Multi-Compartment Bioartificial Organs Engineered via Iterative Sacrificial 3D Printing and Vascular Integration
Bibliographic record
Abstract
Several biofabrication strategies have emerged to incorporate vascular networks within bioartificial tissues. Significant challenges emerge when considering clinical implementation, such as allogeneic graft rejection, as well as achieving adequate perfusion and integration with the endogenous vasculature of human-scale tissues. We propose iterative sacrificial 3D printing and polymer casting to create modular convection-enhanced therapeutic cell delivery systems. The resulting multi-compartment devices comprise a porous vascular graft with controlled geometry and branching, along with an external cell-loading compartment. The devices can be autoclaved, stored, and filled with cell-laden hydrogels for long-term in vitro culture or transplantation. Bioreactor perfusion studies with insulin-secreting β cells showed the potential of this strategy in achieving rapid secretory response dynamics and stem cell-derived islet maturation. Transplantation as iliac arteriovenous shunts in pigs indicated safety and patency. Multi-compartment vascular devices could serve as off-the-shelf human-scale bioartificial organs for extended in vitro culture studies and for transplantation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".