The involvement of interleukin-22 in the expression of pancreatic beta cell regenerative Reg genes
Bibliographic record
Abstract
BACKGROUND: In Type 1 diabetes, the insulin-producing β-cells within the pancreatic islets of Langerhans are destroyed. We showed previously that immunotherapy with Bacillus Calmette-Guerin (BCG) or complete Freund's adjuvant (CFA) of non-obese diabetic (NOD) mice can prevent disease process and pancreatic β-cell loss. This was associated with increased islet Regenerating (Reg) genes expression, and elevated IL-22-producing Th17 T-cells in the pancreas. RESULTS: We hypothesized that IL-22 was responsible for the increased Reg gene expression in the pancreas. We therefore quantified the Reg1, Reg2, and Reg3δ (INGAP) mRNA expression in isolated pre-diabetic NOD islets treated with IL-22. We measured IL-22, and IL-22 receptor(R)-α mRNA expression in the pancreas and spleen of pre-diabetic and diabetic NOD mice. Our results showed: 1) Reg1 and Reg2 mRNA abundance to be significantly increased in IL-22-treated islets in vitro; 2) IL-22 mRNA expression in the pre-diabetic mouse pancreas increased with time following CFA treatment; 3) a reduced expression of IL-22Rα following CFA treatment; 4) a down-regulation in Reg1 and Reg2 mRNA expression in the pancreas of pre-diabetic mice injected with an IL-22 neutralizing antibody; and 5) an increased islet β-cell DNA synthesis in vitro in the presence of IL-22. CONCLUSIONS: We conclude that IL-22 may contribute to the regeneration of β-cells by up-regulating Regenerating Reg1 and Reg2 genes in the islets.
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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 | 0.000 |
| 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) | 0.001 | 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".