Oxygenation and function of endocrine bioartificial pancreatic tissue constructs under flow for preclinical optimization
Why this work is in the frame
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Bibliographic record
Abstract
Islet transplantation and more recently stem cell-derived islets were shown to successfully re-establish glycemic control in people with type 1 diabetes under immunosuppression. These results were achieved through intraportal infusion which leads to early graft losses and limits the capacity to contain and retrieve implanted cells in case of adverse events. Extra-hepatic sites and encapsulation devices have been developed to address these challenges and potentially create an immunoprotective or immune-privileged environment. Many strategies have achieved reversal of hyperglycemia in diabetic rodents. So far, the results have been less promising when transitioning to humans and larger animal models due to challenges in oxygenation and insulin delivery. We propose a versatile in vitro perfusion system to culture and experimentally study the function of centimeter-scale tissues and devices for insulin-secreting cell delivery. The system accommodates various tissue geometries, experimental readouts, and oxygenation tensions reflective of potential transplantation sites. We highlight the system's applications by using case studies to explore three prominent bioartificial endocrine pancreas (BAP) configurations: (I) with internal flow, (II) with internal flow and microvascularized, and (III) without internal flow. Oxygen concentration profiles modeled computationally were analogous to viability gradients observed experimentally through live/dead endpoint measurements and in case I, time-lapse fluorescence imaging was used to monitor the viability of GFP-expressing cells in real time. Intervascular BAPs were cultured under flow for up to 3 days and BAPs without internal flow for up to 7 days, showing glucose-responsive insulin secretion quantified through at-line non-disruptive sampling. This system can complement other preclinical platforms to de-risk and optimize BAPs and other artificial tissue designs prior to clinical studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it