Gene and cell‐based therapies for heart disease
Bibliographic record
Abstract
Heart disease remains the prevalent cause of premature death and accounts for a significant proportion of all hospital admissions. Recent developments in understanding the molecular mechanisms of myocardial disease have led to the identification of new therapeutic targets, and the availability of vectors with enhanced myocardial tropism offers the opportunity for the design of gene therapies for both protection and rescue of the myocardium. Genetic therapies have been devised to treat complex diseases such as myocardial ischemia, heart failure, and inherited myopathies in various animal models. Some of these experimental therapies have made a successful transition to clinical trial and are being considered for use in human patients. The recent isolation of endothelial and cardiomyocyte precursor cells from adult bone marrow may permit the design of strategies for repair of the damaged heart. Cell-based therapies may have potential application in neovascularization and regeneration of ischemic and infarcted myocardium, in blood vessel reconstruction, and in bioengineering of artificial organs and prostheses. We expect that advances in the field will lead to the development of safer and more efficient vectors. The advent of genomic screening technology should allow the identification of novel therapeutic targets and facilitate the detection of disease-causing polymorphisms that may lead to the design of individualized gene and cell-based therapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".