Producing and Testing Prototype Tissue-Engineered 3D Tri-Leaflet Valved Stents on Biodegradable Poly-ε-Caprolactone Scaffolds
Bibliographic record
Abstract
Transcatheter pulmonary valve replacement is a minimally-invasive alternative treatment for right ventricular outflow tract dysfunction and has been rapidly evolving over the past years. Heart valve prostheses currently available still have major limitations. Therefore, one of the significant challenges for the future is the roll out of transcatheter tissue engineered pulmonary valve replacement to more patients. In the present study, biodegradable poly-ε-caprolactone (PCL) nanofiber scaffolds in the form of a 3D leaflet matrix were successfully seeded with human endothelial colony-forming cells (ECFCs), human induced pluripotent stem cell-derived MSCs (hMSCs), and porcine MSCs (pMSCs) for three weeks for the generation of 3D tissue-engineered tri-leaflet valved stent grafts. The cell adhesion, proliferation, and distribution of these 3D heart leaflets was analyzed using fluorescence microscopy and scanning electron microscopy (SEM). All cell lineages were able to increase the overgrown leaflet area within the three-week timeframe. While hMSCs showed a consistent growth rate over the course of three weeks, ECFSs showed almost no increase between days 7 and 14 until a growth spurt appeared between days 14 and 21. More than 90% of heart valve leaflets were covered with cells after the full three-week culturing cycle in nearly all leaflet areas, regardless of which cell type was used. This study shows that seeded biodegradable PCL nanofiber scaffolds incorporated in nitinol or biodegradable stents will offer a new therapeutic option in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".