Early atherogenesis: new insights from new approaches
Bibliographic record
Abstract
PURPOSE OF REVIEW: To highlight recent conceptual and technological advances that have positioned the field to interrogate the cellular and molecular mechanisms contributing to the initiation of atherosclerosis, including intimal lipid accumulation, inflammation, and lesion growth. RECENT FINDINGS: Advances in the understanding of endothelial LDL transcytosis and rapid lipid uptake by intimal macrophages provide mechanistic insights into intimal LDL accumulation and the initiation of atherogenesis. Recent studies have used unbiased single-cell approaches, such as single-cell RNA sequencing and CyTOF, to characterize the cellular components of the normal intima and atherosclerotic lesions. In-vitro studies and high-resolution transcriptomic analysis of aortic intimal lipid-loaded versus lipid-poor myeloid populations in vivo suggest that lipid-loaded macrophages may not be the primary drivers of inflammation in atherosclerotic lesions. SUMMARY: A new perspective on the complex cellular landscape of the aorta, specifically the atherosclerosis-prone regions, confirm that intimal accumulation of lipid, monocyte recruitment, and macrophage accumulation are key events in atherogenesis triggered by hypercholesterolemia. Targeting these early events may prove to be a promising strategy for the attenuation of lesion development; however, the specific details of how hypercholesterolemia acts to initiate early inflammatory events remain to be fully elucidated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".