Sex and diet as modulators of arsenic-induced atherosclerosis in apolipoprotein E knockout mice
Bibliographic record
Abstract
Arsenic is identified as one of the most important chemical contaminants worldwide by the World Health Organization (WHO).Yet, millions of people are still exposed on a daily basis to arsenic through contaminated air, soil, and groundwater due to both natural and anthropogenic sources.Even though arsenic concentrations are heavily regulated in the workplace, in agriculture, and in the municipal drinking water, arsenic exposure remains a significant threat to human health.Arsenic exposure causes a myriad of adverse health effects, including neurological and skin disorders, various cancers, and cardiovascular diseases, such as atherosclerosis.Atherosclerosis is both a cardiovascular disease and an immune disease, mostly associated with the narrowing and hardening of arteries due to fibro-fatty build up involving various immune cells, collagen, and oxidized cholesterol.Due to the high cholesterol content, a high fat diet is a known contributor to atherosclerosis, however, there are sexual dimorphisms between how males and females differ in atherosclerosis development.Previously, our lab has shown that arsenic increases the plaque size in the aortic sinus in male apolipoprotein E knockout (apoE -/-) mice in the absence of high fat diet, while altering various plaque constituents independently from diet.While macrophage levels stayed consistent, lipid levels increased, whereas levels of collagen and smooth muscle cells decreased.However, arsenic-induced atherosclerotic plaques have never been characterized in females.Thus, in this study, we investigated the role of sex and diet in arsenic-induced atherosclerosis in apoE -/-female mice both in the presence and absence of high fat diet by:
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".