Strategies and Challenges to Myocardial Replacement Therapy
Bibliographic record
Abstract
UNLABELLED: Cardiovascular diseases account for the majority of deaths globally and are a significant drain on economic resources. Although heart transplants and left-ventricle assist devices are the solution for some, the best chance for many patients who suffer because of a myocardial infarction, heart failure, or a congenital heart disease may be cell-based regenerative therapies. Such therapies can be divided into two categories: the application of a cell suspension and the implantation of an in vitro engineered tissue construct to the damaged area of the heart. Both strategies have their advantages and challenges, and in this review, we discuss the current state of the art in myocardial regeneration, the challenges to success, and the future direction of the field. SIGNIFICANCE: This article outlines the advantages and limitations of the cell injection and patch approaches to cardiac regenerative therapy. If the field is to move forward, some fundamental questions require answers, including the limitations to the use of animal models for human cell-transplantation studies; the best way to measure success in terms of functional improvements, histological integration, electrical coupling, and arrhythmias; and where the cells should be applied for maximal benefit-the epicardium or the myocardium.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".