Altered molecular pathways and prognostic markers in active systemic juvenile idiopathic arthritis: integrated bioinformatic analysis

Systemic juvenile idiopathic arthritis (SJIA) is a severe childhood-onset inflammatory disease characterized by arthritis accompanied by systemic auto-inflammation and extra-articular symptoms. While recent advances have unraveled a range of risk factors, the pathomechanisms involved in SJIA and potential prognostic markers for treatment success remain partly unknown. In this study, we included 70 active SJIA and 55 healthy control patients from the National Center for Biotechnology Information to analyze for differentially expressed genes (DEGs) using R. Functional enrichment analysis, protein-protein interaction (PPI), and gene module construction were performed for DEGs and hub gene set. We additionally examined immune system cell composition with CIBERSORT and predicted prognostic markers and potential treatment drugs for SJIA. In total, 94 upregulated and 24 downregulated DEGs were identified. Two specific modules of interest and eight hub genes (ARG1, DEFA4, HP, MMP8, MMP9, MPO, OLFM4, PGLYRP1) were screened out. Functional enrichment analysis suggested that complex neutrophil-related functions play a decisive role in the disease pathogenesis. CIBERSORT indicated neutrophils, M0 macrophages, CD8+ T cells, and naïve B cells to be relevant drivers of disease progression. Additionally, we identified TPM2 and GZMB as potential prognostic markers for treatment response to canakinumab. Moreover, sulindac sulfide, (-)-catechin, and phenanthridinone were identified as promising treatment agents. This study provides a new insight into molecular and cellular pathogenesis of active SJIA and highlights potential targets for further research

At-home blood self-sampling in rheumatology: a qualitative study with patients and health care professionals

Abstract Background The goal of the study was to investigate patients’ with systemic rheumatic diseases and healthcare professionals’ experiences and preferences regarding self-sampling of capillary blood in rheumatology care. Methods Patients performed a supervised and consecutive unsupervised capillary blood self-collection using an upper arm based device. Subsequently, patients (n = 15) and their attending health care professionals (n = 5) participated in an explorative, qualitative study using problem-centered, telephone interviews. Interview data were analyzed using structured qualitative content analysis. Results Interviewed patients reported easy application and high usability. Patients and health care professionals alike reported time and cost savings, increased independence and flexibility, improved monitoring and reduction of risk of infection during Covid-19 as benefits. Reported drawbacks include limited blood volume, limited usability in case of functional restrictions, and environmental concerns. Older, immobile patients with long journeys to traditional blood collection sites and young patients with little time to spare for traditional blood collection appointments could be user groups, likely to benefit from self-sampling services. Conclusions At-home blood self-sampling could effectively complement current rheumatology telehealth care. Appropriateness and value of this service needs to be carefully discussed with patients on an individual basis. Trial Registration WHO International Clinical Trials Registry: DRKS00024925. Registered on 15/04/2021

An AI-Powered Clinical Decision Support System to Predict Flares in Rheumatoid Arthritis: A Pilot Study

Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS—Rheuma Care Manager (RCM)—including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients’ flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool (20% to 12% for half dosage flare prediction), and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence

CD200⁺ fibroblasts form a pro-resolving mesenchymal network in arthritis

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200–CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation. </p