Effectiveness of exercise interventions in animal models of multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is associated with an impaired immune system that severely affects the spinal cord and brain, and which is marked by progressive inflammatory demyelination. Patients with MS may benefit from exercise training as a suggested course of treatment. The most commonly used animal models of studies on MS are experimental autoimmune/allergic encephalomyelitis (EAE) models. The present review intends to concisely discuss the interventions using EAE models to understand the effectiveness of exercise as treatment for MS patients and thereby provide clear perspective for future research and MS management. For the present literature review, relevant published articles on EAE animal models that reported the impacts of exercise on MS, were extracted from various databases. Existing literature support the concept that an exercise regimen can reduce the severity of some of the clinical manifestations of EAE, including neurological signs, motor function, pain, and cognitive deficits. Further results demonstrate the mechanisms of EAE suppression with information relating to the immune system, demyelination, regeneration, and exercise in EAE. The role for neurotrophic factors has also been investigated. Analyzing the existing reports, this literature review infers that EAE is a suitable animal model that can help researchers develop further understanding and treatments for MS. Besides, findings from previous animal studies supports the contention that exercise assists in ameliorating MS 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".