Correlations between human alveolar macrophage particulate matter load, air pollution particulate matter levels, and systemic inflammation markers in Mexico City
Bibliographic record
Abstract
This study explores potential associations among ambient particulate matter (PM) exposure, PM load in alveolar macrophage (AM), and biomarkers collected from 53 healthy, adult, nonsmoking residents of the Iztapalapa and Iztacalco municipalities in Mexico City. Ambient PM 2.5 concentrations were estimated using an improved Land Use Regression (LUR) model to approximate PM exposure levels. The PM/carbon loading was quantified by the fraction of AM containing PM (%, %AMPM) and the PM area within the AM (µm 2 ) from BAC cytospin microphotography using CellProfiler cell image analysis software. Concentrations of biomarkers were analyzed in bronchoalveolar lavage fluid (BALF), plasma, and urine. Most AM samples contained PM (median = 62.4%, interquartile range [IQR] = 50.0–73.0%). The median PM area in AM was 1.082 µm 2 (IQR = 0.607–1.855 µm 2 ). Participant with low %AMPM (< 33 percentile) showed 8% increase in %AMPM per 10 µg/m 3 increments of six-month averaged, LUR-estimated PM 2.5 concentrations. The %AMPM had a statistically significant, positive association with plasma von Willebrand Factor (vWF) ( p = 0.016) and serum lactase dehydrogenase (LDH) ( p = 0.026). These findings suggest that ambient urban PM exposure in Mexico City contributes to PM accumulation in AMs and may trigger systemic inflammation and oxidative stress in healthy young residents.
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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 | 0.000 |
| 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 | 0.001 | 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".