Pro- and macroglycogenolysis: relationship with exercise intensity and duration
Bibliographic record
Abstract
We examined the net catabolism of two pools of glycogen, proglycogen (PG) and macroglycogen (MG), in human skeletal muscle during exercise. Male subjects (n = 21) were assigned to one of three groups. Group 1 exercised 45 min at 70% maximal O(2) uptake (VO(2 max)) and had muscle biopsies at rest, 15 min, and 45 min. Group 2 exercised at 85% VO(2 max) to exhaustion (45.4 +/- 3.4 min) and had biopsies at rest, 10 min, and exhaustion. Group 3 performed three 3-min bouts of exercise at 100% VO(2 max) separated by 6 min of rest. Biopsies were taken at rest and after each bout. Group 1 had small MG and PG net glycogenolysis rates (ranging from 3.8 +/- 1.0 to 2.4 +/- 0.6 mmol glucosyl units. kg(-1). min(-1)) that did not change over time. In group 2, the MG glycogenolysis rate remained low and unchanged over time, whereas the PG rate was initially elevated (11.3 +/- 2.3 mmol glucosyl units. kg(-1). min(-1)) and declined (P < or = 0.05) with time. During the first 10 min, PG concentration ([PG]) declined (P < or = 0.05), whereas MG concentration ([MG]) did not. Similarly, in group 3, in both the first and the second bouts of exercise [PG] declined (P < or = 0.05) and [MG] did not, although by the end of the second exercise period the [MG] was lower (P < or = 0.05) than the rest level. The net catabolic rates for PG in the first two exercises were 22.6 +/- 6.8 and 21.8 +/- 8.2 mmol glucosyl units. kg(-1). min(-1), whereas the corresponding values for MG were 17.6 +/- 6.0 and 10.8 +/- 5.6. The MG pool appeared to be more resistant to mobilization, and, when activated, its catabolism was inhibited more rapidly than that of PG. This suggests that the metabolic regulation of the two pools must be different.
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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 | 0.001 |
| 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".