenin activity (P < 0.05). These decrements were associated with a 1.9 +/- 0.4-fold increase in glycogenin mRNA over resting values (P < 0.05). Glycogenolysis in the initial exercise period (0-30 min) was not adequate to induce changes in glycogenin; however, later in exercise when concentration and granule number decreased further, decrements in glycogenin activity and increases in glycogenin mRNA were demonstrated. Results show that glycogenin becomes inactivated with glycogen catabolism and that this event coincides with an increase in glycogenin gene expression as exercise and glycogenolysis progress.
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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".