The Effect Of Swimming Aerobic Training On The Levels Of IL-6 And IL-10 In The Brain Tissue Of Female Mice With Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
The effect of swimming aerobic training on the levels of IL-6 and IL-10 in the brain tissue of female mice with experimental autoimmune encephalomyelitis.Research In Sport Medicine and Technology, 2024: 14(27): 55-74.Fatigue is one of the most common symptoms among individuals with multiple sclerosis (MS).The imbalance of inflammatory cytokines in the brain causes mental fatigue.It is believed that aerobic exercise may moderate the level of inflammation of cytokines and thus reduce mental fatigue.This study was conducted to investigate the effect of swimming aerobic training on proinflammatory factors (interleukin 6) and anti-inflammatory factors (interleukin 10) to evaluate mental fatigue in the brains of female mice after EAE induction.In this research, 21 mice (8 weeks old, 18 to 20 grams) were divided into three groups: healthy control, EAE control, and EAE swimming aerobic exercise.The animals in the training group performed moderate-intensity swimming exercises for 4 weeks, with 5 sessions per week, each session lasting 30 minutes.The western blot method was used to analyze the research variables.The results showed no significant difference in the amount of IL-6 between the EAE group and the swimming training group, but the amount of IL-10 in the swimming training group showed a significant increase compared to the EAE group.The ratio of IL-10/IL-6 values between the swimming training group and the EAE group was also not significant.It seems that moderate-intensity swimming aerobic exercises, without changing the pro-inflammatory factor, along with increasing the anti-inflammatory factor IL-10, can lead to an improvement in the balance of the inflammatory index and a reduction in fatigue in the EAE group and the swimming exercise group.
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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) | 0.000 | 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".