Genetic Deletion of LXRα Prevents Arsenic-Enhanced Atherosclerosis, But Not Arsenic-Altered Plaque Composition
Bibliographic record
Abstract
Arsenic exposure has been linked to an increased incidence of atherosclerosis. Previously, we have shown in vitro and in vivo that arsenic inhibits transcriptional activation of the liver X receptors (LXRs), key regulators of lipid homeostasis. Therefore, we evaluated the role of LXRα in arsenic-induced atherosclerosis using the apoE(-/-) mouse model. Indeed, deletion of LXRα protected apoE(-/-) mice against the proatherogenic effects of arsenic. We have previously shown that arsenic changes the plaque composition in apoE(-/-) mice. Arsenic decreased collagen content in the apoE(-/-) model, and we have observed the same diminution in LXRα(-/-)apoE(-/-) mice. However, the collagen-producing smooth muscle cells (SMCs) were decreased in apoE(-/-), but increased in LXRα(-/-)apoE(-/-). Although transcriptional activation of collagen remained the same in SMC from both genotypes, arsenic-exposed LXRα(-/-)apoE(-/-) plaques had increased matrix metalloproteinase activity compared with both control LXRα(-/-)apoE(-/-) and apoE(-/-), which could be responsible for both the decrease in plaque collagen and the SMC invasion. In addition, arsenic increased plaque lipid accumulation in both genotypes. However, macrophages, the cells known to retain lipid within the plaque, were unchanged in arsenic-exposed apoE(-/-) mice, but decreased in LXRα(-/-)apoE(-/-). We confirmed in vitro that these cells retained more lipid following arsenic exposure and are more sensitive to apoptosis than apoE(-/-). Mice lacking LXRα are resistant to arsenic-enhanced atherosclerosis, but arsenic-exposed LXRα(-/-)apoE(-/-) mice still present a different plaque composition pattern than the arsenic-exposed apoE(-/-) mice.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".