Islet Remodeling in Female Mice with Spontaneous Autoimmune and Streptozotocin-Induced Diabetes
Bibliographic record
Abstract
Islet alpha- and delta-cells are spared autoimmune destruction directed at beta-cells in type 1 diabetes resulting in an apparent increase of non-beta endocrine cells in the islet core. We determined how islet remodeling in autoimmune diabetes compares to streptozotocin (STZ)-induced diabetes. Islet cell mass, proliferation, and immune cell infiltration in pancreas sections from diabetic NOD mice and mice with STZ-induced diabetes was assessed using quantitative image analysis. Serial sections were stained for various beta-cell markers and Ngn3, typically restricted to embryonic tissue, was only upregulated in diabetic NOD mouse islets. Serum levels of insulin, glucagon and GLP-1 were measured to compare hormone levels with respect to disease state. Total pancreatic alpha-cell mass did not change as autoimmune diabetes developed in NOD mice despite the proportion of islet area comprised of alpha- and delta-cells increased. By contrast, alpha- and delta-cell mass was increased in mice with STZ-induced diabetes. Serum levels of glucagon reflected these changes in alpha-cell mass: glucagon levels remained constant in NOD mice over time but increased significantly in STZ-induced diabetes. Increased serum GLP-1 levels were found in both models of diabetes, likely due to alpha-cell expression of prohormone convertase 1/3. Alpha- or delta-cell mass in STZ-diabetic mice did not normalize by replacement of insulin via osmotic mini-pumps or islet transplantation. Hence, the inflammatory milieu in NOD mouse islets may restrict alpha-cell expansion highlighting important differences between these two diabetes models and raising the possibility that increased alpha-cell mass might contribute to the hyperglycemia observed in the STZ model.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".