Combination Therapy With Epidermal Growth Factor and Gastrin Increases β-Cell Mass and Reverses Hyperglycemia in Diabetic NOD Mice
Bibliographic record
Abstract
Combination therapy with epidermal growth factor (EGF) and gastrin induces beta-cell regeneration in rodents with chemically induced diabetes. We investigated whether EGF plus gastrin could correct hyperglycemia in NOD mice with autoimmune diabetes. Combined treatment with EGF (1 mug/kg) and gastrin (3 mug/kg) for 2 weeks restored normoglycemia after diabetes onset in NOD mice, whereas EGF or gastrin alone did not. Fasting blood glucose remained normal (3.5-6.5 mmol/l) or mildly elevated (<11 mmol/l) in five of six mice (83%) for 10 weeks after EGF plus gastrin treatment was stopped, whereas all mice treated with vehicle or EGF or gastrin alone became severely hyperglycemic (12-35 mmol/l). Pancreatic beta-cell mass was increased threefold and insulin content was increased eightfold in mice treated with EGF plus gastrin compared with pretreatment values. The correction of hyperglycemia correlated significantly with increases in pancreatic beta-cell mass and insulin content. In addition, splenic cells from mice treated with EGF plus gastrin delayed diabetes induction by adoptive transfer of diabetogenic cells into immunodeficient NOD-scid mice, suggesting the induction of immunoregulatory cells in NOD mice treated with EGF plus gastrin. We conclude that a short course of combined EGF and gastrin therapy increases pancreatic beta-cell mass and reverses hyperglycemia in acutely diabetic NOD mice; the impact of this combined therapy may result from the effects of EGF and gastrin on beta-cells, immune cells, or both.
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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".