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10.1097/TP.0000000000000943 http://scihub22266oqcxt.onion/10.1097/TP.0000000000000943 C4826324!4826324!26447506     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Transplantation 2016 ; 100 (7): 1405-14 Nephropedia Template TP {{tp|p=26447506|t=2016. THE USE OF GENOMICS AND PATHWAY ANALYSIS IN OUR UNDERSTANDING AND PREDICTION OF CLINICAL RENAL TRANSPLANT INJURY |pdf=|usr=}}{{26447506}} gab.com Text THE USE OF GENOMICS AND PATHWAY ANALYSIS IN OUR UNDERSTANDING AND PREDICTION OF CLINICAL RENAL TRANSPLANT INJURY JO: Transplantation http://www.kidney.de/pget.php?&tw=1&pmid=26447506 #FOAvip The development and application of high-throughput molecular profiling have transformed the study of human diseases. The problem of handling large, complex datasets has been facilitated by advances in complex computational analysis. In this review the recent literature regarding the application of transcriptional genomic information to renal transplantation, with specific reference to acute rejection, acute kidney injury in allografts, chronic allograft injury and tolerance is discussed, as is the current published data regarding other ?omics? strategies- proteomics, metabolomics and the micro-RNA transcriptome. These data have shed new light on our understanding of the pathogenesis of specific disease conditions following renal transplantation but their utility as a biomarker of disease has been hampered by study design and sample size. This review aims to highlight the opportunities and obstacles that exist with genomics and other related technologies in order to better understand and predict renal allograft outcome. Twit Text FOAVip THE USE OF GENOMICS AND PATHWAY ANALYSIS IN OUR UNDERSTANDING AND PREDICTION OF CLINICAL RENAL TRANSPLANT INJURY ????Transplantation http://www.kidney.de/pget.php?&tw=1&pmid=26447506 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26447506 #FOAvip English Wikipedia <ref>{{Cite pmid|26447506}}</ref> THE USE OF GENOMICS AND PATHWAY ANALYSIS IN OUR UNDERSTANDING AND PREDICTION OF CLINICAL RENAL TRANSPLANT INJURY #MMPMID26447506 Menon MC; Keung KL; Murphy B; O?Connell PJ Transplantation 2016[Jul]; 100 (7): 1405-14 PMID26447506show ga The development and application of high-throughput molecular profiling have transformed the study of human diseases. The problem of handling large, complex datasets has been facilitated by advances in complex computational analysis. In this review the recent literature regarding the application of transcriptional genomic information to renal transplantation, with specific reference to acute rejection, acute kidney injury in allografts, chronic allograft injury and tolerance is discussed, as is the current published data regarding other ?omics? strategies- proteomics, metabolomics and the micro-RNA transcriptome. These data have shed new light on our understanding of the pathogenesis of specific disease conditions following renal transplantation but their utility as a biomarker of disease has been hampered by study design and sample size. This review aims to highlight the opportunities and obstacles that exist with genomics and other related technologies in order to better understand and predict renal allograft outcome. äTHE USE OF GENOMICS AND PATHWAY ANALYSIS IN OUR UNDERSTANDING AND PRED(epmc).pdf DeepDyve Pubget Overpricing