Iterative sacrificial 3D printing and polymer casting to create complex vascular grafts and multi-compartment bioartificial organs
Bibliographic record
Abstract
ABSTRACT Several emerging strategies to engineer artificial organs employ 3D printing to create vascular templates to provide nutrients and oxygen to immobilized cells. Significant challenges emerge when considering clinical implementation such as immune rejection of allogeneic cell sources, as well as achieving adequate perfusion and integration with endogenous vasculature. We propose a method by which cell-laden hydrogels are molded around ready-made polymeric vascular templates created via 3D printing to create human-scale artificial organs with internal vasculature. We applied this technique to create bioartificial pancreas systems with up to 9 internal flow channels via sacrificial carbohydrate glass 3D printing, porogen-loaded polycarbonate polyurethane dip-coating, followed by casting cell-laden hydrogels around the vascular templates. We optimized porogen size and concentration to maximise the porosity of our scaffolds without compromising mechanical properties, resulting in suture retention strength and compliance respectively matching commercial vascular grafts and native vessels. Bioreactor perfusion studies showed survival and maturation of stem cell derived pancreatic islets without significant differences to traditional suspension culture protocols. Insulin response dynamics were rapid in response to a glucose challenge at the perfusion inlet. Transplantation of the devices as iliac arteriovenous shunts in nondiabetic pigs confirmed safety and patency. These results show promise for the development of an implantable vascularized pancreas for the treatment of type 1 diabetes and demonstrate how bioartificial organs with engineered vascular geometries can be designed for translational applications.
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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".