Using the Apolipoprotein E Knock-Out Mouse Model to Define Atherosclerotic Plaque Changes Induced by Low Dose Arsenic
Bibliographic record
Abstract
Arsenic exposure increases the risk of atherosclerosis, the gradual occlusion of the large arteries with fibro-fatty plaque. While epidemiologic data provide convincing evidence this is true at higher exposures, it is unclear whether this may occur at low arsenic exposures, near the maximum contaminant level of 10 ppb. We have previously shown that 200 ppb arsenite in the drinking water increased the atherosclerosis in apolipoprotein E knock-out (apoE-/-) mice after 13 weeks, but the effects of lower concentrations were unknown. Therefore, here, we analyzed the effects of oral exposure to arsenite from 10 to 200 ppb after 13 weeks. Importantly, we found that even at the lowest concentration of arsenite, there was a significant increase in atherosclerotic plaque size. In our previous studies, we found that arsenite exposure resulted in decreased smooth muscle cells (SMCs) and collagen within the plaque. This change is indicative of a less stable phenotype that could increase the risk of rupture and subsequently, myocardial infarct or stroke in humans. In addition, we observed that lipid increased within the plaque without concomitant increase in macrophage content, suggesting that the macrophages were retaining more lipid intracellularly. We also assessed these plaque components in apoE-/- mice exposed to 10-200 ppb arsenite. Interestingly, we observed that macrophage lipid accumulation occurred at lower concentrations than the decreased SMC/collagen content. Together these data suggest that in the apoE-/- model, low arsenite concentrations are pro-atherogenic and that macrophage lipid homeostasis is more sensitive to arsenite-induced perturbation than the SMCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".