Beyond the Foam Cell: The Role of LXRs in Preventing Atherogenesis
Bibliographic record
Abstract
Atherosclerosis is a chronic condition associated with cardiovascular disease. While largely identified by the accumulation of lipid-laden foam cells within the aorta later on in life, atherosclerosis develops over several stages and decades. During atherogenesis, various cell types of the aorta acquire a pro-inflammatory phenotype that initiates the cascade of signaling events facilitating the formation of these foam cells. The liver X receptors (LXRs) are nuclear receptors that upon activation induce the expression of transporters responsible for promoting cholesterol efflux. In addition to promoting cholesterol removal from the arterial wall, LXRs have potent anti-inflammatory actions via the transcriptional repression of key pro-inflammatory cytokines. These beneficial functions sparked an interest in the potential to target LXRs and the development of agonists as anti-atherogenic agents. These early studies focused on mediating the contributions of macrophages to the underlying pathogenesis. However, further evidence has since demonstrated that LXRs reduce atherosclerosis through their actions in multiple cell types apart from those monocytes/macrophages that infiltrate the lesion. LXRs and their target genes have profound effects on multiple other cells types of the hematopoietic system. Furthermore, LXRs can also mediate dysfunction within vascular cell types of the aorta including endothelial and smooth muscle cells. Taken together, these studies demonstrate the whole-body benefits of LXR activation with respect to anti-atherogenesis, and that LXRs remain a viable target for the treatment of atherosclerosis, with a reach which extends beyond plaque macrophages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".