Porcine Marginal Mass Islet Autografts Resist Metabolic Failure Over Time and Are Enhanced by Early Treatment with Liraglutide
Bibliographic record
Abstract
Although insulin independence is maintained in most islet recipients at 1 yr after transplant, extended follow-up has revealed that many patients will eventually require insulin therapy. Previous studies have shown that islet autografts are prone to chronic failure in large animals and humans, suggesting that nonimmunological events contribute to islet graft functional decay. Early intervention with therapies that promote graft stability should provide a measurable benefit over time. In this study, the efficacy of the long-acting glucagon-like peptide-1 analog liraglutide was explored in a porcine marginal mass islet autograft transplant model. Incubation with liraglutide enhanced porcine islet survival and function after prolonged culture. Most vehicle-treated (83%) and liraglutide-treated (80%) animals became insulin independent after islet autotransplantation. Although liraglutide therapy did not improve insulin independence rates or blood glucose levels after transplant, a significant increase in insulin secretion and acute-phase insulin response was observed in treated animals. Surprisingly, no evidence for deterioration of graft function was observed in any of the transplanted animals over more than 18 months of follow-up despite significant weight gain; in fact, an enhanced response to glucose developed over time even in control animals. Histological analysis showed that intraportally transplanted islets remained highly insulin positive, retained alpha-cells, and did not form amyloid deposits. This study demonstrates that marginal mass porcine islet autografts have stable long-term function, even in the presence of an increasing metabolic demand. These results are discrepant with previous large animal studies and suggest that porcine islets may be resistant to metabolic failure.
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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".