Improving the Pancreatic Islet Graft Structure and Function: Application of a Novel Bioengineered Composite Scaffold
Bibliographic record
Abstract
Introduction: Transplantation of the insulin-producing islet cells of the pancreas has been a promising strategy for management of type 1 diabetes. However, shortage of islet donors, and poor survival of the islets after transplantation are some of the main challenges of this procedure. Enzymatic digestion of the pancreas in the process of islet isolation results in the loss of peri-insular extracellular matrix (ECM) and peri-vascular basement membrane of the islets that can significantly disrupt the ECM-mediated interactions and result in apoptosis. Naturally a collagen matrix provides for a viable environment in which both stromal and islet cells can survive. However this matrix is mainly biodegradable and subject to gradual disintegration and contraction after transplantation. We have previously shown that a fibroblast populated collagen matrix (FPCM) significantly improves islet cell viability and glucose responsiveness and reduces the marginal islet mass required for hyperglycemia reversal in diabetic mice. Fibroblasts were incorporated to provide a favorable support for islets by producing various growth and angiogenic factors, while maintaining the integrity of the collagen matrix. Although this composite was able to promote the islet graft survival and function, it was still prone to gradual biodegradation. For this reason, we designed a novel bioengineered crosslinked-interpenetrating network of glycosaminoglycan (GAG) and type I collagen (Collagen-GAG matrix, CGM). When populated with fibroblasts (FP-CGM), it would provide an optimal matrix biomimetic with reduced contraction and enhanced mechanical strength for the transplanted islets. Methods: Pancreatic islets and dermal fibroblasts were obtained from Balb/c mice. Islets (50 islets per well in 48-well plates) were cultured in medium (2D), acellular collagen matrix (CM), FPCM, acellular CGM and FP-CGM composites. In order to prepare the FPCM and FP-CGM composites, 200,000 fibroblasts per 100μl of the gel were used. The viability of the islets within composites was assessed by a Live/Dead assay kit on days 1, 15 and 30 post culture. Islets were retrieved on days 1, 15 and 30 post culture and paraffin-embedded sections were stained for insulin, glucagon, caspase-3 and Ki67 and the β-cell proliferation rate was calculated. Retrieved islets were evaluated for glucose-stimulated insulin secretion. Finally, the expression of the islet key specific genes including insulin, PDX1, GLUT2, Pax4, and Pax6 were examined before and after culture at different conditions using reverse transcriptase-PCR analysis. Results: Our preliminary findings have demonstrated that our matrix is non-toxic to both fibroblasts and islets and able to withstand contraction in vitro: Live and Dead assay showed that islets perfectly survive within the new composite matrix. Histology revealed reductions in graft contraction and fibroblast cellularity, increased insulin and glucagon production, and decreased apoptosis rate compared to naked islets in 2D system. Islet morphology remained intact within the composite FP-CGM. PCR analysis showed high expression of islets key genes when islets are embedded within the proposed matrix. Conclusions: We have identified that the use of a crosslinked collagen-GAG graft has the potential to improve graft survival, improving the post-surgical outcome of the transplanted islets.
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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 | 0.001 | 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 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".