Pro- and macroglycogenolysis during repeated exercise: roles of glycogen content and phosphorylase activation
Bibliographic record
Abstract
This study examined the relationship between preexercise muscle glycogen content and glycogen utilization in two physiological pools, pro- (PG) and macroglycogen (MG). Male subjects (n = 6) completed an exercise and dietary protocol before the experiment that resulted in one leg with high glycogen (HL) and one with low glycogen (LL). Preexercise PG levels were 312 +/- 29 and 208 +/- 31 glucosyl units/kg dry wt (dw) (P < or = 0.05) in the HL and LL, respectively, and the corresponding values for MG were 125 +/- 37 and 89 +/- 43 mmol glucosyl units/kg dw (P < or = 0.05). Subjects then performed two 90-s exercise bouts at 130% maximal oxygen uptake separated by a 10-min rest period. Biopsies were obtained at rest and after each exercise bout. Preexercise glycogen concentration was correlated to net glycogenolysis for both PG and MG for bout 1 and bouts 1 and 2 (r < or = 0.60). In bout 1, there was no difference in the rate of PG or MG catabolism between HL and LL despite a 26% increase (P < or = 0.05) in glycogen phosphorylase transformation (phos a %) in the HL. In the second bout, more PG was catabolized in the HL vs. LL (38 +/- 9 vs. 9 +/- 6 mmol glucosyl units. kg dw(-1). min(-1)) (P < or = 0.05) with no difference between legs in phos a %. phos a % was increased in HL vs. LL but does not necessarily increase glycogenolysis in either PG or MG. Despite both legs performing the same exercise and having identical metabolic demands, the HL catabolized 2.3 (P < or = 0.05) times more PG and 1.5 (P < or = 0.05) times more MG vs. LL in bouts 1 and 2, indicating that preexercise glycogen concentration is a regulator of glycogenolysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".