Dipeptidyl Peptidase IV Inhibition With MK0431 Improves Islet Graft Survival in Diabetic NOD Mice Partially via T-Cell Modulation
Bibliographic record
Abstract
OBJECTIVE: The endopeptidase dipeptidyl peptidase-IV (DPP-IV) has been shown to NH2-terminally truncate incretin hormones, glucose-dependent insulinotropic polypeptide, and glucagon-like peptide-1, thus ablating their ability to potentiate glucose-stimulated insulin secretion. Increasing the circulating levels of incretins through administration of DPP-IV inhibitors has therefore been introduced as a therapeutic approach for the treatment of type 2 diabetes. DPP-IV inhibitor treatment has also been shown to preserve islet mass in rodent models of type 1 diabetes. The current study was initiated to define the effects of the DPP-IV inhibitor sitagliptin (MK0431) on transplanted islet survival in nonobese diabetic (NOD) mice, an autoimmune type 1 diabetes model. RESEARCH DESIGN AND METHODS: Effects of MK0431 on islet graft survival in diabetic NOD mice were determined with metabolic studies and micropositron emission tomography imaging, and its underlying molecular mechanisms were assessed. RESULTS: Treatment of NOD mice with MK0431 before and after islet transplantation resulted in prolongation of islet graft survival, whereas treatment after transplantation alone resulted in small beneficial effects compared with nontreated controls. Subsequent studies demonstrated that MK0431 pretreatment resulted in decreased insulitis in diabetic NOD mice and reduced in vitro migration of isolated splenic CD4+ T-cells. Furthermore, in vitro treatment of splenic CD4+ T-cells with DPP-IV resulted in increased migration and activation of protein kinase A (PKA) and Rac1. CONCLUSIONS: Treatment with MK0431 therefore reduced the effect of autoimmunity on graft survival partially by decreasing the homing of CD4+ T-cells into pancreatic beta-cells through a pathway involving cAMP/PKA/Rac1 activation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".