Adjuvant Immunotherapy Increases β Cell Regenerative Factor <i>Reg2</i> in the Pancreas of Diabetic Mice
Bibliographic record
Abstract
Insulin-producing β cells can partially regenerate in adult pancreatic tissues, both in human and animal models of type 1 diabetes (T1D). Previous studies have shown that treatment with mycobacterial adjuvants such as CFA and bacillus Calmette-Guérin prevents induction and recurrence of T1D in NOD mice with partial recovery of β cell mass. In this study, we investigated factors involved in the regeneration of β cells in the pancreas of NOD mice during diabetes development and after treatment with adjuvants. The Regeneration (Reg) gene family is known to be involved in regeneration of various tissues including β cells. Reg2 expression was found to be upregulated in pancreatic islets both during diabetes development and as a result of adjuvant treatment in diabetic NOD mice and in C57BL/6 mice made diabetic by streptozotocin treatment. The upregulation of Reg2 by adjuvant treatment was independent of signaling through MyD88 and IL-6 because it was not altered in MyD88 or IL-6 knockout mice. We also observed upregulation of Reg2 in the pancreas of diabetic mice undergoing β cell regenerative therapy with exendin-4 or with islet neogenesis-associated protein. Reg2 expression following adjuvant treatment correlated with a reduction in insulitis, an increase in insulin secretion, and an increase in the number of small islets in the pancreas of diabetic NOD mice and with improved glucose tolerance tests in streptozotocin-treated diabetic C57BL/6 mice. In conclusion, adjuvant immunotherapy regulates T1D in diabetic mice and induces Reg2-mediated regeneration of β cells.
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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 | 0.001 | 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) | 0.002 | 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".