Engineering human-scale perfusable tissues for diabetes cell therapy using 3D printing
Bibliographic record
Abstract
Pluripotent stem cell-derived beta cells have emerged as a potentially unlimited supply of therapeutic cells that can be used to treat type 1 diabetes (T1D). Encapsulation device designs have been envisioned to transplant these cells while assuring safety to the recipient and graft function. Although many designs have succeeded in reversing T1D in rodents and small animals, consistently replicating the same therapeutic effect in humans has been challenging. The scaling up of beta cell transplantation devices introduces challenges in mass transport, specifically in oxygenation and insulin distribution. This thesis focuses on how 3D printing can help us better address these hurdles, specifically through the development of biofabrication techniques and tissue culturing tools for vascularized devices. Two approaches in fabricating sacrificial templates for the vascularization of beta cell-laden hydrogels were studied. The first 3D prints the template out of carbohydrate glass and casts the hydrogel and cells around it, while the second uses embedded 3D printing to construct the template inside of the cell-laden hydrogel. For the first approach, the thermophysical properties of sugar glass inks were characterized and later optimized for freeform extrusion 3D printing. This work led to the development of a sucrose-based formulation with the capacity to 3D print centimeter-scale, self-supporting sacrificial templates that could be used to create perfusable hydrogels. Next, an in vitro perfusion system for the culture of centimeter-scale artificial tissue constructs was engineered. This platform was used to study the oxygenation, viability, and function of prominent beta cell encapsulation configurations, with or without internal vascularisation. In the second approach to engineer vascularized pancreatic tissues, a self-healing alginate matrix was engineered and used to streamline the creation of centimeter-scale cell-laden hydrogels with intricate vascular networks. Beta cells, including stem cell-derived islets, within these tissue constructs remained functional after being cultured under perfusion for up to 25 days in vitro. Overall, these advancements in 3D printing and biofabrication technology could be used to design and evaluate clinical scale vascularized beta cell-laden tissues and other bioartificial organ systems
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".