Metals, Nanoparticles, Particulate Matter, and Cognitive Decline
Bibliographic record
Abstract
Exposure to metals is ubiquitous and emission sources include gasoline, diesel, smoke from wildfires, contaminated soil, water and food, medical implants, waste recycling facilities, subway exposures, and occupational environments. PM2.5 exposure is associated with impaired cognitive performance, neurobehavioral alterations, incidence of dementia, and Alzheimer's disease (AD) risk. Heavy-duty diesel vehicles are major emitters of metal-rich PM2.5 and nanoparticles in Metropolitan Mexico City (MMC). Cognitive impairment was investigated in 336 clinically healthy, middle-class, Mexican volunteers, age 29.2 ± 13.3 years with 13.7 ± 2.4 years of education using the Montreal Cognitive Assessment (MoCA). MoCA scores varied with age and residency in three Mexican cities with cognition deficits impacting ~74% of the young middle-class population (MoCA ≤ 25). MMC residents ≥31 years ( x¯ 46.2 ± 11.8 y) had MoCA x¯ 20.4 ± 3.4 vs. low pollution controls 25.2 ± 2.4 (p < 0.0001). Formal education years positively impacted MoCA total scores across all participants (p < 0.0001). Residency in PM2.5 polluted cities impacts multi-domain cognitive performance. Identifying and making every effort to lower key pollutants impacting neural risk trajectories and monitoring cognitive longitudinal performance are urgent. PM2.5 emission control should be prioritized, metal emissions targeted, and neuroprevention interventions implemented early.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".