Combination Therapy with a Dipeptidyl Peptidase-4 Inhibitor and a Proton Pump Inhibitor Induces β-Cell Neogenesis from Adult Human Pancreatic Duct Cells Implanted in Immunodeficient Mice
Bibliographic record
Abstract
Combination therapy with a dipeptidyl peptidase-4 inhibitor (DPP-4i) and a proton pump inhibitor (PPI) raises endogenous levels of GLP-1 and gastrin, respectively, and restores pancreatic β-cell mass and normoglycemia in nonobese diabetic (NOD) mice with autoimmune diabetes. The aim of this study was to determine whether a DPP-4i and PPI combination could increase β-cell mass in the adult human pancreas. Pancreatic cells from adult human pancreas donors were implanted in NOD-severe combined immunodeficient (NOD-scid) mice and the mice were treated with a DPP-4i and a PPI for 16 weeks. Human grafts were examined for insulin content and insulin-stained cells. Graft β-cell function was assessed by intravenous glucose tolerance tests (IVGTT) and by glucose control in human cell-engrafted mice treated with streptozotocin (STZ) to delete mouse pancreatic β-cells. Plasma GLP-1 and gastrin levels were raised to two- to threefold in DPP-4i- and PPI-treated mice. Insulin content and insulin-stained cells in human pancreatic cell grafts were increased 9- to 13-fold in DPP-4i and PPI-treated mice and insulin-stained cells were colocalized with pancreatic exocrine duct cells. Plasma human C-peptide responses to IVGTT were significantly higher and STZ-induced hyperglycemia was more completely prevented in DPP-4i- and PPI-treated mice with grafts than in vehicle-treated mice with grafts. In conclusion, DPP-4i and PPI combination therapy raises endogenous levels of GLP-1 and gastrin and greatly expands the functional β-cell mass in adult human pancreatic cells implanted in immunodeficient mice, largely from pancreatic duct cells. This suggests that a DPP-4i and PPI combination treatment may provide a pharmacologic therapy to correct the β-cell deficit in type 1 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 | 0.000 | 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 | 0.001 |
| 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".