Fibroblast populated collagen matrix promotes islet survival and reduces the number of islets required for diabetes reversal
Bibliographic record
Abstract
Islet transplantation represents a viable treatment for type 1 diabetes. However, due to loss of substantial mass of islets early after transplantation, islets from two or more donors are required to achieve insulin independence. Islet-extracellular matrix disengagement, which occurs during islet isolation process, leads to subsequent islet cell apoptosis and is an important contributing factor to early islet loss. In this study, we developed a fibroblast populated collagen matrix (FPCM) as a novel scaffold to improve islet cell viability and function post-transplantation. FPCM was developed by embedding fibroblasts within type-I collagen and used as scaffold for islet grafts. Viability and insulin secretory function of islets embedded within FPCM was evaluated in vitro and in a syngeneic murine islet transplantation model. Islets embedded within acellular matrix or naked islets were used as control. Islet cell survival and function was markedly improved particularly after embedding within FPCM. The composite scaffold significantly promoted islet isograft survival and reduced the critical islet mass required for diabetes reversal by half (from 200 to 100 islets per recipient). Fibroblast embedded within FPCM produced fibronectin and growth factors and induced islet cell proliferation. No evidence of fibroblast over-growth within composite grafts was noticed. These results confirm that FPCM significantly promotes islet viability and functionality, enhances engraftment of islet grafts and decreases the critical islet mass needed to reverse hyperglycemia. This promising finding offers a new approach to reducing the number of islet donors per recipient and improving islet transplant outcome.
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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".