Objective
To identify molecular features associated with the development of rheumatoid arthritis (RA), to understand the pathophysiology of preclinical development of RA, and to assign predictive ...biomarkers.
Methods
The study group comprised 109 anti–citrullinated protein antibody (ACPA)– and/or rheumatoid factor–positive patients with arthralgia who did not have arthritis but were at risk of RA, and 25 patients with RA. The gene expression profiles of blood samples obtained from these patients were determined by DNA microarray analysis and quantitative polymerase chain reaction.
Results
In 20 of the 109 patients with arthralgia who were at risk of RA, arthritis developed after a median of 7 months. Gene expression profiling of blood cells revealed heterogeneity among the at‐risk patients, based on differential expression of immune‐related genes. This report is the first to describe gene signatures relevant to the development of arthritis. Signatures significantly associated with arthritis development were involved in interferon (IFN)–mediated immunity, hematopoiesis, and chemokine/cytokine activity. Logistic regression analysis revealed that the odds ratio (OR) for developing arthritis within 12 months was 21.0 (95% confidence interval 95% CI 2.8–156.1 P = 0.003) for the subgroup characterized by increased expression of genes involved in IFN‐mediated immunity and/or cytokine/chemokine‐activity. Genes involved in B cell immunology were associated with protection against progression to arthritis (OR 0.38, 95% CI 0.21–0.70 P = 0.002). These processes were reminiscent of those in patients with RA, implying that the preclinical phase of disease is associated with features of established disease.
Conclusion
The results of this study indicate that IFN‐mediated immunity, hematopoiesis, and cell trafficking specify processes relevant to the progression of arthritis independent of ACPA positivity. These findings strongly suggest that certain gene signatures have value for predicting the progression to arthritis, which will pave the way to preventive medicine.
The EC-funded thematic network `Speciation 21' links scientists in analytical chemistry working in method development for the chemical speciation of trace elements, and potential users from industry ...and representatives of legislative agencies, in the field of environment, food and occupational health and hygiene. The network covers a number of important issues including organotin compounds, chromium and nickel species, chemical characterisation of environmental and industrial particulate samples, risk assessment, selenium and a series of other essential and toxic elements in food, as well as the influence of packing materials. Once the analytical methodology for the measurement of the trace element species has been optimised, the importance of trace element speciation will grow enormously. Food sciences, material sciences, medicine and occupational health, environmental sciences and related fields will all profit. The network will also substantially promote the dissemination of knowledge through the member states.
Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, finding the optimal radiomics method out of the wide range of available ...options has to be done manually through a heuristic trial-and-error process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms for each component. To optimize the workflow per application, we employ automated machine learning using a random search and ensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1) liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77); 5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis (0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer's disease (0.87); and 12) head and neck cancer (0.84). We show that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performs similar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automatically optimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications. To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework, and the code to reproduce this study.
Gastrointestinal stromal cell tumours (GIST) of the small intestine are rare malignancies. Recently, an association of Epstein-Barr virus (EBV) with malignant stromal cell tumour in young people with ...AIDS and past EBV infection has been described. We describe a 33-year-old heterosexual male with asymptomatic human immunodeficiency virus (HIV) infection who had had an EBV infection in the past and who presented with an EBV-negative GIST. The association between EBV and malignant stromal cell tumours in young people with AIDS could not be reconfirmed in our adult patient. The relationship between EBV and malignant stromal cell tumours in AIDS patients and the possible pathogenetic role of EBV remains to be established, at least in adults.
Objective
Rheumatoid arthritis (RA) is a heterogeneous disease that exhibits a complex genetic component. Previous RA genome scans confirmed the involvement of the HLA region and generated data on ...suggestive signals at non‐HLA regions, albeit with few overlaps in findings between studies. The present study was undertaken to detect potential RA gene regions and to estimate the number of true RA gene regions, taking into account the heterogeneity of RA, through performance of a dense genome scan.
Methods
In a study of 88 French Caucasian families (105 RA sibpairs), 1,088 microsatellite markers were genotyped (3.3‐cM genome scan), and a multipoint model‐free linkage analysis was performed. The statistical assessment of the results relied on 10,000 computer simulations. A covariate‐based multipoint model‐free linkage analysis was performed on the locations of regions with suggestive evidence for linkage.
Results
Involvement of the HLA region was strongly confirmed (P = 6 × 10−5), and 19 non‐HLA regions showed suggestive evidence for linkage (P < 0.05); 9 of these overlapped with regions suggested in other published RA genome scans. A routine 12‐cM genome scan with the same families would have detected only 7 of the 19 regions, including only 4 of the 9 overlapping regions. From the 10,000 computer simulations, we estimated that 8 ± 4 regions (mean ± SD) were true‐positives. RA covariate–based analysis provided additional linkage evidence for 3 regions, with age at disease onset, erosions, and HLA–DRB1 shared epitope as covariates.
Conclusion
The results of this study provide evidence of 19 non‐HLA RA gene regions, with an estimate of 8 ± 4 as true‐positives, and provide additional evidence for 3 regions from covariate‐based analysis.
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