High intensity exercise preconditioning influences on steroid hormones following the experimental autoimmune encephalomyelitis model
Bibliographic record
Abstract
Abstract Steroid hormones improve clinical and pathological symptoms using the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS). In addition, exercise seems to play an important role in increasing hormones such as 17beta-estradiol and estrogen receptor beta (ERβ). In the present study, we evaluated whether 6 weeks of high-intensity interval training (HIIT) prior to induction of EAE increased 17beta-estradiol and ERβ and attenuate the severity of symptoms and/or disease progression in the EAE model. Female C57BL/6 mice were randomly divided into exercise (EX) and control (Con) groups. After 4 weeks of training, EAE was induced in half of the Con and the EX groups. The EAE-EX group after EAE induction trained for two more weeks. The EX group trained for 6 weeks. Six weeks of HIIT increased 17beta-estradiol and ERβ in the EX group compared to the control group (P ≤ 0.05). The EAE-EX group had a significant increase in 17beta-estradiol and ERβ and a significant decrease in clinical symptoms compared to the EAE group (P ≤ 0.05). In addition, the EAE group had a significant decrease in ERβ (P ≤ 0.05) compared to the control group. Our data demonstrate that 6 week of HIIT increased 17beta-estradiol and ERβ in the cerebellum tissue. These hormones are associated with decrease clinical outcomes and further research is required to examine potential clinical relevance.
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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) | 0.000 | 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) | 0.001 | 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".