Gene Therapy in Cardiology: Is a Cure for Hypertrophic Cardiomyopathy on the Horizon?
Bibliographic record
Abstract
Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy worldwide, affecting approximately 1 in 500 individuals. Current therapeutic interventions include lifestyle optimisation, medications, septal reduction therapies, and, rarely, cardiac transplantation. Advances in our understanding of disease-causing genetic variants in HCM and their associated molecular mechanisms have led to the potential for targeted therapeutics and implementation of precision and personalised medicine. Results from preclinical research are promising and raise the question of whether cure of some subtypes of HCM may be possible in the future. This review provides an overview of current genetic therapy platforms, including 1) genome editing, 2) gene replacement, 3) allelic-specific silencing, and 4) signalling pathway modulation. The current applicability of each of these platforms within the paradigm of HCM is examined, with updates on current and emerging trials in each domain. Barriers and limitations within the current landscape are also highlighted. Despite recent advances, translation of genetic therapy for HCM to clinical practice is still in early development. In realising the promises of genetic HCM therapies, ethical and equitable access to safe gene therapy must be prioritised.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".