Training Effects in Mice after Long-Term Voluntary Exercise
Bibliographic record
Abstract
BACKGROUND: Mice are an important animal model in exercise studies on the immune system, cancer, and aging. There is limited research about the training effects of long-term voluntary exercise in this species. PURPOSE: To describe the training effects in mice given long-term aerobic voluntary exercise. METHODS: Female C57BL/6 mice were randomly assigned to 1) individual cages with in-cage running wheels with 24-h access (WR; N = 31), or 2) individual cages without running wheels for 16 wk (NR; N = 20). Run-to-exhaustion (RTE) times, VO2peak, speed at VO2peak, and citrate synthase (CS), succinate dehydrogenase (SDH), and phosphofructokinase (PFK) activity in the soleus, plantaris, and red and white gastrocnemius were assessed. RESULTS: Final body weight and speed at VO2peak did not differ by training condition. WR mice had significantly longer RTE times (P < 0.001) and higher VO2peak (P < 0.05) compared with NR mice. Higher CS and SDH activities were found in WR compared with NR mice for soleus (P < 0.01), red gastrocnemius (P < 0.01), and plantaris (P < 0.01) muscles. PFK activity was higher in WR mice in white gastrocnemius compared with NR mice (P < 0.01). CONCLUSIONS: Voluntary running wheel activity for 16 wk in female C57BL/6 mice resulted in longer run times to exhaustion, higher VO2peak, and higher SDH and CS activities in oxidative muscles. These findings suggest that wheel running in female C57BL/6 mice: 1) produces a measurable aerobic training effect and 2) is an effective exercise modality for long-term training studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".