Background: Autoantibodies such as rheumatoid factor (RF) and anticitrullinated protein antibodies can be detected in rheumatoid arthritis (RA) sera. Objective: To determine the diagnostic values of RF, anticitrullinated protein antibodies, and the shared epitope (SE), and their associations with radiological progression rates and extra-articular manifestations. Methods: Population 1 consisted of sera from 315 patients, consecutively sent for detection of anticitrullinated protein antibodies, of which 264 were used to determine the sensitivity and specificity of RF and of antibodies against three synthetic citrullinated peptides: peptide A (pepA), peptide B (pepB), and CCP2. Population 2 consisted of sera from 180 longstanding RA patients and was used to determine associations of RA associated antibodies and the SE with radiological progression rates and extraarticular manifestations. Antibodies to pepA and pepB were detected by line immunoassay, and antibodies to CCP2 by ELISA. HLA Class II typing was performed by LiPA. Results: In population 1, we defined adapted cut offs corresponding to a specificity of >98.5%. This yielded the following sensitivities: RF 12.8%; anti-pepA antibodies 63.6%; anti-pepB antibodies 54.2%; and anti-CCP2 antibodies 73.7%. In population 2, significant differences in radiological progression rates were found between positive and negative patients for different RA antibodies and the SE. RF, but not anticitrullinated protein antibodies or the SE, were more frequent in patients with extra-articular manifestations. Conclusion: A valid comparison of RA associated antibodies shows superior sensitivity of the anticitrullinated protein antibodies compared with RF. The presence of RA associated antibodies and the SE are indicative for poorer radiological outcome, and presence of extra-articular manifestations is associated with RF but not with anticitrullinated protein antibodies.

Objective. To explore prospectively the value of synovial histopathology in comparison with the value of classic parameters for diagnostic classification of spondylarthropathy (SpA) and rheumatoid arthritis (RA) in patients with an atypical disease presentation.Methods. Synovial biopsy samples were obtained from 154 consecutive patients presenting for diagnostic evaluation; 67 patients fulfilled the classification criteria for RA, SpA, or other well-defined disease at the time of arthroscopy (cohort 1), and an additional 53 patients were classified after a full diagnostic reevaluation at 6 months (cohort 2). Synovial parameters with diagnostic value were identified in cohort 1 and were compared prospectively with classic diagnostic parameters in cohort 2.Results. Staining with anticitrulline, staining with monoclonal antibody 12A (recognizing HLA-DR shared epitope-human cartilage glycoprotein 39 263-275 complexes), and crystal deposition had positive predictive values (PPVs) for diagnosis of >90% in patients with an atypical disease presentation (cohort 2). Using these 3 parameters, a diagnosis was predicted by synovial histopathology in 39.6% of cohort 2 patients and turned out to be correct in 90.5% of these patients at 6 months of followup. Using a multiparameter model rather than single histopathologic parameters, even better results were obtained, with a diagnostic prediction in 79.2% of samples and a PPV of 81.0%. In comparison, a similar multiparameter model using classic diagnostic criteria rather than synovial histopathology performed poorly in cohort 2; the sensitivity was 56.6% and the PPV was 73.3%, with an inferior capacity to predict SpA. Especially for the presence of crystals and anticitrulline staining, the analysis of synovial tissue had a clear added value to the analysis of synovial fluid or serum in patients with an atypical presentation.Conclusion. This proof-of-concept study indicates that synovial histopathology can contribute to the multiparametric diagnostic classification of inflammatory arthritis in patients with an atypical presentation.

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

This paper presents an overview of model-based (Nonlinear Model Predictive Control, Iterative Learning Control and Iterative Optimization) and model-free (Genetic-based Machine Learning and Reinforcement Learning) learning strategies for the control of wet-clutches. The benefits and drawbacks of the different methodologies are discussed, and illustrated by an experimental validation on a test bench containing wet-clutches. In general, all strategies yield a good engagement quality once they converge. The model-based strategies seems most suited for an online application, because they are inherently more robust and require a shorter convergence time. The model-free strategies meanwhile seem most suited to offline calibration procedures for complex systems where heuristic tuning rules no longer suffice.

This study is based on an expanded access program in which 511 patients suffering from active refractory rheumatoid arthritis (RA) were treated with intravenous infusions of infliximab (3 mg/kg+methotrexate (MTX)) at weeks 0, 2, 6 and every 8 weeks thereafter. At week 22, 474 patients were still in follow-up, of whom 102 (21.5%), who were not optimally responding to treatment, received a dose increase from week 30 onward. We aimed to build a model to discriminate the decision to give a dose increase. This decision was based on the treating rheumatologist's clinical judgment and therefore can be considered as a clinical measure of insufficient response. Different single and composite measures at weeks 0, 6, 14 and 22, and their differences over time were taken into account for the model building. Ranking of the continuous variables based on areas under the curve of receiver-operating characteristic (ROC) curve analysis, displayed the momentary DAS28 (Disease Activity Score including a 28-joint count) as the most important discriminating variable. Subsequently, we proved that the response scores and the changes over time were less important than the momentary evaluations to discriminate the physician's decision. The final model we thus obtained was a model with only slightly better discriminative characteristics than the DAS28. Finally, we fitted a discriminant function using the single variables of the DAS28. This displayed similar scores and coefficients as the DAS28. In conclusion, we evaluated different variables and models to discriminate the treating rheumatologist's decision to increase the dose of infliximab (+MTX), which indicates an insufficient response to infliximab at 3 mg/kg in patients with RA. We proved that the momentary DAS28 score correlates best with this decision and demonstrated the robustness of the score and the coefficients of the DAS28 in a cohort of RA patients under infliximab therapy.