228.6: Vascularizing a human-scale bioartificial pancreas using sacrificial embedded 3D printing into self-healing alginate
Notice bibliographique
Résumé
Introduction: With beta cell therapies emerging as a pathway towards a cure for type 1 diabetes, the need to engineer transplantation methods has become increasingly critical. A major limitation in the conception of a bioartificial pancreas is implementing vascularization to achieve sufficient cell survival and function. Layer-by-layer 3D printing offers an avenue to create perfusable artificial tissues, but the need for robust 3D printable materials and lengthy printing times (several hours) limits this approach in its feasibility, scalability, and accessibility. By 3D printing directly into a self-healing, cell-laden bath (embedded 3D printing), inks are provided with mechanical support in situ (Fig. 1), build times are significantly decreased (by ~90%), and more complex, multi-material designs become achievable. Here, we have engineered a self-healing alginate that can be used for embedded 3D printing to rapidly create perfusable bioartificial pancreas tissue.Methods: Self-healing alginates were created by partially gelling with calcium ions and were characterized using oscillatory and rotational rheometry. Pluronic F127 (35% w/v) was used as a sacrificial ink to generate perfusable vascular networks via embedded 3D printing. The viability and insulin secretion response of MIN6 cells were assessed after 2 days of perfusion. The viability of induced pluripotent stem cell (iPSC)-derived pancreatic progenitor aggregates were studied after 5 days of perfusion as they were differentiated from the primitive gut tube to the pancreatic endoderm state. Results: We show that self-healing matrices can be engineered from alginate by crosslinking it with 10.0 to 25.0 mM of calcium ions. We then measured the self-healing property of these materials by their yield stress (11.0±0.6 to 51.0±3.5 Pa), flow index (0.48±0.01 to 0.53±0.02), and consistency index (12.9±0.5 to 18.3±2.9). Formulations characterized by an Oldroyd number of ~1.52 yielded the most reproducible prints and were conducive to generating multi-branched vascular networks (Fig. 2a). Filament diameters were controlled (0.4-1.8 mm) by varying print speeds. Both MIN6 cells and iPSC-derived pancreatic endoderm cells or aggregates (Fig. 2d) retained high viability immediately after immobilization (≥90%). Following 2 days of perfusion culture, irrigated MIN6 constructs remained viable within a 600±85 μm radius of each channel and were glucose responsive (Fig. 2b and 2c). MIN6 tissues with more complex vascular networks (up to 10 branching channels) remained viable after 2 days of perfusion as well. Similarly, only the iPSC-derived pancreatic progenitor aggregates localized around the perfusion channels remained viable following 5 days of perfusion culture.Conclusion: Here we show that we can prepare a self-healing alginate that can be used to streamline the fabrication of perfusable bioartificial pancreas tissues with complex vascular networks. References: 1. Moeun, B. N., Da Ling, S., Gasparrini, M., Rutman, A. K., Negi, S., Paraskevas, S., et al. Islet Encapsulation: a Long-Term Treatment for Type 1 Diabetes. Encyclopedia of Tissue Engineering and Regenerative Medicine. 2019; ed. R. L. Reis (Oxford: Academic Press), 217–231. 2. Skylar-Scott, M. A., Uzel, S. G., Nam, L. L., Ahrens, J. H., Truby, R. L., Damaraju, S., & Lewis, J. A. Biomanufacturing of organ-specific tissues with high cellular density and embedded vascular channels. Science advances. 2019; 5(9), eaaw2459. 3. Grosskopf, A. K., Truby, R. L., Kim, H., Perazzo, A., Lewis, J. A., & Stone, H. A. Viscoplastic matrix materials for embedded 3D printing. ACS applied materials & interfaces. 2018; 10(27), 23353-23361.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».