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10.1038/pr.2015.137 http://scihub22266oqcxt.onion/10.1038/pr.2015.137 C4628575!4628575!26237629     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       Pediatr+Res 2015 ; 78 (5): 547-53 Nephropedia Template TP {{tp|p=26237629|t=2015. Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease |pdf=|usr=}}{{26237629}} gab.com Text Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease JO: Pediatr Res http://www.kidney.de/pget.php?&tw=1&pmid=26237629 #FOAvip Background: As Kawasaki disease (KD) shares many clinical features with other more common febrile illnesses and misdiagnosis, leading to a delay in treatment, increases the risk of coronary artery damage, a diagnostic test for KD is urgently needed. We sought to develop a panel of biomarkers that could distinguish between acute KD patients and febrile controls (FC) with sufficient accuracy to be clinically useful. Methods: Plasma samples were collected from three independent cohorts of FC and acute KD patients who met the American Heart Association definition for KD and presented within the first 10 days of fever. The levels of 88 biomarkers associated with inflammation were assessed by Luminex bead technology. Unsupervised clustering followed by supervised clustering using a Random Forest model was used to find a panel of candidate biomarkers. Results: A panel of biomarkers commonly available in the hospital laboratory (absolute neutrophil count, erythrocyte sedimentation rate, alanine aminotransferase, gamma glutamyl transferase, concentrations of alpha-1-antitrypsin, C-reactive protein, and fibrinogen, and platelet count) accurately diagnosed 81 to 96% of KD patients in a series of three independent cohorts. Conclusions: After prospective validation, this 8-biomarker panel may improve the recognition of KD. Twit Text FOAVip Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease ????Pediatr Res http://www.kidney.de/pget.php?&tw=1&pmid=26237629 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26237629 #FOAvip English Wikipedia <ref>{{Cite pmid|26237629}}</ref> Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease #MMPMID26237629 Tremoulet AH; Dutkowski J; Sato Y; Kanegaye JT; Ling XB; Burns JC Pediatr Res 2015[Nov]; 78 (5): 547-53 PMID26237629show ga Background: As Kawasaki disease (KD) shares many clinical features with other more common febrile illnesses and misdiagnosis, leading to a delay in treatment, increases the risk of coronary artery damage, a diagnostic test for KD is urgently needed. We sought to develop a panel of biomarkers that could distinguish between acute KD patients and febrile controls (FC) with sufficient accuracy to be clinically useful. Methods: Plasma samples were collected from three independent cohorts of FC and acute KD patients who met the American Heart Association definition for KD and presented within the first 10 days of fever. The levels of 88 biomarkers associated with inflammation were assessed by Luminex bead technology. Unsupervised clustering followed by supervised clustering using a Random Forest model was used to find a panel of candidate biomarkers. Results: A panel of biomarkers commonly available in the hospital laboratory (absolute neutrophil count, erythrocyte sedimentation rate, alanine aminotransferase, gamma glutamyl transferase, concentrations of alpha-1-antitrypsin, C-reactive protein, and fibrinogen, and platelet count) accurately diagnosed 81 to 96% of KD patients in a series of three independent cohorts. Conclusions: After prospective validation, this 8-biomarker panel may improve the recognition of KD. äNovel data-mining approach identifies biomarkers for diagnosis of Kawa(epmc).pdf DeepDyve Pubget Overpricing