Relative Contributions of PM <sub>2.5</sub> Chemical Constituents to Acute Arterial Vasoconstriction in Humans
Bibliographic record
Abstract
Studies have shown associations between acute ambient particulate matter (PM) levels and increases in morbidity and mortality from cardiovascular diseases. We have previously reported in 24 healthy adults that exposure to concentrated ambient particles plus ozone (CAP + O(3)) caused a mean decrease of 0.09 mm in brachial artery diameter (BAD), which was significantly larger than a mean increase of 0.01 mm among the same individuals exposed to filtered air (FA). Our current objective is to examine the relationship between total and constituent PM(2.5) mass concentrations and the acute vascular response. We have analyzed both ambient and exposure filters from the brachial artery study for major chemical constituents, allowing us to compare the strength of the associations between each constituent and an individual's arterial response. We determined gravimetric PM(2.5) mass concentration and inorganic ion content from exposure filters. Twenty-three-hour ambient PM(2.5) filters collected from the same site and on the same day were used to estimate exposure concentrations of trace elements and organic and elemental carbon. We performed linear regression analyses on the levels of measured or estimated PM constituents using each subject's FA exposure as a control. We found, from our regression analyses, a significant negative association between both the organic and elemental carbon concentrations and the difference in the postexposure change in the BAD (Delta BAD) between and CAP + O(3) and FA exposure days. An understanding of the PM constituents most responsible for adverse health outcomes is critical for efforts to develop pollution abatement strategies that maximize benefits to public health.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".