Combination Therapy With Glucagon-Like Peptide-1 and Gastrin Restores Normoglycemia in Diabetic NOD Mice
Bibliographic record
Abstract
OBJECTIVE: Glucagon-like peptide-1 (GLP-1) and gastrin promote pancreatic beta-cell function, survival, and growth. Here, we investigated whether GLP-1 and gastrin can restore the beta-cell mass and reverse hyperglycemia in NOD mice with autoimmune diabetes. RESEARCH DESIGN AND METHODS: Acutely diabetic NOD mice were treated with GLP-1 and gastrin, separately or together, twice daily for 3 weeks. Blood glucose was measured weekly and for a further 5 weeks after treatments, after which pancreatic insulin content and beta-cell mass, proliferation, neogenesis, and apoptosis were measured. Insulin autoantibodies were measured, and adoptive transfer of diabetes and syngeneic islet transplant studies were done to evaluate the effects of GLP-1 and gastrin treatment on autoimmunity. RESULTS: Combination therapy with GLP-1 and gastrin, but not with GLP-1 or gastrin alone, restored normoglycemia in diabetic NOD mice. The GLP-1 and gastrin combination increased pancreatic insulin content, beta-cell mass, and insulin-positive cells in pancreatic ducts, and beta-cell apoptosis was decreased. Insulin autoantibodies were reduced in GLP-1- and gastrin-treated NOD mice, and splenocytes from these mice delayed adoptive transfer of diabetes in NOD-scid mice. Syngeneic islet grafts in GLP-1 -and gastrin-treated NOD mice were infiltrated by leukocytes with a shift in cytokine expression from interferon-gamma to transforming growth factor-beta1, and beta-cells were protected from apoptosis. CONCLUSIONS: Combination therapy with GLP-1 and gastrin restores normoglycemia in diabetic NOD mice by increasing the pancreatic beta-cell mass and downregulating the autoimmune response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".