IL‐33 attenuates the development of experimental autoimmune uveitis
Bibliographic record
Abstract
Interleukin-33 (IL-33) is associated with several important immune-mediated disorders. However, its role in uveitis, an important eye inflammatory disease, is unknown. Here, we investigated the function of IL-33 in the development of experimental autoimmune uveitis (EAU). IL-33 and IL-33 receptor (ST2) were expressed in murine retinal pigment epithelial (RPE) cells in culture, and IL-33 increased the expression of Il33 and Mcp1 mRNA in RPE cells. In situ, IL-33 was highly expressed in the inner nuclear cells of the retina of naïve mice, and its expression was elevated in EAU mice. ST2-deficient mice developed exacerbated EAU compared with WT mice, and administration of IL-33 to WT mice significantly reduced EAU severity. The attenuated EAU in IL-33-treated mice was accompanied by decreased frequency of IFN-γ+ and IL-17(+) CD4+ T cells and reduced IFN-γ and IL-17 production but with increased frequency of IL-5(+) and IL-4(+) CD4 T cells and IL-5 production in the draining lymph node and spleen. Macrophages from the IL-33-treated mice show a significantly higher polarization toward an alternatively activated macrophage phenotype. Our results therefore demonstrate that the endogenous IL-33/ST2 pathway plays an important role in EAU, and suggest that IL-33 represents a potential option for treatment of uveitis.
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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) | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| 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".