Nonhuman Primate Models for Islet Transplantation in Type 1 Diabetes Research
Bibliographic record
Abstract
Insulin-dependent diabetes mellitus is an autoimmune disease that causes a progressive destruction of the pancreatic beta cells. As a result, the patient requires exogenous insulin to maintain normal blood glucose levels. Both the pancreas and the islets of Langerhans have been transplanted successfully in humans and in animal models, resulting in full normalization of glucose homeostasis. However, insulin independence, transient or persistent, was documented in only a small fraction of cases until recently. The chronic immunosuppression required to avoid immunological rejection appears to be toxic to the islets and adds the risk of lymphoproliferative disease reported earlier. For islet transplantation to become the method of choice, it is essential first to identify islet-friendly immunosuppressive regimens and/or to develop methods that induce donor-specific tolerance and improve islet isolation and transplantation protocols. Indeed, researchers have already successfully allografted islets in the presence of nonsteroidal immunosuppression in a process known as the Edmonton protocol. An alternative method, gene therapy, could replace these other methods and better meet the insulin requirement of an individual without requiring pancreatic or islet transplantation. This alternative, however, requires animal models to develop and test clinical protocols and to demonstrate the feasibility of preclinical trials. Nonhuman primates are ideally suited to achieve these goals. The efforts toward developing a nonhuman primate diabetic model with demonstrable insulin dependence are discussed and include pancreatic and islet transplant trials to reverse the diabetic state and achieve insulin independence. Also described are the various protocols that have been tested in primates to circumvent immunosuppression by using tolerance induction strategies in lieu of immunosuppression, thus exploring the field of donor-specific tolerance that extends beyond islet transplantation.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".