The Role of Metal Components in the Cardiovascular Effects of PM2.5
Bibliographic record
Abstract
Exposure to ambient fine particulate matter (PM2.5) increases risks for cardiovascular disorders (CVD). However, themechanisms and components responsible for the effects are poorly understood. Based on our previous murineexposure studies, a translational pilot study was conducted in female residents of Jinchang and Zhangye, China, totest the hypothesis that specific chemical component of PM2.5 is responsible for PM2.5 associated CVD. Daily ambientand personal exposures to PM2.5 and 35 elements were measured in the two cities. A total of 60 healthy nonsmokingadult women residents were recruited for measurements of inflammation biomarkers. In addition, circulatingendothelial progenitor cells (CEPCs) were also measured in 20 subjects. The ambient levels of PM2.5 werecomparable between Jinchang and Zhangye (47.4 and 54.5μg/m3, respectively). However, the levels of nickel,copper, arsenic, and selenium in Jinchang were 82, 26, 12, and 6 fold higher than Zhangye, respectively. The levelsof C-reactive protein (3.44±3.46 vs. 1.55±1.13), interleukin-6 (1.65±1.17 vs. 1.09±0.60), and vascular endothelialgrowth factor (117.6±217.0 vs. 22.7±21.3) were significantly higher in Jinchang. Furthermore, all phenotypes ofCEPCs were significantly lower in subjects recruited from Jinchang than those from Zhangye. These results suggestthat specific metals may be important components responsible for PM2.5-induced cardiovascular effects and that thereduced capacity of endothelial repair may play a critical role.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".