Differential type I and type III interferon expression profiles in rheumatoid and juvenile idiopathic arthritis
Bibliographic record
Abstract
Background The role of type I and type III interferons (IFNs) in rheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA) is still poorly understood. The objective of this study was to examine the hypothesis that IFN expression profiles in the peripheral blood differ between subsets of arthritic subjects. Multiple type I and type III IFNs were examined in patients with RA and JIA, as well as among subtypes of JIA. Methods Treatment-naïve RA and JIA patients were enrolled. Droplet digital PCR was used to measure the expression of type I, II, and III interferons in blood and synovial fluid leukocytes. Dendritic cell subsets were isolated from synovial fluid to examine IFN expression in each subset. Additionally, synovial mononuclear cells and JIA-derived fibroblast-like synoviocytes were stimulated with TNF, IFNγ, and poly(I:C) to examine inducible IFN expression. Results The predominant type I IFN gene expressed by blood leukocytes was IFNκ and was significantly lower in RA than JIA and controls. Oligoarticular and psoriatic JIA subgroups showed higher IFNκ expression compared to polyarticular JIA and RA. JIA synovial fluid leukocytes expressed abundant IFNγ and type III IFNs (IFNλ1, IFNλ3), with distinct dendritic cell subset contributions. JIA fibroblast-like synoviocytes produced IFNβ, IFNλ1, and IFNλ2 mRNA upon poly(I:C) stimulation. Conclusion This study revealed differences in IFN expression patterns in RA and JIA, with notable differences between JIA subtypes. The expression levels of IFNκ, IFNγ, IFNλ1 and IFNλ3 in JIA suggest specific roles in disease pathology, influenced by disease subtype and joint microenvironment. This study contributes to understanding IFN-mediated mechanisms in arthritis, potentially guiding targeted therapeutic strategies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".