Báo cáo y học: "Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học General Psychiatry cung cấp cho các bạn kiến thức về ngành y đề tài: Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept. | Available online http content 10 3 R50 Research article Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept Dirk Koczan1 Susanne Drynda2 Michael Hecker3 Andreas Drynda2 Reinhard Guthke3 Joern Kekow2 and Hans-Juergen Thiesen1 Department of Immunology University of Rostock Schillingallee 70 1 8055 Rostock Germany 2Clinic of Rheumatology University of Magdeburg Sophie-von-Boetticher-StraBe 1 39245 Vogelsang Germany 3Leibnitz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute . BeutenbergstraBe 11a 07745 Jena Germany Corresponding author Hans-Juergen Thiesen Received 26 Oct 2007 Revisions requested 14 Dec 2007 Revisions received 18 Apr 2008 Accepted 2 May 2008 Published 2 May 2008 Arthritis Research Therapy 2008 10 R50 doi ar2419 This article is online at http content 10 3 R50 2008 Koczan et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Open Access Abstract Introduction About 30 of rheumatoid arthritis patients fail to respond adequately to TNFa-blocking therapy. There is a medical and socioeconomic need to identify molecular markers for an early prediction of responders and nonresponders. Methods RNA was extracted from peripheral blood mononuclear cells of 19 rheumatoid arthritis patients before the first application of the TNFa blocker etanercept as well as after 72 hours. Clinical response was assessed over 3 months using the 28-joint-count Disease Activity Score and X-ray scans. Supervised learning methods were applied to Affymetrix Human Genome U133 microarray data analysis to determine highly selective discriminatory .

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