Combination Therapy with Glucagon-Like Peptide-1 and Gastrin Induces β-Cell Neogenesis from Pancreatic Duct Cells in Human Islets Transplanted in Immunodeficient Diabetic Mice
Bibliographic record
Abstract
Pancreatic islet transplantation as a treatment for type 1 diabetes is limited by human donor tissue availability. We investigated whether the beta-cell mass in human isolated islets could be expanded by treatments with glucagon-like peptide-1 (GLP-1) and gastrin, peptides reported to stimulate beta-cell growth in mice and rats with deficits in beta-cell mass. Human islets with low endocrine cell purity (7% beta-cells, 4% alpha-cells) and abundant exocrine cells (29% duct cells and 25% acinar cells) were implanted under the renal capsule of nonobese diabetic-severe combined immune deficiency (NOD-scid) mice made diabetic with streptozotocin. The mice were treated with GLP-1 and gastrin, separately and together, daily for 5 weeks. Blood glucose was significantly reduced only in mice implanted with human pancreatic cells and treated with GLP-1 plus gastrin. Correction of hyperglycemia was accompanied by increased insulin content in the human pancreatic cell grafts as well as by increased plasma levels of human C-peptide in the mice. Immunocytochemical examination revealed a fourfold increase in insulin-positive cells in the human pancreatic cell grafts in GLP-1 plus gastrin-treated mice, and most of this increase was accounted for by the appearance of cytokeratin 19-positive pancreatic duct cells expressing insulin. We conclude that combination therapy with GLP-1 and gastrin expands the beta-cell mass in human islets implanted in immunodeficient diabetic mice, largely from pancreatic duct cells associated with the islets, and this is sufficient to ameliorate hyperglycemia in the mice.
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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".