The Effect of Concurrent Endurance and Strength Training on Quantitative Estimates of Subsarcolemmal and Intermyofibrillar Mitochondria
Bibliographic record
Abstract
We examined the effect of combined strength and endurance training on quantitative estimates of mitochondria in subsarcolemmal and intermyofibrillar regions of muscle fibers. Ten subjects (five males, five females) participated in a 12 week program of combined strength and endurance training. Seven subjects (three males and four females) served as controls. Biopsy samples from the vastus lateralis were obtained before and after training in both groups and also at the mid-point of training in the exercise group. Measurement of succinate dehydrogenase activity throughout muscle fibers, as a quantitative estimate of mitochondrial subpopulations, revealed no differences between exercise and control groups before and after training. Within the exercise group, there was a significant increase in succinate dehydrogenase activity in all regions of muscle fibers from before to after training. There was also a significant increase in succinate dehydrogenase activity in the subsarcolemmal, relative to the intermyofibrillar region from mid-(six weeks) to after-training ( regional distribution x time; p < 0.05). This may have been associated with an oxidative shift in fiber types, as type I fiber percentage was increased in the exercise, compared to the control group (group x time; p < 0.05). We conclude that mitochondrial populations undergo differential changes throughout training. IMF mitochondria increase in a linear manner throughout training, while SS mitochondria undergo a preferential increase late in training. This increase late in training may be related to an increase in proportion of type I fibers.
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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".