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10.1186/s12920-015-0100-6 http://scihub22266oqcxt.onion/10.1186/s12920-015-0100-6 C4455917!4455917!26036272     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Genomics 2015 ; 8 (ä): ä Nephropedia Template TP {{tp|p=26036272|t=2015. Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis |pdf=|usr=}}{{26036272}} gab.com Text Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis JO: BMC Med Genomics http://www.kidney.de/pget.php?&tw=1&pmid=26036272 #FOAvip Background: Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease. Methods: We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n?=?116?=?44?+?15?+?30?+?27) were treated with infliximab, and their response assessed 14?16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression. Results: The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment. Conclusions: We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use. Electronic supplementary material: The online version of this article (doi:10.1186/s12920-015-0100-6) contains supplementary material, which is available to authorized users. Twit Text FOAVip Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis ????BMC Med Genomics http://www.kidney.de/pget.php?&tw=1&pmid=26036272 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26036272 #FOAvip English Wikipedia <ref>{{Cite pmid|26036272}}</ref> Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis #MMPMID26036272 Thomson TM; Lescarbeau RM; Drubin DA; Laifenfeld D; de Graaf D; Fryburg DA; Littman B; Deehan R; Van Hooser A BMC Med Genomics 2015[]; 8 (ä): ä PMID26036272show ga Background: Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease. Methods: We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n?=?116?=?44?+?15?+?30?+?27) were treated with infliximab, and their response assessed 14?16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression. Results: The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment. Conclusions: We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use. Electronic supplementary material: The online version of this article (doi:10.1186/s12920-015-0100-6) contains supplementary material, which is available to authorized users. äBlood-based identification of non-responders to anti-TNF therapy in rh(epmc).pdf DeepDyve Pubget Overpricing