Load-matched acute and chronic exercise induce changes in mitochondrial biogenesis and metabolic markers
Bibliographic record
Abstract
We investigated the effects of acute and chronic exercise, prescribed in different intensity zones, but with total load-matched on mitochondrial markers (cytochrome C oxidase subunit IV (COX-IV), mitochondrial transcription factor A (Tfam), and citrate synthase (CS) activity in skeletal muscles, heart, and liver), glycogen stores, aerobic capacity, and anaerobic index in swimming rats. For this, 2 experimental designs were performed (acute and chronic efforts). Load-matched exercises were prescribed below, above, and on the anaerobic threshold (AnT), determined by the lactate minimum test. In chronic programs, 2 training prescription strategies were assessed (monotonous and linear periodized model). Results show changes in glycogen stores but no modification in the COX-IV and Tfam contents after acute exercises. In the chronic protocols, COX-IV and Tfam proteins and CS adaptations were intensity- and tissue-dependent. Monotonous training promoted better adaptations than the periodized model. Training at 80% of the AnT improved both performance variables, emphasizing the anaerobic index, concomitant to CS and COX-IV improvement (soleus muscle). The aerobic capacity and CS activity (gastrocnemius) were increased after 120% AnT training. In conclusion, acute exercise protocol did not promote responses in mitochondrial target proteins. An intensity and tissue dependence were reported in the chronic protocols, highlighting training at 80 and 120% of the AnT. Novelty: Load-matched acute exercise did not enhance COX-IV and Tfam contents in skeletal muscles, heart, and liver. In chronic exercise, COX-IV, Tfam, and CS activity adaptations were intensity- and tissue-dependent. Monotonous training was more efficient than the periodized linear model in adaptations of target proteins and enzymatic activity.
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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 | 0.001 |
| 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".