Improving the accuracy of detecting steroid abuse in cattle by pairwise learning of serum samples

• Autorzy: Xia X. L.

Identyfikatory

DOI

10.1016/j.bbe.2017.05.009

• Języki publikacji: EN

Abstrakty

EN

Issues surrounding the misuse of illegal drugs in animals destined for food production have be an enormous challenge to regulatory authorities charged with enforcing their control. A method has been proposed recently which compared the bovine blood biochemistry profiles between control and treated animals, using the support vector machine (SVM) as the classification tool. Whether an animal has been treated is determined by the classification outcome of the SVM on an individual serum sample taken off the animal. However, the acquisition time of the serum sample is essential in the classification performance of the SVM. Thus, the paper proposed to collect and analyze a pair of samples, in order to obtain at least one sample whose acquisition time resulted in an SVM with the highest sensitivity. The power of the strategy in improving sensitivity was theoretically proven to be up to 0.25 and empirically confirmed on a bovine blood biochemistry data. Furthermore, classification rules of the SVM were proposed to be adapted to meet higher levels of demands on sensitivity. Schemes were described which optimized the time apart between the collection of the two samples and the impact of the proposed strategy on specificity was also investigated.

Słowa kluczowe

EN

support vector machine; steroid abuse; screening method

PL

maszyna wektorów wspierających; nadużywanie steroidów; metoda przesiewowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 3
• Strony: 510--519
• Opis fizyczny: Bibliogr. 20 poz., tab., wykr.

Twórcy

autor

Xia X. L.

• School of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, PR China

Bibliografia

• [1] Antignac J-P, Brosseaud A, Gaudin-Hirret I, André F, Le Bizec B. Analytical strategies for the direct mass spectrometric analysis of steroid and corticosteroid phase II metabolites. Steroids 2005;70(3):205–16.
• [2] Burges C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998;2(2):121–67.
• [3] Cantiello M, Giantin M, Carletti M, Lopparelli RM, Capolongo F, Lasserre F, et al. Effects of dexamethasone, administered for growth promoting purposes, upon the hepatic cytochrome p450 3a expression in the veal calf. Biochem Pharmacol 2009;77(3):451–63.
• [4] Chang C-C, Lin C-J. LIBSVM: a library for support vector machines.. ACM Trans Intel Syst Technol 2011;2. 27:1–27:27. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
• [5] Cooper J, Elliott CT, Baxter GA, Hewitt SA, McEvoy JD, McCaughey WJ. Comparison of two polyclonal antibodies for the detection of 19-nortestosterone in bovine bile by ELISA. Food Agric Immunol 1998;10(2):133–42.
• [6] Cunningham RT, Mooney MH, Xia X-L, Crooks S, Matthews D, OKeeffe M, et al. Feasibility of a clinical chemical analysis approach to predict misuse of growth promoting hormones in cattle. Anal Chem 2009;81(3):977–83.
• [7] Draisci R, Palleschi L, Marchiafava C, Ferretti E, Quadri FD. Confirmatory analysis of residues of stanozolol and its major metabolite in bovine urine by liquid chromatography–tandem mass spectrometry. J Chromatogr A 2001;926(1):69–77.
• [8] Dumas M-E, Canlet C, Vercauteren J, Andre F, Paris A. Homeostatic signature of anabolic steroids in cattle using 1H–13C HMBC NMR metabolomics. J Proteome Res 2005;4 (5):1493–502.
• [9] Gardini G, Del Boccio P, Colombatto S, Testore G, Corpillo D, Di Ilio C, et al. Proteomic investigation in the detection of the illicit treatment of calves with growth-promoting agents. Proteomics 2006;6(9).
• [10] Joos P, Van Ryckeghem M. Liquid chromatography–tandem mass spectrometry of some anabolic steroids. Anal Chem 1999;71(20):4701–10.
• [11] Makarov A, Denisov E, Kholomeev A, Balschun W, Lange O, Strupat K, et al. Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer. Anal Chem 2006;78 (7):2113–20.
• [12] Mooney M, Bergwerff A, van Meeuwen J, Luppa P, Elliott C. Biosensor-based detection of reduced sex hormone-binding globulin binding capacities in response to growth-promoter administrations. Anal Chim Acta 2009;637(1-2):235–40.
• [13] Noppe H, Le Bizec B, Verheyden K, De Brabander H. Novel analytical methods for the determination of steroid hormones in edible matrices. Anal Chim Acta 2008;611(1):1–16.
• [14] Odore R, Badino P, Barbero R, Cuniberti B, Pagliasso S, Girardi C, et al. Regulation of tissue b-adrenergic, glucocorticoid and androgen receptors induced by repeated exposure to growth promoters in male veal calves. Res Vet Sci 2007;83(2):227–33.
• [15] O'Keeffe M. Trenbolone levels in tissues of trenbolone acetate-implanted steers: radioimmunoassay determination using different antisera. Br Vet J 1984;140 (6):592–9.
• [16] Stephany R. Hormones in meat: different approaches in the EU and in the USA APMIS. Supplementum 2001;(103):S357–63.
• [17] Stolker AA, Zoontjes PW, van Ginkel LA. The use of supercritical fluid extraction for the determination of steroids in animal tissues. Analyst 1998;123(12):2671–6.
• [18] Vapnik V. The nature of statistical learning theory. NY: Springer; 1995.
• [19] White M, Johnson B, Hathaway M, Dayton W. Growth factor messenger RNA levels in muscle and liver of steroid- implanted and nonimplanted steers. J Anim Sci 2003;81(4):965.
• [20] Xia X-L, Xing H, Liu X. Analyzing kernel matrices for the identification of differentially expressed genes. PLoS ONE 2013;8(12):e81683.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).