Stem Cell Therapy in Heart Diseases: A Review of Selected New Perspectives,Practical Considerations and Clinical Applications
Bibliographic record
Abstract
Degeneration of cardiac tissues is considered a major cause of mortality in the western world and is expected to be a greater problem in the forthcoming decades. Cardiac damage is associated with dysfunction and irreversible loss of cardiomyocytes. Stem cell therapy for ischemic heart failure is very promising approach in cardiovascular medicine. Initial trials have indicated the ability of cardiomyocytes to regenerate after myocardial injury. These preliminary trials aim to translate cardiac regeneration strategies into clinical practice. In spite of advances, current therapeutic strategies to ischemic heart failure remain very limited. Moreover, major obstacles still need to be solved before stem cell therapy can be fully applied. This review addresses the current state of research and experimental data regarding embryonic stem cells (ESCs), myoblast transplantation, histological and functional analysis of transplantation of co-cultured myoblasts and mesenchymal stem cells, as well as comparison between mononuclear and mesenchymal stem cells in a model of myocardium infarction. We also discuss how research with stem cell transplantation could translate to improvement of cardiac function.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.002 |
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".