Effects of high‐intensity endurance exercise training in the G93A mouse model of amyotrophic lateral sclerosis
Bibliographic record
Abstract
The G93A transgenic mouse has a mutation in copper/zinc superoxide dismutase (CuZnSOD) that results in oxidative stress and motor neuron loss. Endurance exercise training is known to increase antioxidant capacity in skeletal muscle. Therefore, we hypothesized that endurance training may extend onset of disease or survival in the G93A mouse. We examined the effects of high-intensity endurance exercise training (45 min/day, 5 times/week, progressive increase from 9 to 22 m/min) on disease onset and survival in G93A mice. Endurance training did not affect clinical onset, although it hastened death in male mice (P < 0.05). Endurance-trained males had a statistically significant decrease in rotarod performance at 112 days (P < 0.05), whereas sedentary males decreased at 119 days (P < 0.05). Endurance-trained and sedentary females decreased at 126 days and 129 days, respectively (P < 0.05). Female mice lived longer than males (P < 0.05), and there was a trend for hastened clinical onset in males (P = 0.062). We conclude that high-intensity endurance exercise training does not affect onset of clinical symptoms in G93A mice but hastens a decrease in motor performance and death following onset of clinical symptoms in male mice only. In light of a recent report describing increased survival following low-intensity endurance training, it appears that training intensity is an important determinant of survival in the G93A mouse.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".