IL-23 Is Critical in the Induction but Not in the Effector Phase of Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Experimental autoimmune encephalomyelitis (EAE), a T cell-mediated inflammatory disease of the CNS, is a rodent model of human multiple sclerosis. IL-23 is one of the critical cytokines in EAE development and is currently believed to be involved in the maintenance of encephalitogenic responses during the tissue damage effector phase of the disease. In this study, we show that encephalitogenic T cells from myelin oligodendrocyte glycopeptide (MOG)-immunized wild-type (WT) mice caused indistinguishable disease when adoptively transferred to WT or IL-23-deficient (p19 knockout (KO)) recipient mice, demonstrating that once encephalitogenic cells have been generated, EAE can develop in the complete absence of IL-23. Furthermore, IL-12/23 double-deficient (p35/p19 double KO) recipient mice developed EAE that was indistinguishable from WT recipients, indicating that IL-12 did not compensate for IL-23 deficiency during the effector phase of EAE. In contrast, MOG-specific T cells from p19KO mice induced EAE with delayed onset and much lower severity when transferred to WT recipient mice as compared with the EAE that was induced by cells from WT controls. MOG-specific T cells from p19KO mice were highly deficient in the production of IFN-gamma, IL-17A, and TNF, indicating that IL-23 plays a critical role in development of encephalitogenic T cells and facilitates the development of T cells toward both Th1 and Th17 pathways.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".