Mild Cognitive Impairment and Dementia Involving Multiple Cognitive Domains in Mexican Urbanites
Bibliographic record
Abstract
Exposures to fine particulate matter PM2.5 and ozone O3 are associated with Alzheimer’s disease (AD) risk. Mexico City residents have lifetime exposures to PM2.5 and O3 above annual USEPA standards and their brains contain high redox, combustion, and friction-derived magnetite nanoparticles. AD pathological changes with subcortical pre-tangle stages in infancy and cortical tau pre-tangles, NFT Stages I-II, and amyloid phases 1-2 are identified by the 2nd decade. Given their AD continuum, a reliable identification of cognitive impairment is of utmost importance. The Montreal Cognitive Assessment (MoCA) was administered to 517 urbanites, age 21.60±5.88 years, with 13.69±1.28 formal education years, in Mexican PM2.5 polluted cities. MoCA score was 23.92±2.82, and 24.7% and 30.3% scored ≤24 and ≤22, respectively (MCI≤24, AD≤22). Cognitive deficits progressively targeted Visuospatial, Executive, Language, and Memory domains, body mass index (BMI) impacting total scores negatively (p = 0.0008), aging driving down Executive, Visuospatial, and Language index scores (p < 0.0001, 0.0037, and 0.0045), and males performing better in Executive tasks. Average age for AD MoCA scores was 22.38±7.7 years. Residency in polluted cities is associated with progression of multi-domain cognitive impairment affecting 55% of Mexican seemingly healthy youth. Normal BMI ought to be a neuroprotection goal. MoCA provides guidance for further mandatory neuropsychological testing in young populations. Identifying and lowering key neurotoxicants impacting neural risk trajectories in the developing brain and monitoring cognitive performance would greatly facilitate multidisciplinary early diagnosis and prevention of AD in high risk young populations. Cognitive deficits hinder development of those representing the force moving the country in future years.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".