Acute responses in muscle mitochondrial and cytosolic enzyme activities during heavy intermittent exercise
Bibliographic record
Abstract
To examine the effects of repetitive bouts of heavy exercise on the maximal activities of enzymes representative of the major metabolic pathways and segments, 13 untrained volunteers [peak aerobic power (Vo(2 peak)) = 44.3 +/- 2.3 ml.kg(-1).min(-1)] cycled at approximately 91% Vo(2 peak) for 6 min once per hour for 16 h. Maximal enzyme activities (V(max), mol.kg(-1).protein.h(-1)) were measured in homogenates from tissue extracted from the vastus lateralis before and after exercise at repetitions 1 (R1), 2 (R2), 9 (R9), and 16 (R16). For the mitochondrial enzymes, exercise resulted in reductions (P < 0.05) in cytochrome-c oxidase (COX, 14.6%), near significant reductions in malate dehydrogenase (4.06%; P = 0.06) and succinic dehydrogenase (4.82%; P = 0.09), near significant increases in beta-hydroxyacyl-CoA dehydrogenase (4.94%; P = 0.08), and no change in citrate synthase (CS, 2.88%; P = 0.37). For the cytosolic enzymes, exercise reduced (P < 0.05) V(max) in hexokinase (Hex, 4.4%), creatine phosphokinase (9.0%), total phosphorylase (13.5%), phosphofructokinase (16.6%), pyruvate kinase (PK, 14.1%) and lactate dehydrogenase (10.7%). Repetition-dependent reductions (P < 0.05) in V(max) were observed for CS (R1, R2 > R16), COX (R1, R2 > R16), Hex (1R, 2R > R16), and PK (R9 > R16). It is concluded that heavy exercise results in transient reductions in a wide range of enzymes involved in different metabolic functions and that in the case of selected enzymes, multiple repetitions of the exercise reduce average V(max).
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".