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A new approach based on the serological tests for the diagnosis of tuberculosis in cattle

机译：基于血清学检测的牛结核病诊断新方法

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The diagnosis of bovine tuberculosis (TB) is still a challenge for a better control of the disease. Here we report the use of a simple ELISA test combined with a multilayer neuronal network analyzing method in order to diagnose TB in cattle from the north part of Tunisia. A panel of Mycobacterium bovis (Mbv)-specific recombinant proteins along with crud extracts were used to coat the 96 wells plates, namely the recombinant 10 kDa culture filtrate antigen (CFP-10), the 6 kD Early Secretory Antigenic Target (ESAT-6), the recombinant Esat-6/CFP-10 heterodimer along with the crud BCG proteins and Tuberculin purified protein derivative (PPD). In the current article, a new approach is described to compare their characterization degree to select the most discriminative antigens. The classification of subjects into two groups: TB+ and TB-subjects was affected by an artificial multilayer neural network and the statistical study to estimate the diagnosis of bovine tuberculosis and construct an optimal partition of the results. This method was applied on the serological tests of a set of cattles. The results are encouraging.
机译：牛结核（TB）的诊断仍然是更好地控制疾病的挑战。在这里，我们报告使用简单的ELISA测试结合多层神经网络分析方法来诊断突尼斯北部牛群中的TB。使用一组牛分枝杆菌（Mbv）特异的重组蛋白和原生提取物包被96孔板，即重组10 kDa培养滤液抗原（CFP-10），6 kD早期分泌抗原靶标（ESAT-6） ），重组Esat-6 / CFP-10异二聚体以及粗粒BCG蛋白和结核菌素纯化的蛋白衍生物（PPD）。在当前的文章中，描述了一种新方法来比较它们的表征程度以选择最具区分性的抗原。受试者的分类分为两组：TB +和TB受试者受到人工多层神经网络和统计研究的影响，以评估牛结核病的诊断并构建结果的最佳划分。该方法用于一组牛的血清学测试。结果令人鼓舞。

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