Acute exercise increases hippocampal TNF-α, Caspase-3 and Caspase-7 expression in healthy young and older mice.
Bibliographic record
Abstract
AIM: Regular exercise may protect against cognitive decline by preventing central inflammation. The effect of an acute exercise bout on central cytokine and apoptotic protein expression is not known. The brain may be protected from transient oxidative stress such as that induced by acute-exercise. The purpose of this exploratory study was to determine the effect of a single bout of intense exercise on hippocampal expression of inflammatory mediators (TNF-α) and apoptotic proteins (caspase-3, caspase-7), and to evaluate any potential age-related differences. METHODS: Using a C57BL/6 mouse model (N.=98), we evaluated the effect of an acute exercise bout (90 minutes of treadmill running: 10 min warm-up, 30 min at 22 m.min⁻¹, 30 min at 25 m.min⁻¹, and 30 min at 28 m.min⁻¹, 2° slope) on hippocampal inflammation in young (3-4 months), middle-aged (13-14 months) and older (16-17 months) C57BL/6 mice. RESULTS: Our results show post-exercise increases in hippocampal TNF-α and caspase-3/7 in each age group (main effect of acute exercise, P<0.05). Older mice displayed higher TNF-α (main effect of age, P<0.05) expression compared with younger animals at baseline. Young mice demonstrated greater increases in caspase-7 following acute exercise, compared to older mice (interaction effect for caspase-7, P<0.05). CONCLUSION: Given the relationship between aging, inflammation and neurodegenerative disease, and the protective effects of exercise, we cautiously propose that acute-exercise induced inflammation may be a normal physiologic response that elicits a favorable (anti-inflammatory) hippocampal environment.
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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 | 0.000 | 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".