Biomaterial strategies to improve the efficacy of bone marrow cell therapy for myocardial infarction
Bibliographic record
Abstract
INTRODUCTION: The feasibility and safety of bone marrow cell (BMC) therapy for cardiac repair following myocardial infarction has been demonstrated in clinical studies, albeit with relatively modest structural and functional benefits. In response to the shortcomings of BMC therapy, the use of biomaterials to enhance cell transplantation is being investigated. Areas covered: The authors first review what has been learned from BMC therapies for the treatment of myocardial infarction in animal models and in clinical trials. Some issues that may be limiting the efficacy of BMC therapy are then described. Lastly, they summarize several biomaterial approaches that have been reported to improve transplanted cell retention and functional outcome, and then focus on how a material can enhance cell function such as proliferation, viability, endothelial differentiation and angiogenic potential. Expert opinion: Improvements are needed if BMC therapy is to become a viable treatment in the clinic. There is optimism that a biomaterial strategy will lead to superior results compared to the cell therapy alone. Through the identification of underlying cell-biomaterial mechanisms, the establishment of comparative standards, and an awareness of the lessons learned from cell therapy trials, biomaterial-enhanced BMC therapy may become an option for the treatment of heart disease patients.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".