Mind the Gap: Genetic Variation and Personalized Therapies for Cardiomyopathies
Bibliographic record
Abstract
Inherited cardiomyopathies are cardiovascular disorders that are one of the leading causes of death and are strongly associated with genetic mutations. These include hypertrophic, dilated, restrictive, as well as arrhythmogenic right ventricular cardiomyopathies. Among the patients presenting with these specific forms of cardiomyopathies, there is significant phenotypic, genotypic, and environmental heterogeneity. Over the years, the identification of the underlying mutations common to specific forms of cardiomyopathies have facilitated clinic diagnosis. However, the variation between patient genetics and phenotypes highlights the need for improved understanding of these diseases and the development of innovative treatments. To better understand the diseases, researchers are capitalizing on two innovative technologies: cardiac reprogramming and gene editing using CRISPR-Cas9. Deriving cardiomyocytes from patient blood samples and gene editing allows for the efficient generation of cellular and animal models that allow researchers to model the disease more accurately. In addition, the recent advances in high throughput drug screening allows for the efficient testing of patient-derived cardiomyocytes for patient-specific susceptibility to various drugs that are currently approved. In addition, this technology can facilitate the development of new pharmacological compounds for the treatment of specific cardiomyopathies. Overall, the recent technological advances in molecular medicine now presents an opportunity to gain unprecedented insight into solving the complex issue of inherited cardiomyopathies. These techniques pave the way for the new generation of personalized medicine in treating cardiovascular diseases.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".