Neuroprotective effects of physical activity on the brain: a closer look at trophic factor signaling
Bibliographic record
Abstract
While the relationship between increased physical activity and cognitive ability has been conjectured for centuries, only recently have the mechanisms underlying this relationship began to emerge. Convergent evidence suggests that physical activity offers an affordable and effective method to improve cognitive function in all ages, particularly the elderly who are most vulnerable to neurodegenerative disorders. In addition to improving cardiac and immune function, physical activity alters trophic factor signaling and, in turn, neuronal function and structure in areas critical for cognition. Sustained exercise plays a role in modulating anti-inflammatory effects and may play a role in preserving cognitive function in aging and neuropathological conditions. Moreover, recent evidence suggests that myokines released by exercising muscles affect the expression of brain-derived neurotrophic factor synthesis in the dentate gyrus of the hippocampus, a finding that could lead to the identification of new and therapeutically important mediating factors. Given the growing number of individuals with cognitive impairments worldwide, a better understanding of how these factors contribute to cognition is imperative, and constitutes an important first step toward developing non-pharmacological therapeutic strategies to improve cognition in vulnerable populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".