Regulated Necrosis in Atherosclerosis
Bibliographic record
Abstract
During atherosclerosis, lipid-rich plaques are formed in large- and medium-sized arteries, which can reduce blood flow to tissues. This situation becomes particularly precarious when a plaque develops an unstable phenotype and becomes prone to rupture. Despite advances in identifying and treating vulnerable plaques, the mortality rate and disability caused by such lesions remains the number one health threat in developed countries. Vulnerable, unstable plaques are characterized by a large necrotic core, implying a prominent role for necrotic cell death in atherosclerosis and plaque destabilization. Necrosis can occur accidentally or can be induced by tightly regulated pathways. Over the past decades, different forms of regulated necrosis, including necroptosis, ferroptosis, pyroptosis, and secondary necrosis, have been identified, and these may play an important role during atherogenesis. In this review, we describe several forms of necrosis that may occur in atherosclerosis and how pharmacological modulation of these pathways can stabilize vulnerable plaques. Moreover, some challenges of targeting necrosis in atherosclerosis such as the presence of multiple death-inducing stimuli in plaques and extensive cross-talk between necrosis pathways are discussed. A better understanding of the role of (regulated) necrosis in atherosclerosis and the mechanisms contributing to plaque destabilization may open doors to novel pharmacological strategies and will enable clinicians to tackle the residual cardiovascular risk that remains in many atherosclerosis patients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".