Interferon-β is a key regulator of proinflammatory events in experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
BACKGROUND: Interferon (IFN)-β is an effective therapy for relapsing-remitting multiple sclerosis, yet its mechanism of action remains ill-defined. OBJECTIVES: Our objective was to characterize the role of IFN-β in immune regulation in experimental autoimmune encephalomyelitis (EAE). METHODS: IFN-β(+/+) and IFN-β(-/-) mice were immunized with myelin oligodendrocyte glycoprotein peptide in the presence or absence of IFN-β, to induce EAE. Disease pathogenesis was monitored in the context of incidence, time of onset, clinical score, and immune cell activation in the brains, spleens and lymph nodes of affected mice. RESULTS: Compared with IFN-β(+/+) mice, IFN-β(-/-) mice exhibited an earlier onset and a more rapid progression of EAE, increased numbers of CD11b(+) leukocytes infiltrating affected brains and an increased percentage of Th17 cells in the central nervous system and draining lymph nodes. IFN-β treatment delayed disease onset and reduced disease severity. Ex vivo experiments revealed that the lack of IFN-β results in enhanced generation of autoreactive T cells, a likely consequence of the absence of IFN-β-regulated events in both the CD4(+) T cells and antigen-presenting dendritic cells. Gene expression analysis of IFN-β-treated bone marrow macrophages (CD11b(+)) identified modulation of genes affecting T cell proliferation and Th17 differentiation. CONCLUSIONS: We conclude that IFN-β acts to suppress the generation of autoimmune-inducing Th17 cells during the development of disease as well as modulating pro-inflammatory mediators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".