State of the Art and Perspectives of Gene Therapy in Heart Failure. A Scientific Statement of the Heart Failure Association of the ESC, the ESC Council on Cardiovascular Genomics and the ESC Working Group on Myocardial & Pericardial Diseases
Bibliographic record
Abstract
Gene therapy has recently become a reality in the treatment of cardiovascular diseases. Strategies to modulate gene expression using antisense oligonucleotides or small interfering RNA are proving to be safe and effective in the clinic. Adeno-associated viral vector-based gene delivery and CRISPR-Cas9-based genome editing have emerged as efficient strategies for gene delivery and repair in humans. Overall, gene therapy holds the promise not only of expanding current treatment options, but also of intervening in previously untackled causal disease mechanisms with little side effects. This scientific statement provides a comprehensive overview of the various modalities of gene therapy used to treat heart failure and some of its risk factors, and their application in the clinical setting. It discusses specifically the possibilities of gene therapy for hereditary heart diseases and (non)-genetic heart failure. Furthermore, it addresses safety and clinical trial design issues and challenges for future regulatory strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".