Literature DB >> 15457319

Classification of signatures of Bovine Spongiform Encephalopathy in serum using infrared spectroscopy.

T C Martin1, J Moecks, A Belooussov, S Cawthraw, B Dolenko, M Eiden, J Von Frese, W Kohler, J Schmitt, R Somorjai, T Udelhoven, S Verzakov, W Petrich.   

Abstract

Signatures of Bovine Spongiform Encephalopathy (BSE) have been identified in serum by means of "Diagnostic Pattern Recognition (DPR)". For DPR-analysis, mid-infrared spectroscopy of dried films of 641 serum samples was performed using disposable silicon sample carriers and a semi-automated DPR research system operating at room temperature. The combination of four mathematical classification approaches (principal component analysis plus linear discriminant analysis, robust linear discriminant analysis, artificial neural network, support vector machine) allowed for a reliable assignment of spectra to the class "BSE-positive" or "BSE-negative". An independent, blinded validation study was carried out on a second DPR research system at the Veterinary Laboratory Agency, Weybridge, UK. Out of 84 serum samples originating from terminally-ill, BSE-positive cattle, 78 were classified correctly. Similarly, 73 out of 76 BSE-negative samples were correctly identified by DPR such that, numerically, an accuracy of 94.4 % can be calculated. At a confidence level of 0.95 (alpha = 0.05) these results correspond to a sensitivity > 85% and a specificity > 90%. Identical class assignment by all four classifiers occurred in 75% of the cases while ambiguous results were obtained in only 8 of the 160 cases. With an area under the ROC (receiver operating charateristics) curve of 0.991, DPR may potentially supply a valuable surrogate marker for BSE even in cases in which a deliberate bias towards improved sensitivity or specificity is desired. To the best of our knowledge, DPR is the first and--up to now--only method which has demonstrated its capability of detecting BSE-related signatures in serum.

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Year:  2004        PMID: 15457319     DOI: 10.1039/b408950m

Source DB:  PubMed          Journal:  Analyst        ISSN: 0003-2654            Impact factor:   4.616


  4 in total

1.  A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data.

Authors:  Bjoern H Menze; B Michael Kelm; Ralf Masuch; Uwe Himmelreich; Peter Bachert; Wolfgang Petrich; Fred A Hamprecht
Journal:  BMC Bioinformatics       Date:  2009-07-10       Impact factor: 3.169

Review 2.  Application of "omics" to prion biomarker discovery.

Authors:  Rhiannon L C H Huzarewich; Christine G Siemens; Stephanie A Booth
Journal:  J Biomed Biotechnol       Date:  2010-03-04

3.  Ion mobility spectrometry nuisance alarm threshold analysis for illicit narcotics based on environmental background and a ROC-curve approach.

Authors:  Thomas P Forbes; Marcela Najarro
Journal:  Analyst       Date:  2016-05-20       Impact factor: 4.616

Review 4.  Detection of Pathognomonic Biomarker PrPSc and the Contribution of Cell Free-Amplification Techniques to the Diagnosis of Prion Diseases.

Authors:  Hasier Eraña; Jorge M Charco; Ezequiel González-Miranda; Sandra García-Martínez; Rafael López-Moreno; Miguel A Pérez-Castro; Carlos M Díaz-Domínguez; Adrián García-Salvador; Joaquín Castilla
Journal:  Biomolecules       Date:  2020-03-19
  4 in total

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