Mechanism of Hypercholesterolemia-Induced Atherosclerosis
Bibliographic record
Abstract
Hypercholesterolemia is involved in the development of atherosclerosis and is a risk factor for coronary artery disease, stroke, and peripheral vascular disease. This paper deals with the mechanism of development of hypercholesterolemic atherosclerosis. Hypercholesterolemia increases the formation of numerous atherogenic biomolecules including reactive oxygen species (ROS), proinflammatory cytokines [interleukin (IL)-1, IL-2, IL-6, IL-8, tumor necrosis factor-alpha (TNF-α)], expression of intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), E-selectin, monocyte chemoattractant protein-1 (MCP-1), granulocyte macrophage-colony stimulating factor (GM-CSF) and numerous growth factors [insulin-like growth factor-1 (IGF-1), platelet-derived growth factor-1 (PDGF-1) and transforming growth factor-beta (TGF-β)]. ROS mildly oxidizes low-density lipoprotein-cholesterol (LDL-C) to form minimally modified LDL (MM-LDL) which is further oxidized to form oxidized LDL (OX-LDL). Hypercholesterolemia also activates nuclear factor-kappa-B (NF-κB). The above atherogenic biomolecules are involved in the development of atherosclerosis which has been described in detail. Hypercholesterolemia also assists in the development of atherosclerosis through AGE (advanced glycation end-products)-RAGE (receptor for AGE) axis and C-reactive protein (CRP). Hypercholesterolemia is associated with increases in AGE, oxidative stress [AGE/sRAGE (soluble receptor for AGE)] and C-reactive protein, and decreases in the sRAGE, which are known to be implicated in the development of atherosclerosis. In conclusion, hypercholesterolemia induces atherosclerosis through increases in atherogenic biomolecules, AGE-RAGE axis and CRP.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".