Microglial modulation as a mechanism behind the promotion of central nervous system well‐being by physical exercise
Bibliographic record
Abstract
Abstract The role of microglia within the central nervous system (CNS), and their contribution to processes critical for both normal function and the development of pathology have expanded significantly in recent years. Distinct microglial subpopulations are described that exert differential effects depending on region, environmental cues and activation state. This has led to the proposition of microglia as a novel therapeutic target in a variety of CNS disorders. Exercise has recently been shown to reduce the chronic activation and aberrant regulation of microglia that occurs during pathology, and to promote the adoption of neuroprotective phenotypes. This is thought to translate into decreased destruction of dopaminergic neurons in models of Parkinson's disease, the promotion of hippocampal neurogenesis, and the reduction of age‐induced neuroinflammation and microglial priming. The present review will detail the emerging evidence suggesting microglial modulation as a key mechanism through which exercise exerts beneficial effects on the CNS. Here, we will present the role of microglia within select CNS processes/disorders, and describe how physical exercise improves CNS well‐being by directly acting through microglia. Finally, we discuss considerations of implementation of exercise as a therapeutic intervention in neurological disorders.
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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 | 0.001 | 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".