Canadian Journal of Cardiology Symposium Review Targeted Gene Therapy for the Treatment of Heart Failure
Bibliographic record
Abstract
Chronic heart failure is one of the leading causes of morbidity and mortality in Western countries and is a major financial burden to the health care system. Pharmacologic treatment and implanting devices are the predominant therapeutic approaches. They improve survival and have offered significant improvement in patient quality of life, but they fall short of producing an authentic remedy. Cardiac gene therapy, the introduction of genetic material to the heart, offers great promise in filling this void. In-depth knowledge of the underlying mechanisms of heart failure is, obviously, a prerequisite to achieve this aim. Extensive research in the past decades, supported by numerous methodological breakthroughs, such as transgenic animal model development, has led to a better understanding of the cardiovascular diseases and, inadvertently, to the identification of several candidate genes. Of the genes that can be targeted for gene transfer, calcium cycling proteins are prominent, as abnormalities in calcium handling are key determinants of heart failure. A major impediment, however, has been the development of a safe, yet efficient, delivery system. Nonviral vectors have been used extensively in clinical trials, but they fail to produce significant gene expression. Viral vectors, especially adenoviral, on the other hand, can produce high levels of expression, at the expense of safety. Adeno-associated viral vectors have emerged in recent years as promising myocardial gene delivery vehicles. They can sustain gene expression at a therapeutic level and maintain it over extended periods of time, even for years, and, most important, without a safety risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.020 |
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".