Thrombotic Markers in Metabolic Syndrome Subjects Exposed to Diesel Exhaust
Bibliographic record
Abstract
Traffic-derived particulate matter (PM) is associated with cardiovascular morbidity and mortality, but the mechanism of this association is unclear. Prothrombotic processes have been linked to PM in epidemiological and animal models, but have not been consistently implicated in controlled human models. Diesel exhaust (DE) is a major contributor to PM. We conducted a controlled human exposure of DE in subjects with metabolic syndrome. The study objective was to evaluate DE exposure effects on prothrombotic markers in a population vulnerable to cardiovascular disease. A randomized, crossover, double-blinded design was used: 16 subjects with metabolic syndrome exposed on 3 different days (> or = 2 wk washout) to DE at 0 (filtered air, FA), 100 microg PM(2.5)/m(3) (DE(100)) and 200 mug PM(2.5)/m(3) (DE(200)). We assessed DE-associated changes in D-dimer, von Willebrand factor (VWF), and plasmin activator inhibitor-1 (PAI-1) at 3, 7, and 22 h after exposure initiation. A DE(200)-attributable decrease (1.17-fold; CI 1.04 to 1.34) in VWF was noted at 7 h. Significant changes did not occur in other primary endpoints. As previously noted with healthy subjects, strong diurnal patterns in PAI-1 were observed. Thus, in a novel study, we were unable to demonstrate a prothrombotic effect of moderate-dose diesel exhaust exposure in a population at risk for cardiovascular disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".