Role of IL-22 in tissue regeneration in autoimmunity (P5164)
Bibliographic record
Abstract
Abstract The various cell types in our bodies are constantly regenerating but at a different rate. In autoimmune diseases cells once destroyed can regenerate. However, immune system once activated continues to target and destroy these cells. Thus, tissue regeneration remains a challenge in these diseases. Cytokines play a major role in immune regulation, inflammation, tissue injury and autoimmunity. We have shown that immunostimulation by mycobacterial adjuvants such as BCG vaccine as well as complete Freund’s adjuvant (CFA) can prevent the autoimmune process and can stimulate tissue regeneration. These adjuvants induce regulatory Th17 (Treg17) cells and stimulate expression of Regenerative (Reg) genes such as Reg1 and Reg2 in pancreatic islets. Th17 cells also produce Interleukin-22 (IL-22) that has been shown to stimulate This is probably mediated through STAT3/ERK signaling. Reg gene expression. Blocking of IL-22 prevented the expression of Reg genes in vivo. In this study we explored the cell types that express Reg genes following IL-22 treatment by using RT-PCR analysis and by histological staining. Our hypothesis is that the Reg gene expression drives the islet regeneration following tissue injury by autoimmunity in type 1 diabetes. These approaches offer alternatives to tissue transplantation in autoimmunity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".