Lasch Peter, Schmitt Jürgen, Beekes Michael, Udelhoven Thomas, Eiden Michael, Fabian Heinz, Petrich Wolfgang, Naumann Dieter
P13, Robert Koch-Institut, Nordufer 20, 13353 Berlin, Germany.
Anal Chem. 2003 Dec 1;75(23):6673-8. doi: 10.1021/ac030259a.
Since 1986, more than 180 000 clinical cases of bovine spongiform encephalopathy (BSE) have been observed in the U.K. alone. Most of these cases were confirmed by postmortem examination of brain tissue. However, BSE-related risk assessment and risk management would greatly benefit from antemortem testing on living animals. A serum-based test could allow for screening of the cattle population; thus, even a BSE eradication program would be conceivable. Here we report on a novel method for antemortem BSE testing, which combines infrared spectroscopy of serum samples with multivariate pattern recognition analysis. A classification algorithm was trained using infrared spectra of bovine sera from more than 800 animals (including BSE-positive, healthy controls and animals suffering from classical viral or bacterial infections). In two validation studies, sensitivities of 85 and 84% and specificities of 86 and 91% were achieved, respectively. The combination of classification algorithms increased the sensitivity and specificity of BSE detection to 96 and 92%, respectively.
自1986年以来，仅在英国就观察到超过18万例牛海绵状脑病（BSE）临床病例。这些病例大多通过脑组织的尸检得以确诊。然而，BSE相关的风险评估和风险管理将极大地得益于对活体动物进行的生前检测。基于血清的检测能够对牛群进行筛查；因此，甚至可以设想一个BSE根除计划。在此，我们报告一种用于BSE生前检测的新方法，该方法将血清样本的红外光谱与多变量模式识别分析相结合。使用来自800多头动物（包括BSE阳性、健康对照以及患有经典病毒或细菌感染的动物）的牛血清红外光谱训练了一种分类算法。在两项验证研究中，灵敏度分别达到了85%和84%，特异性分别达到了86%和91%。分类算法的组合将BSE检测的灵敏度和特异性分别提高到了96%和92%。
