Bioengineering a highly vascularized matrix for the ectopic transplantation of islets
Bibliographic record
Abstract
Islet transplantation is a promising treatment for Type 1 diabetes; however limitations of the intra-portal site and poor revascularization of islets must be overcome. We hypothesize that engineering a highly vascularized collagen-based construct will allow islet graft survival and function in alternative sites. In this study, we developed such a collagen-based biomaterial. Neonatal porcine islets (NPIs) were embedded in collagen matrices crosslinked with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide and N-hydroxysuccinimide containing combinations of chondroitin-6-sulfate, chitosan, and laminin, and compared with controls cultured in standard media. Islets were examined for insulin secretory activity after 24 h and 4 d and for apoptotic cell death and matrix integrity after 7 d in vitro. These same NPI/collagen constructs were transplanted subcutaneously in immunoincompetent B6.Rag-/- mice and then assessed for islet survival and vascularization. At all time points assessed during in vitro culture there were no significant differences in insulin secretory activity between control islets and those embedded in the collagen constructs, indicating that the collagen matrix had no adverse effect on islet function. Less cell death was observed in the matrix with all co-polymers compared with the other matrices tested. Immunohistochemical analysis of the grafts post-transplant confirmed the presence of intact insulin-positive islets; grafts were also shown to be vascularized by von Willebrand factor staining. This study demonstrates that a collagen, chondroitin-6-sulfate, chitosan, and laminin matrix supports islet function in vitro and moreover allows islet survival and vascularization post-transplantation; therefore, this bio-engineered vascularized construct is capable of supporting islet survival.
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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".