Myocardial Infarction Following Atherosclerosis in Murine Models
Bibliographic record
Abstract
The most widely used mouse models for atherosclerosis are LDL receptor knockout (KO) mice and apolipoprotein E (apoE) KO mice fed standard chow diets or lipid-supplemented diets. Unfortunately, these do not usually exhibit myocardial infarction or other features of human cardiovascular disease such as occlusive coronary artery disease, cardiac dysfunction and/or reduced lifespan. Surgical models of myocardial infarction are successfully used for drug testing analyses during acute ischemia, but do not allow investigation of underlying mechanisms related to atherosclerotic coronary artery disease. Recently, experts in the pharmaceutical industry as well as some at the US Food and Drug Administration have identified inadequate animal models as being one of the major hurdles in drug discovery and development. There is an important need for additional well-characterized, genetically manipulable, small animal models that mimic many features of human coronary heart disease (CHD), which would provide investigators in academia and in the pharmaceutical industry with a better system to unravel the pathophysiology of atherosclerotic CHD and to evaluate pre-clinical drug candidates. Here we will review recently developed mouse models of occlusive CHD, focusing on mice lacking expression of the HDL receptor, SR-BI in the context of reduced expression of apoE.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".