Fibrin supports subcutaneous neonatal porcine islet transplantation without the need for pre‐vascularization
Bibliographic record
Abstract
BACKGROUND: Neonatal porcine islets (NPIs) are a promising tissue source for clinical islet xenotransplantation. To facilitate graft monitoring and recovery if needed, an extra-hepatic transplant site would be optimal. In addition, islet transplantation into the portal vein has been associated with life-threatening intraperitoneal bleeding, portal vein thrombosis, hepatic steatosis, and loss of islet graft function. Although it is hypoxic, the subcutaneous space is a potential extra-hepatic location for clinical islet transplantation. In this study, we explore the benefits of fibrin scaffolds in enhancing the engraftment and long-term function of NPI grafts in this ectopic site. METHODS: Diabetic immune-compromised mice were transplanted with 5000 NPIs under the kidney capsule (KC), and subcutaneously with or without fibrin (SC + F, SC, respectively). All mice were monitored for reversal of hyperglycemia and long-term metabolic function. RESULTS: All mice transplanted with NPI under the KC or SC + F (12/12 and 17/17, respectively) achieved normal fasting blood glucose levels between 5 and 22 weeks post-transplantation and displayed normal glucose tolerance during an intraperitoneal glucose tolerance test. In contrast, NPIs transplanted SC with no fibrin (n = 7) failed to obtain normoglycemia. CONCLUSION: Fibrin matrix facilitates engraftment of NPIs in the subcutaneous site of diabetic mice. These data support further investigation of the subcutaneous site for clinical islet xenotransplantation.
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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) | 0.003 | 0.001 |
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".