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Tetracycline transcriptional silencer (tTS) tightly controls transgene expression in the skeletal muscle: in vivo intramuscular IL-10 DNA electrotransfer application to arthritis

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S1 Available online Autoantibodies and antigens 1 Rheumatoid arthritis — class prediction by autoreactivity profiles R Bergholz1, F Schumann1, S Behrens1, U Ungethüm1, G Valet2, WA Schmidt3, GR Burmester1, JM Engel4, WJ van Venrooij5, G Steiner6, S Bläß1 1Department of Rheumatology & Clinical Immunology, Charité University Clinic, Berlin, Germany 2MPI Biochemistry, Munich, Germany 3Clinic for Rheumatology Berlin Buch, Berlin, Germany 4Rheumaklinik, Bad Liebenwerda, Germany 5Department of Biochemistry, University of Nijmegen, The Netherlands 6Divison of Rhematology, Department Internal Medicine III, Vienna General Hospital, Austria Arthritis Res Ther 2003, 5 (suppl 1):1 Heterogeneity and multifactoriality complicate diagnostics and our understanding of pathogenesis of rheumatoid arthritis (RA). The only accepted serologic parameter (rheumatoid factor [RF]) is not disease specific, nor are any of several novel RA autoantibodies. We aimed at identifying profiles instead of individual autoreactivities allowing for unambiguous prediction of RA. Selected RA autoantigens were tested by ELISA (RF and anti-cyclic citrullinated peptide [anti-CCP]) or Western blot (heavy-chain-binding protein [BiP], heterogeneous ribonucleoprotein particle A2 [RA33/ hnRNP A2], calpastatin and calreticulin). Antibody reactivities were assayed from serum samples of 149 RA patients and 132 patients with other rheumatic diseases and from synovial fluids (SF) (58 RA, 65 non-RA). No single autoreactivity was sufficient for unambiguous prediction of RA. Frequencies of multiparameter profiles consisting of 3, 4, 5 and 6 autoreactivites were determined. Fifteen six-parameter serum profiles were exclusively expressed in RA patients, representing a cumulative sensitivity of 59%. Twelve SF profiles were exclusively expressed in 64% of RA patients. The self-learning classification algorithm CLASSIF1 was capable of accurately predicting RA when these profiles were present. Data

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