Genetic Animal Models for Arrhythmogenic Cardiomyopathy
Bibliographic record
Abstract
Arrhythmogenic cardiomyopathy has been clinically defined since the 1980s and causes right or biventricular cardiomyopathy associated with ventricular arrhythmia. Although it is a rare cardiac disease, it is responsible for a significant proportion of sudden cardiac deaths, especially in athletes. The majority of patients with arrhythmogenic cardiomyopathy carry one or more genetic variants in desmosomal genes. In the 1990s, several knockout mouse models of genes encoding for desmosomal proteins involved in cell-cell adhesion revealed for the first time embryonic lethality due to cardiac defects. Influenced by these initial discoveries in mice, arrhythmogenic cardiomyopathy received an increasing interest in human cardiovascular genetics, leading to the discovery of mutations initially in desmosomal genes and later on in more than 25 different genes. Of note, even in the clinic, routine genetic diagnostics are important for risk prediction of patients and their relatives with arrhythmogenic cardiomyopathy. Based on improvements in genetic animal engineering, different transgenic, knock-in, or cardiac-specific knockout animal models for desmosomal and nondesmosomal proteins have been generated, leading to important discoveries in this field. Here, we present an overview about the existing animal models of arrhythmogenic cardiomyopathy with a focus on the underlying pathomechanism and its importance for understanding of this disease. Prospectively, novel mechanistic insights gained from the whole animal, organ, tissue, cellular, and molecular levels will lead to the development of efficient personalized therapies for treatment of arrhythmogenic cardiomyopathy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.003 | 0.002 |
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".