Vaccination against IL‐17 suppresses autoimmune arthritis and encephalomyelitis
Bibliographic record
Abstract
Interleukin 17 is a T cell-derived cytokine that induces the release of pro-inflammatory mediators in a wide range of cell types. Recently, a subset of IL-17-producing T helper cells (Th17) distinct from Th1 and Th2 cells has been described, which constitutes a new T cell polarization state. Aberrant Th17 responses and overexpression of IL-17 have been implicated in a number of autoimmune disorders including rheumatoid arthritis and multiple sclerosis. Molecules blocking IL-17 such as IL-17-specific monoclonal antibodies have proved to be effective in ameliorating disease in animal models. Hitherto, active immunization targeting IL-17 is an untried approach. Herein we explore the potential of neutralizing IL-17 by active immunization using virus-like particles conjugated with recombinant IL-17 (IL-17-VLP). Immunization with IL-17-VLP induced high levels of anti-IL-17 antibodies thereby overcoming natural tolerance, even in the absence of added adjuvant. Mice immunized with IL-17-VLP had lower incidence of disease, slower progression to disease and reduced scores of disease severity in both collagen-induced arthritis and experimental autoimmune encephalomyelitis. Active immunization against IL-17 therefore represents a novel therapeutic approach for the treatment of chronic inflammatory diseases.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".