3D-printed sacrificial molds for high-resolution, patient-specific hydrogel heart valve engineering
Bibliographic record
Abstract
Abstract The fabrication of anatomically accurate, cellularized heart valve substitutes remains a significant challenge in tissue engineering, particularly for pediatric and patient-specific applications. While three-dimensional (3D) bioprinting enables the creation of complex geometries, it often compromises cell viability and lacks the precision required for small-scale constructs. In this study, we present a high-fidelity, reproducible molding technique using 3D-printed sugar glass molds to engineer custom, alginate-based hydrogel cellularized heart valves. Human adipose-derived stromal cells (ASCs) were used as the cell source due to their accessibility and regenerative potential. This approach overcomes the limitations of conventional molding and bioprinting by enabling the reproduction of intricate anatomical features, including the sinuses of Valsalva, which are critical for physiological hemodynamics. The molding method maintains high cell viability (>90%) at the time of fabrication and the process supports both scalability and automation. Sugar glass molds for valve sizes from 16 to 26 mm inner diameter were printed with 90% of the mold surface within a ±0.3 mm deviation of the reference computer-aided design model. Cellularized valves cultured in a custom perfusion bioreactor retained structural integrity and cell viability over a 14 d period. This biofabrication strategy offers a promising platform for engineering patient-specific heart valves and also lays the groundwork for in vitro disease modeling, including valve mineralization, using living cells such as ASCs.
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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) | 0.001 | 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".