Nano‐Enabled Approaches for Stem Cell‐Based Cardiac Tissue Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it