Current Stem Cell Delivery Methods for Myocardial Repair
Bibliographic record
Abstract
Heart failure commonly results from an irreparable damage due to cardiovascular diseases (CVDs), the leading cause of morbidity and mortality in the United States. In recent years, the rapid advancements in stem cell research have garnered much praise for paving the way to novel therapies in reversing myocardial injuries. Cell types currently investigated for cellular delivery include embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and adult stem cell lineages such as skeletal myoblasts, bone-marrow-derived stem cells (BMSCs), mesenchymal stem cells (MSCs), and cardiac stem cells (CSCs). To engraft these cells into patients' damaged myocardium, a variety of approaches (intramyocardial, transendocardial, transcoronary, venous, intravenous, intracoronary artery and retrograde venous administrations and bioengineered tissue transplantation) have been developed and explored. In this paper, we will discuss the pros and cons of these delivery modalities, the current state of their therapeutic potentials, and a multifaceted evaluation of their reported clinical feasibility, safety, and efficacy. While the issues of optimal delivery approach, the best progenitor stem cell type, the most effective dose, and timing of administration remain to be addressed, we are highly optimistic that stem cell therapy will provide a clinically viable option for myocardial regeneration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".