New insight into dyslipidemia‐induced cellular senescence in atherosclerosis
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
Atherosclerosis, characterized by lipid-rich plaques in the arterial wall, is an age-related disorder and a leading cause of mortality worldwide. However, the specific mechanisms remain complex. Recently, emerging evidence has demonstrated that senescence of various types of cells, such as endothelial cells (ECs), vascular smooth muscle cells (VSMCs), macrophages, endothelial progenitor cells (EPCs), and adipose-derived mesenchymal stem cells (AMSCs) contributes to atherosclerosis. Cellular senescence and atherosclerosis share various causative stimuli, in which dyslipidemia has attracted much attention. Dyslipidemia, mainly referred to elevated plasma levels of atherogenic lipids or lipoproteins, or functional impairment of anti-atherogenic lipids or lipoproteins, plays a pivotal role both in cellular senescence and atherosclerosis. In this review, we summarize the current evidence for dyslipidemia-induced cellular senescence during atherosclerosis, with a focus on low-density lipoprotein (LDL) and its modifications, hydrolysate of triglyceride-rich lipoproteins (TRLs), and high-density lipoprotein (HDL), respectively. Furthermore, we describe the underlying mechanisms linking dyslipidemia-induced cellular senescence and atherosclerosis. Finally, we discuss the senescence-related therapeutic strategies for atherosclerosis, with special attention given to the anti-atherosclerotic effects of promising geroprotectors as well as anti-senescence effects of current lipid-lowering drugs.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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