In vitro and in vivo approaches to assess atherosclerosis following exposure to low-dose mixtures of arsenic and cadmium
Bibliographic record
Abstract
Worldwide, millions of people are co-exposed to arsenic and cadmium. Environmental exposure to both metals is linked with a higher risk of atherosclerosis. While studies have characterized the pro-atherosclerotic effects of arsenic and cadmium as single agents, little is known about the potential effects of metal mixtures, particularly at low doses. Here, we used a combination of in vitro and in vivo models to assess the effects of low-dose metals individually and as mixtures on early events and plaque development associated with atherosclerosis. In vitro, we investigated early pro-atherogenic changes in macrophages and endothelial cells with metal treatments. The combined cytotoxic effects of both metals at low concentrations were dose interactive, specifically, synergistic in macrophages, but antagonistic in endothelial cells. Despite this differential behavior across cell types, the mixtures did not initiate early pro-atherogenic events: neither reactive oxygen species generation in macrophages nor adhesion molecule expression on endothelial cells. In vivo, we utilized the well-characterized hyperlipidemic apolipoprotein E knock-out (ApoE−/−) mouse model. Previously, we have shown that low concentrations of arsenic (down to 10 ppb) enhance atherosclerosis in ApoE−/− mice. This model has also been used with cadmium to demonstrate pro-atherogenic effects, although at concentrations above human-relevant exposures. In both sexes, there are some small increases in atherosclerotic lesion size, but very few changes in plaque constituents in the ApoE−/− mouse model. Together, these results suggests that low-dose metal mixtures are not significantly more pro-atherogenic than either metal alone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".