An in vitro Perfused Macroencapsulation Device to Study Hemocompatibility and Survival of Islet-Like Cell Clusters
Bibliographic record
Abstract
Transplantation of hydrogel-encapsulated pancreatic islets is a promising long-term treatment for type 1 diabetes that restores blood glucose regulation while providing graft immunoprotection. Most human-scale islet encapsulation devices that rely solely on diffusion fail to provide sufficient surface area to meet islet oxygen demands. Perfused macroencapsulation devices use blood flow to mitigate oxygen limitations but increase the complexity of blood-device interactions. Here we describe a human-scale in vitro perfusion system to study hemocompatibility and performance of islet-like cell clusters (ILCs) in alginate hydrogel. A cylindrical perfusion device was designed for multi-day culture without leakage, contamination, or flow occlusion. Rat blood perfusion was assessed for prothrombin time and international normalized ratio and demonstrated no significant change in clotting time. Ex vivo perfusion performed with rats showed patency of the device for over 100 min using Doppler ultrasound imaging. PET-CT imaging of the device successfully visualized metabolically active mouse insulinoma 6 ILCs. ILCs cultured for 7 days under static conditions exhibited abnormal morphology and increased activated caspase-3 staining when compared with the perfused device. These findings reinforce the need for convective transport in macroencapsulation strategies and offer a robust and versatile in vitro system to better inform preclinical design.
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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".