Low-grade-/High-grade-Synovialitis

Objective In this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning. Methods Twenty histologic features were assessed in 129 synovial tissue samples (n = 123 RA patients and n = 6 osteoarthritis [OA] patients). Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes. Results Consensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor β, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C‐reactive protein (CRP) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others. Conclusion Gene expression analysis of RA and OA synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate, CRP level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.

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Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data

Orange

Agius

DiCarlo

et al. 2018

Arthritis & Rheumatology

166

159

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Is the bioactivity of induced membranes time dependent?

Geßmann

Rosteius

Baecker

et al. 2021

Eur J Trauma Emerg Surg

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Purpose The induced membrane technique (IMT) is a two-stage surgical procedure for reconstruction of bone defects. Bone grafting (second stage of IMT) is recommend after 4–8 weeks assuming the highest bioactivity of IMs. However, larger studies concerning the biology and maturation of IMs and a potential time dependency of the bioactivity are missing. Therefore, aim of this study was the time-dependent structural and cellular characterization of cement spacer IMs concomitantly to an analysis of membrane bioactivity. Methods IMs from 60 patients (35–82 years) were obtained at different maturation stages (1–16 weeks). IMs were studied by histology and co-culture with mesenchymal stem cells (MSC). IM lysates were analyzed by ELISA and protein microarray. Results Increasing vascularization and fibrosis were found in membranes older than 4 and 7 weeks, respectively. MSC grew out from all membranes and all membranes enhanced proliferation of cultured MSC. Osteocalcin and osteopontin (in membrane lysates or induced in MSC by membrane tissue) were found over all time points without significant differences. In contrast to alkaline phosphatase activity, increasing levels of osteoprotegerin were found in membranes. Conclusion The histological structure of IMs changes during growth and maturation, however, biologically active MSC and factors related to osteogenesis are found over all time points with minor changes. Thus, membranes older than 8 weeks exert regenerative capacities comparable to the younger ones. The postulated narrow time frame of 4–8 weeks until bone grafting can be questioned and surgeons may choose timing for the second operation more independently and based on other clinical factors.

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Applications of molecular pathology in the diagnosis of joint infections

et al. 2009

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The diagnosis of infections in patients with arthritis and/or joint prostheses requires interdisciplinary cooperation and the use of up-to-date methods. Massive bacterial infection can be identified by bacterial culture, and minimal infection can be detected by molecular pathological methods. These processes include specific enrichment of bacterial and fungal DNA, amplification, and identification of the DNA by gel electrophoresis, sequencing techniques, and chip technologies.Anamnesis (enteral or urogenital infection), the clinical picture (oligoarthritis), and further parameters (e.g., HLA B27 status) are important for the diagnosis of reactive arthritis. In many cases of reactive arthritis, molecular methods allow detection of bacterial DNA or RNA in synovial fluid or tissue. Molecular pathological methods allow the fast and reliable differential diagnosis of granulomatous synovialitis without prior cultivation of bacteria or fungi. The development of new molecular pathological methods for detecting bacterial and fungal nucleic acids will increase diagnostic accuracy.

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Low-grade-/High-grade-Synovialitis

Cited by 7 publications

References 17 publications

Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data

Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data

Is the bioactivity of induced membranes time dependent?

Applications of molecular pathology in the diagnosis of joint infections

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