An ex vivo model of systemically-mediated effects of ozone inhalation on the brain
Bibliographic record
Abstract
Air pollution is associated with increased risk of neurodegenerative and neuropsychiatric conditions. While animal models have increased our understanding of how air pollution contributes to brain pathologies - including through oxidative stress, inflammatory, and stress hormone pathways - investigation of underlying mechanisms remains limited due to a lack of human-relevant models that incorporate systemic processes. Our objective was to establish an ex vivo approach that enables assessment of the roles of plasma mediators in pollutant-induced effects in the brain. As a proof-of-concept for application in the human context, we assessed whether such effects reproduced in vivo responses to pollutant exposure. Primary rat hippocampal neurons and microglia were each treated with plasma collected from rats immediately or 24 h after ozone inhalation (0 or 0.8 ppm) ± pre-treatment with the glucocorticoid synthesis inhibitor metyrapone. Microglia were further challenged with lipopolysaccharide to evaluate modification of inflammatory responses. Plasma from the ozone-exposed group produced transcriptional changes (inflammatory, antioxidant, glucocorticoid-responsive) in neurons, some of which were glucocorticoid-dependent. Ex vivo and hippocampal responses were strongly correlated, establishing the in vivo relevance of the model. Plasma from the ozone-exposed group modified inflammatory responses to lipopolysaccharide challenge in microglia, demonstrating the model's utility to assess functional changes resulting from pollutant exposure. This study establishes that an ex vivo approach can reproduce ozone-induced effects in the brain. The model was sensitive to specific plasma mediators and temporal effects, and enabled assessment of functional responses. This approach may serve to investigate mechanisms underlying effects of pollutants on the human brain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".