Stem cell therapy for cardiac regeneration: past, present, and future
Bibliographic record
Abstract
Cardiac disorders remain the leading cause of mortality worldwide. Current clinical strategies, including drug therapy, surgical interventions, and organ transplantation offer limited benefits to patients without regenerating the damaged myocardium. Over the past decade, stem cell therapy has generated a keen interest owing to its unique self-renewal and immune privileged characteristics. Furthermore, the ability of stem cells to differentiate into specialized cell types, has made them a popular therapeutic tool against various diseases. This comprehensive review provides an overview of therapeutic potential of different types of stem cells in reference to cardiovascular diseases. Furthermore, it sheds light on the advantages and limitations associated with each cell type. An in-depth analysis of the challenges associated with stem cell research and the hurdles for its clinical translation and their possible solutions have also been elaborated upon. It examines the controversies surrounding embryonic stem cells and the emergence of alternative approaches, such as the use of induced pluripotent stem cells for cardiac therapeutic applications. Overall, this review serves as a valuable resource for researchers, clinicians, and policymakers involved in the field of regenerative medicine, guiding the development of safe and effective stem cell-based therapies to revolutionize patient care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".