Amyloid Formation in Human Islets Is Enhanced by Heparin and Inhibited by Heparinase
Bibliographic record
Abstract
Islet transplantation is a promising therapy for patients with diabetes, but its long-term success is limited by many factors, including the formation of islet amyloid deposits. Heparin is employed in clinical islet transplantation to reduce clotting but also promotes fibrillization of amyloidogenic proteins. We hypothesized that heparin treatment of islets during pre-transplant culture may enhance amyloid formation leading to beta cell loss and graft dysfunction. Heparin promoted the fibrillization of human islet amyloid polypeptide (IAPP) and enhanced its toxicity to INS-1 beta cells. Heparin increased amyloid deposition in cultured human islets, but surprisingly decreased islet cell apoptosis. Treatment of human islets with heparin prior to transplantation increased the likelihood of graft failure. Removal of islet heparan sulfate glycosaminoglycans, which localize with islet amyloid deposits in type 2 diabetes, by heparinase treatment decreased amyloid deposition and protected against islet cell death. These findings raise the possibility that pretransplant treatment of human islets with heparin could potentiate IAPP aggregation and amyloid formation and may be detrimental to subsequent graft function.
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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".