Feasibility of a Clinical Chemical Analysis Approach To Predict Misuse of Growth Promoting Hormones in Cattle
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- 作者：Rodat T. Cunningham ; Mark H. Mooney ; Xiao-Lei Xia ; Steven Crooks ; David Matthews ; Michael O’ ; Keeffe ; Kang Li ; Christopher T. Elliott
- 刊名：Analytical Chemistry
- 出版年：2009
- 出版时间：February 1, 2009
- 年：2009
- 卷：81
- 期：3
- 页码：977-983
- 全文大小：171K
- 年卷期：v.81,no.3(February 1, 2009)
- ISSN：1520-6882
文摘
A study was performed to determine if targeted metabolic profiling of cattle sera could be used to establish a predictive tool for identifying hormone misuse in cattle. Metabolites were assayed in heifers (n = 5) treated with nortestosterone decanoate (0.85 mg/kg body weight), untreated heifers (n = 5), steers (n = 5) treated with oestradiol benzoate (0.15 mg/kg body weight) and untreated steers (n = 5). Treatments were administered on days 0, 14, and 28 throughout a 42 day study period. Two support vector machines (SVMs) were trained, respectively, from heifer and steer data to identify hormone-treated animals. Performance of both SVM classifiers were evaluated by sensitivity and specificity of treatment prediction. The SVM trained on steer data achieved 97.33% sensitivity and 93.85% specificity while the one on heifer data achieved 94.67% sensitivity and 87.69% specificity. Solutions of SVM classifiers were further exploited to determine those days when classification accuracy of the SVM was most reliable. For heifers and steers, days 17−35 were determined to be the most selective. In summary, bioinformatics applied to targeted metabolic profiles generated from standard clinical chemistry analyses, has yielded an accurate, inexpensive, high-throughput test for predicting steroid abuse in cattle.