Contribution of the arterial cells to atherosclerosis and plaque formation
Bibliographic record
Abstract
Atherosclerosis is commonly known as an inflammatory disease that is characterized by lipid deposition in the arterial wall, causing gradual restriction or complete blockade of blood flow, which can cause complications such as myocardial infarction, stroke, or peripheral artery disease. Several factors contribute to initiation and progression of atherosclerotic plaque formation. The role of macrophages and leukocytes in atherosclerosis has been well explored. Here, we provide an overview of what has been reported on the role and impact of the arterial cells on plaque formation, and vice versa. The atherogenic environment can trigger transformation and dedifferentiation of the endothelial cells (ECs), smooth muscle cells, and fibroblasts (FBs) whereby they can either directly contribute to plaque formation or influence its composition. Recent studies have demonstrated the plasticity in the identity of the arterial cells, the formation of intermediate cell types that share the characteristics of multiple cell types, and have revealed novel roles and functions for these cells in atherosclerosis. The potential for all vascular cells to cross-transdifferentiate, and detection of cells with mosaic characteristics in the atherosclerotic plaques reveal that the plaque environment is a complex and dynamic environment that could regulate the disease progression independent from the circulating lipid levels. We will also provide an overview on the interplay between sex and atherosclerosis, which has remained an underexplored area.
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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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".