Bibliographic record
Abstract
In this mini-review several commonly used animal models of atherosclerosis have been discussed. Among them, emphasis has been made on mice, rabbits, pigs and non-human primates. Although these animal models have played a significant role in our understanding of induction of atherosclerotic lesions, we still lack a reliable animal model for regression of the disease. Researchers have reported several genetically modified and transgenic animal models that replicate human atherosclerosis, however each of current animal models have some limitations. Among these animal models, the apolipoprotein (apo) E-knockout (KO) mice have been used extensively because they develop spontaneous atherosclerosis. Furthermore, atherosclerotic lesions developed in this model depending on experimental design may resemble humans' stable and unstable atherosclerotic lesions. This mouse model of hypercholesterolemia and atherosclerosis has been also used to investigate the impact of oxidative stress and inflammation on atherogenesis. Low density lipoprotein (LDL)-r-KO mice are a model of human familial hypercholesterolemia. However, unlike apo E-KO mice, the LDL-r-KO mice do not develop spontaneous atherosclerosis. Both apo E-KO and LDL-r-KO mice have been employed to generate other relevant mouse models of cardiovascular disease through breeding strategies. In addition to mice, rabbits have been used extensively particularly to understand the mechanisms of cholesterol-induced atherosclerosis. The present review paper details the characteristics of animal models that are used in atherosclerosis research.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.006 | 0.005 |
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".