Biofabrication of Sodium Alginate Hydrogel Scaffolds for Heart Valve Tissue Engineering
Bibliographic record
Abstract
Every year, thousands of aortic valve replacements must take place due to valve diseases. Tissue-engineered heart valves represent promising valve substitutes with remodeling, regeneration, and growth capabilities. However, the accurate reproduction of the complex three-dimensional (3D) anatomy of the aortic valve remains a challenge for current biofabrication methods. We present a novel technique for rapid fabrication of native-like tricuspid aortic valve scaffolds made of an alginate-based hydrogel. Using this technique, a sodium alginate hydrogel formulation is injected into a mold produced using a custom-made sugar glass 3D printer. The mold is then dissolved using a custom-made dissolving module, revealing the aortic valve scaffold. To assess the reproducibility of the technique, three scaffolds were thoroughly compared. CT (computed tomography) scans showed that the scaffolds respect the complex native geometry with minimal variations. The scaffolds were then tested in a cardiac bioreactor specially designed to reproduce physiological flow and pressure (aortic and ventricular) conditions. The flow and pressure profiles were similar to the physiological ones for the three valve scaffolds, with small variabilities. These early results establish the functional repeatability of this new biofabrication method and suggest its application for rapid fabrication of ready-to-use cell-seeded sodium alginate scaffolds for heart valve tissue engineering.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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 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".