Islet amyloid deposition limits the viability of human islet grafts but not porcine islet grafts
Bibliographic record
Abstract
Islet transplantation is a promising treatment for diabetes but long-term success is limited by progressive graft loss. Aggregates of the beta cell peptide islet amyloid polypeptide (IAPP) promote beta cell apoptosis and rapid amyloid formation occurs in transplanted islets. Porcine islets are an attractive alternative islet source as they demonstrate long-term graft survival. We compared the capacity of transplanted human and porcine islets to form amyloid as an explanation for differences in graft survival. Human islets were transplanted into streptozotocin-diabetic immune-deficient mice. Amyloid deposition was detectable at 4 weeks posttransplantation and was associated with islet graft failure. More extensive amyloid deposition was observed after 8 weeks. By contrast, no amyloid was detected in transplanted neonatal or adult porcine islets that had maintained normoglycemia for up to 195 days. To determine whether differences in IAPP sequence between humans and pigs could explain differences in amyloid formation and transplant viability, we sequenced porcine IAPP. Porcine IAPP differs from the human sequence at 10 positions and includes substitutions predicted to reduce its amyloidogenicity. Synthetic porcine IAPP was considerably less amyloidogenic than human IAPP as determined by transmission electron microscopy, circular dichroism, and thioflavin T binding. Viability assays indicated that porcine IAPP is significantly less toxic to INS-1 beta cells than human IAPP. Our findings demonstrate that species differences in IAPP sequence can explain the lack of amyloid formation and improved survival of transplanted porcine islets. These data highlight the potential of porcine islet transplantation as a therapeutic approach for human diabetes.
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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 | 0.001 | 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 | 0.001 | 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".