Identification of Diagnostic Urinary Biomarkers for Acute Kidney Injury
Identification of Diagnostic Urinary Biomarkers for Acute Kidney Injury
复制标题
急性肾损伤诊断性尿液生物标志物的鉴定
DOI：
10.2310/jim.0b013e3181d473e7
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发表时间：
2010
影响因子：
2.6
通讯作者：
J. Arthur
中科院分区：
文献类型：
--
作者：
S. Varghese;T. B. Powell;M. Janech;M. Budisavljevic;R. Stanislaus;Jonas S. Almeida;J. Arthur
急性肾损伤（阿基）是住院患者死亡的重要原因。阿基的2种最常见原因是急性肾小管坏死（ATN）和肾前性氮质血症（PRA）。正确诊断该病很重要，但往往很困难。我们通过双向凝胶电泳分析了38例阿基患者的尿蛋白。患者被随机分配到训练集、内部测试集或外部验证集。通过人工神经网络分析斑点丰度，以确定区分ATN和PRA的生物标志物。当训练的神经网络算法针对训练数据进行测试时，它识别了训练集中18名患者中的16名和内部测试集中的所有10名患者的诊断。在新的外部患者集中验证了准确性，其中10名患者中有9名患者的病情得到正确诊断，包括5名ATN患者中的5名和5名PRA患者中的4名。在一个斑点中鉴定出血浆视黄醇结合蛋白，在另一个斑点中鉴定出白蛋白和血浆视黄醇结合蛋白的片段。这些蛋白质是用于阿基诊断测定的候选标志物。
Acute kidney injury (AKI) is an important cause of death among hospitalized patients. The 2 most common causes of AKI are acute tubular necrosis (ATN) and prerenal azotemia (PRA). Appropriate diagnosis of the disease is important but often difficult. We analyzed urine proteins by 2-dimensional gel electrophoresis from 38 patients with AKI. Patients were randomly assigned to a training set, an internal test set, or an external validation set. Spot abundances were analyzed by artificial neural networks to identify biomarkers that differentiate between ATN and PRA. When the trained neural network algorithm was tested against the training data, it identified the diagnosis for 16 of 18 patients in the training set and all 10 patients in the internal test set. The accuracy was validated in the novel external set of patients where conditions of 9 of 10 patients were correctly diagnosed including 5 of 5 with ATN and 4 of 5 with PRA. Plasma retinol-binding protein was identified in 1 spot and a fragment of albumin and plasma retinol-binding protein in the other. These proteins are candidate markers for diagnostic assays of AKI.
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DOI：
10.1097/01.asn.0000088027.54400.c6
发表时间：
2003-10-01
影响因子：
13.6
作者：
Mishra, J;Ma, Q;Devarajan, P
通讯作者：
Devarajan, P
影响因子：
19.6
作者：
Han, WK;Bailly, V;Bonventre, JV
通讯作者：
Bonventre, JV
影响因子：
13.6
作者：
Varghese, Sanju A.;Powell, T. Brian;Arthur, John M.
通讯作者：
Arthur, John M.
DOI：
--
发表时间：
2003
期刊：
--
影响因子：
--
作者：
T. Ichimura;Cheng Chieh Hung;Soon Ae Yang;James L. Stevens;Joseph V. Bonventre
通讯作者：
T. Ichimura;Cheng Chieh Hung;Soon Ae Yang;James L. Stevens;Joseph V. Bonventre
影响因子：
13.2
作者：
Parikh, CR;Jani, A;Edelstein, CL
通讯作者：
Edelstein, CL