Neuroinflammation and Neurodegeneration of the Central Nervous System from Air Pollutants: A Scoping Review
Bibliographic record
Abstract
In this scoping review, we provide a selective mapping of the global literature on the effects of air pollution on the life-span development of the central nervous system. Our synthesis first defines developmental neurotoxicants and the model effects of particulate matter. We then discuss air pollution as a test bench for neurotoxicants, including animal models, the framework of systemic inflammation in all affected organs of the body, and the cascade effects on the developing brain, with the most prevalent neurological structural and functional outcomes. Specifically, we focus on evidence on magnetic resonance imaging and neurodegenerative diseases, and the links between neuronal apoptosis and inflammation. There is evidence of a developmental continuity of outcomes and effects that can be observed from utero to aging due to severe or significant exposure to neurotoxicants. These substances alter the normal trajectory of neurological aging in a propulsive way towards a significantly higher rate of acceleration than what is expected if our atmosphere were less polluted. The major aggravating role of this neurodegenerative process is linked with the complex action of neuroinflammation. However, most recent evidence learned from research on the effects of COVID-19 lockdowns around the world suggests that a short-term drastic improvement in the air we breathe is still possible. Moreover, the study of mitohormesis and vitagenes is an emerging area of research interest in anti-inflammatory and antidegenerative therapeutics, which may have enormous promise in combatting the deleterious effects of air pollution through pharmacological and dietary interventions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".