Nano‐Enabled Approaches for Stem Cell‐Based Cardiac Tissue Engineering
Bibliographic record
Abstract
Cardiac diseases are the most prevalent causes of mortality in the world, putting a major economic burden on global healthcare system. Tissue engineering strategies aim at developing efficient therapeutic approaches to overcome the current challenges in prolonging patients survival upon cardiac diseases. The integration of advanced biomaterials and stem cells has offered enormous promises for regeneration of damaged myocardium. Natural or synthetic biomaterials have been extensively used to deliver cells or bioactive molecules to the site of injury in heart. Additionally, nano-enabled approaches (e.g., nanomaterials, nanofeatured surfaces) have been instrumental in developing suitable scaffolding biomaterials and regulating stem cells microenvironment to achieve functional therapeutic outcomes. This review article explores tissue engineering strategies, which have emphasized on the use of nano-enabled approaches in combination with stem cells for regeneration and repair of injured myocardium upon myocardial infarction (MI). Primarily a wide range of biomaterials, along with different types of stem cells, which have utilized in cardiac tissue engineering will be presented. Then integration of nanomaterials and surface nanotopographies with biomaterials and stem cells for myocardial regeneration will be presented. The advantages and challenges of these approaches will be reviewed and future perspective will be discussed.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".