Evaluating Micro- and Macro-vascular Disease, the End Stage of Atherosclerosis, in Rat Models
Bibliographic record
Abstract
Development of effective treatment or, more critically, preventative measures against atherosclerosis and cardiovascular disease will require animal models that mimic the disease processes seen in humans and permit identification of the genetic and physiological factors. The Rat is normally resistant to cardiovascular disease, but a number of genetic mutations make affected strains of rats highly susceptible to atherosclerosis and micro- and macro-vascular disease that is highly analogous to human disease. These models of obesity develop the metabolic syndrome and type 2 diabetes, hyperinsulinemia, hyperlipidemia, vascular and myocardial dysfunction, and end-stage lesions in the heart and kidney. The models offer the prospect of both genetic and molecular biology studies that are linked directly to spontaneous cardiovascular disease and exploration of putative preventative or treatment approaches, including pharmaceutical agents. Use of small animal models of cardiovascular disease is dependent on appropriate experimental design and techniques that take account of the complex nature of the disease processes. Detailed experimental procedures for the use of rat models, including handling and treatment of animals, choice of experimental variables and endpoints, assay methods, and histological and electron microscopy techniques are covered in this chapter.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".