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Detection of global glycosylation changes of serum proteins in type 1 diabetes using a lectin panel and multivariate data analysis

机译:使用凝集素面板和多元数据分析检测1型糖尿病患者血清蛋白的总体糖基化变化

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Global glycosylation changes of serum proteins in type 1 diabetic patients have in this paper been investigated based on the interaction of the saccharide moiety of serum proteins with different lectins. Lectins are proteins, which bind carbohydrates specifically and reversibly. Panels with lectins of various carbohydrate specificities were immobilized on gold surfaces. Sera from healthy individuals, newly diagnosed type 1 diabetes patients and type 1 diabetes patients having had the disease for 4-6 years, respectively, were applied to the lectin panel. The biorecognition was evaluated with null ellipsometry. Data obtained were related to an internal standard of lactoferrin. Multivariate data analysis (MVDA) techniques were used to analyze data. Principal component analysis showed that the lectin panel enabled discrimination between sera from the three different above-mentioned groups. Using an artificial neuronal net (ANN), it was possible to correctly categorize unknown serum samples into one of the three groups. (C) 2008 Elsevier B.V. All rights reserved
机译:本文基于1型糖尿病患者血清蛋白糖基部分与不同凝集素的相互作用研究了血清蛋白的总糖基化变化。凝集素是蛋白质,可特异性和可逆地结合碳水化合物。将具有各种碳水化合物特异性的凝集素的板固定在金表面上。将来自健康个体,新诊断的1型糖尿病患者和患有该疾病4-6年的1型糖尿病患者的血清分别应用于凝集素组。用零椭偏仪评估生物识别。获得的数据与乳铁蛋白的内标有关。多元数据分析(MVDA)技术用于分析数据。主成分分析表明,凝集素检测组能够区分上述三个不同组的血清。使用人工神经网络(ANN),可以将未知血清样本正确分类为三组之一。 (C)2008 Elsevier B.V.保留所有权利

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