Effects of exercise on brain function: role of free radicals
Bibliographic record
Abstract
Reactive oxygen species (ROS) are continuously generated during aerobic metabolism. Certain levels of ROS, which could be dependent on the type of cell, cell age, history of ROS exposure, etc., could facilitate specific cell functions. Indeed, ROS stimulate a number of stress responses and activate gene expression for a wide range of proteins. It is well known that increased levels of ROS are involved in the aging process and the pathogenesis of a number of neurodegenerative diseases. Because of the enhanced sensitivity of the central nervous system to ROS, it is especially important to maintain the normal redox state in different types of neuro cells. In the last decade it became clear that regular exercise beneficially affects brain function as well, and can play an important preventive and therapeutic role in stroke and in Alzheimer's and Parkinson's diseases. The effects of exercise appear to be very complex and could include neurogenesis via neurotrophic factors, increased capillarization, decreased oxidative damage, and increased proteolytic degradation by proteasome and neprilysin. Data from our and other laboratories indicate that exercise-induced modulation of ROS levels plays a role in the protein content and expression of brain-derived neurotrophic factor, tyrosine recepetor kinase B, and cAMP response element binding protein, resulting in better function and increased neurogenesis. The enhanced activities of proteasome and neprilysin result in decreased accumulation of carbonyls and amyloid beta-proteins, as well as improved memory. It appears that exercise-induced modulation of the redox state is an important means by which exercise benefits brain function, increases the resistance against oxidative stress, and facilitates recovery from oxidative stress.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".