Inflammatory cytokines in the pathogenesis of autoimmune type 1 diabetes (123.21)
Bibliographic record
Abstract
Abstract Inflammatory cytokines are implicated in the pathogenesis of different autoimmune diseases like Type 1 diabetes, Rhumatoid arthritis and inflammatory bowel disease in humans as well as in animal models. IL-21, and IL-15 are pro-inflammatory cytokines and they exert their biological effects at various stages of the immune responses. We have shown previously that IL-21 can synergize with IL-15 or IL-7 to reduce the threshold of TCR signaling, while simultaneously increasing the production of effector cytokines such as TNFα and IFNγ. IL-21 maps to one of the shared autoimmune disease susceptibility loci in humans and mice. In Non Obese Diabetic (NOD) mouse that develops spontaneous autoimmune type 1 diabetes (T1D), IL-21 is a candidate gene in the Idd3 locus. C57BL/6 derived Idd3 allele protects NOD mouse from T1D. As IL-2 also maps to the same locus, the relative contribution of IL-2 and IL-21 to the disease process is not well understood. It has been shown previously that NOD mouse lacking the receptor for IL-21 does not develop autoimmune diabetes. Here we report that the absence of IL-21 protects the 8.3 TCR transgenic NOD mice from diabetes. Similarly we observed that NOD mice that lack the expression of IL-15 show reduced incidence of type 1 diabetes. Our results suggest that IL-21 and IL-15 may be implicated at different stages of disease development in the NOD mouse 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".