White Matter Hyperintensities, Systemic Inflammation, Brain Growth, and Cognitive Functions in Children Exposed to Air Pollution
Bibliographic record
Abstract
Air pollution exposures are linked to neuroinflammation and neuropathology in young urbanites. Forty percent of exposed children and young adults exhibit frontal tau hyperphosphorylation and 51% have amyloid-β diffuse plaques compared to 0% in low pollution controls. In older adults, white matter hyperintensities (WMH) are associated with cognitive deficits while inflammatory markers correlate with greater atrophy than expected for age. We investigated patterns of WMH, magnetic resonance imaging (MRI) volume growth, blood inflammatory mediators, and cognition in matched children from two urban cohorts: one severely and one minimally exposed to air pollution. Baseline and one year follow-up measurements of cognitive abilities, brain MRI volumes, and blood were collected in 20 Mexico City (MC) children (10 with WMH+, and 10 without WMH-) and 10 matched controls (WMH-). MC WMH- children display the profile of classical pro-inflammatory defensive responses: high interleukin 12, production of powerful pro-inflammatory cytokines, and low concentrations of key cytokines and chemokines associated with neuroprotection. MC WMH+ children exhibit a response involved in resolution of inflammation, immunoregulation, and tissue remodeling. The MC WMH+ group responded to the air pollution-associated brain volumetric alterations with white and grey matter volume increases in temporal, parietal, and frontal regions and better cognitive performance compared to MC WMH-. We conclude that complex modulation of cytokines and chemokines influences children's central nervous system structural and volumetric responses and cognitive correlates resulting from environmental pollution exposures. Identification of biomarkers associating systemic inflammation to brain growth is critical for detecting children at higher risk for cognitive deficits and neurodegeneration, thereby warranting early implementation of neuroprotective measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".