Effect of 10-day forced treadmill training on neurotrophic factors in experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
The impact of exercise on disease progression in multiple sclerosis (MS) is unclear. In the present study, we evaluated the clinical effects of forced wheel running on rats induced with experimental autoimmune encephalomyelitis (EAE), a model of MS. Female Lewis rats (n = 40) were randomly assigned to 1 of 4 groups prior to inoculation: EAE exercise (EAE-Ex), EAE sedentary (EAE-Sed), control exercise (Con-Ex), or control sedentary (Con-Sed). Exercise training was composed of forced treadmill running at increasing intensity across 10 consecutive days. No significant differences in clinical disability were observed in the EAE groups at the conclusion of this study. Furthermore, no significant differences in brain mass were observed across groups. Analysis of brain tissue proteins revealed that tumour necrosis factor-α (TNF-α) concentrations were higher in both EAE groups compared with the control groups (p < 0.05); however, no significant differences were seen between the EAE-Ex and EAE-Sed groups. The Con-Ex group had lower whole-brain TNF-α compared with the Con-Sed group (p < 0.05). Nerve growth factor concentrations were greater in the EAE-Ex animals compared with both control groups (p < 0.05 for both). No differences were seen in brain-derived neurotrophic factor. Our results indicate that aerobic exercise can modulate the proteins associated with disability in EAE; however, further research is required to understand the total impact of exercise on EAE disability and disease progression.
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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) | 0.001 | 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) | 0.001 | 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".