Macrophages in Atherosclerosis, First or Second Row Players?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Macrophages represent a cell type that has been widely described in the context of atherosclerosis since the earliest studies in the 17th century. Their role has long been considered to be preponderant in the onset and aggravation of atherosclerosis, in particular by participating in the establishment of a chronic inflammatory state by the release of pro-inflammatory cytokines and by uncontrolled engorgement of lipids resulting in the formation of foam cells and later of the necrotic core. However, recent evidence from mouse models using an elegant technique of tracing vascular smooth muscle cells (VSMCs) during plaque development revealed that resident VSMCs display impressive plastic properties in response to an arterial injury, allowing them to switch into different cell types within the plaque, including mesenchymal-like cells, macrophage-like cells and osteochondrogenic-like cells. In this review, we oppose the arguments in favor or against the influence of macrophages versus VSMCs in all stages of atherosclerosis including pre-atherosclerosis, formation of lipid-rich foam cells, development of the necrotic core and the fibrous cap as well as calcification and rupture of the plaque. We also analyze the relevance of animal models for the investigation of the pathophysiological mechanisms of atherosclerosis in humans, and discuss potential therapeutic strategies targeting either VSMCs or macrophage to prevent the development of cardiovascular events. Overall, although major findings have been made from animal models, efforts are still needed to better understand and therefore prevent the development of atherosclerotic plaques in humans.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it