Decellularized pancreas as a native extracellular matrix scaffold for pancreatic islet seeding and culture
Bibliographic record
Abstract
Diabetes mellitus involves the loss of function and/or absolute numbers of insulin-producing β cells in pancreatic islets. Islet transplantation is currently being investigated as a potential cure, and advances in tissue engineering methods can be used to improve pancreatic islets survival and functionality. Transplanted islets experience anoikis, hypoxia, and inflammation-mediated immune response, leading to early damage and subsequent failure of the graft. Recent development in tissue engineering enables the use of decellularized organs as scaffolds for cell therapies. Decellularized pancreas could be a suitable scaffold as it can retain the native extracellular matrix and vasculature. In this study, mouse pancreata were decellularized by perfusion using 0.5% sodium dodecyl sulfate. Different characterizations revealed that the resulting matrix was free of cells and retained part of the pancreas extracellular matrix including the vasculature and its internal elastic basal lamina, the ducts with their basal membrane, and the glycosaminoglycan and collagen structures. Islets were infused into the ductal system of decellularized pancreata, and glucose-stimulated insulin secretion results confirmed their functionality after 48 hr. Also, recellularizing the decellularized pancreas with green fluorescent protein-tagged INS-1 cells and culturing the system over 120 days confirmed the biocompatibility and non-toxic nature of the scaffold. Green fluorescent protein-tagged INS-1 cells formed pseudoislets that were, over time, budding out of the decellularized pancreata. Decellularized pancreatic scaffolds seeded with endocrine pancreatic tissue could be a potential bioengineered organ 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) | 0.000 | 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".