Serum interleukin-38 is paradoxically elevated in rheumatoid arthritis and associated with disease activity and treatment intensity: a cross-sectional study
Oct 2026· Egyptian Rheumatology and Rehabilitation· Vol 53· 0 citations· 31 references
Rheumatoid Arthritis Research and Therapies
Abstract
Interleukin-38 (IL-38), an anti-inflammatory IL-1 superfamily member, is paradoxically elevated in Rheumatoid Arthritis (RA). Whether this reflects pathogenic involvement or compensatory counter-regulation remains unresolved. Co-measuring Protein Tyrosine Phosphatase Non-Receptor (PTPN22) expression offers a unique opportunity to determine whether IL-38 operates within the upstream molecular architecture of RA or merely co-varies with downstream inflammation. This cross-sectional study enrolled 140 individuals (70 RA, 70 controls) from rheumatology centers in Babylon, Iraq. Serum IL-38, Interleukin-6 (IL-6), Interleukin-17 A (IL-17 A), and tumor necrosis factor alpha (TNF-α) were quantified by sandwich enzyme linked immunosorbent assay (ELISA); (PTPN22) and Human Leukocyte Antigen DRB1 (HLA-DRB1) were quantified by quantitative real-time polymerase chain reaction (qRT-PCR) and expressed as relative fold change using the 2 − ΔΔCt method. Spearman correlation, Kruskal–Wallis with Dunn’s post hoc, and multivariable linear regression were applied. IL-38 was significantly elevated in RA (104.47 ± 37.82 pg/mL) versus controls (58.60 ± 16.73 pg/mL; p < 0.001), correlating strongly with DAS28-ESR (ρ = +0.722), CRP (ρ = +0.661), IL-6 (ρ = +0.587), and IL-17 A (ρ = +0.543). It showed a strong inverse correlation with PTPN22 (ρ = −0.664) but null correlation with HLA-DRB1 (ρ = −0.001). IL-38 was lowest in biologic-treated patients and highest in newly diagnosed cases (H = 39.56, p < 0.001) and remained independently significant in multivariable regression (adjusted β = 0.005, p = 0.025; multivariable R² = 0.818). IL-38 is associated with disease activity, shows no association with HLA-DRB1 expression, and is lower among patients receiving more advanced pharmacological therapy — identifying it as a candidate pharmacodynamic biomarker in RA.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.