1 July 2004 Ante mortem identification of BSE from serum using infrared spectroscopy
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Abstract
In our former studies a diagnostic approach for the detection of transmissible spongiform encephalopaties (TSE) based on FT-IR spectroscopy in combination with artificial neural networks was described, based on a controlled animal study with terminally ill Syrian hamsters and control animals. As a consequence of the bovine spongiform encephalopathy (BSE) crisis in Europe, the development of a disgnostic ante mortem test for cattle has become a matter of great scientific importance and public interest. Since 1986 more than 180,000 clinical cases of BSE have been observed in the UK alone. Most of these cases were confirmed by post mortem examination of brain tissue. However, BSE-related risk assessment and risk-management would greatly benefit from ante mortem testing on living animals. For example, 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 ante mortem BSE testing, which combines infrared spectroscopy of serum samples with multivariate pattern recognition analysis. A classification algorithm was trained using infrared spectra of sera from more than 800 animals from a field study (including BSE positive, healthy controls and animals suffering from viral or bacterial infections). In two validation studies sensitivities of 85% and 87% and specificities of 84% and 91% were achieved, respectively. The combination of classification algorithms increased sensitivity and specificity to 96% and 92%, respectively.
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Jürgen Schmitt, Jürgen Schmitt, Peter Lasch, Peter Lasch, Michael Beekes, Michael Beekes, Thomas Udelhoven, Thomas Udelhoven, Michael Eiden, Michael Eiden, Heinz Fabian, Heinz Fabian, Wolfgang H. Petrich, Wolfgang H. Petrich, Dieter Naumann, Dieter Naumann, } "Ante mortem identification of BSE from serum using infrared spectroscopy", Proc. SPIE 5321, Biomedical Vibrational Spectroscopy and Biohazard Detection Technologies, (1 July 2004); doi: 10.1117/12.552848; https://doi.org/10.1117/12.552848
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