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Validation of Prediction Algorithms for Early Detection of Clinical Mastitis caused by Gram-positive and Gram-negative Pathogens

机译:克阳性和革兰氏阴性病原体引起临床乳腺炎早期检测的预测算法

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As precision technologies become more available on-farm, an opportunity exists for automated animal health monitoring and the early detection of diseases such as mastitis. Milk components remain relatively stable over time in healthy cows (Forsback etal. 2010), but changes in milk composition and cow activity have been reported prior to clinical mastitis (CM; Tholen, 2012). This time-series change in cow performance and behavior could help to not only identify mastitis but also give an indication ofthe pathogen type, which could assist with treatment decisions. This is especially important as the use of antimicrobials is coming under increased scrutiny. The objective of this study was to validate algorithms derived from milk and activity measuresfor the retrospective ability to identify CM caused by Gram-positive and Gram-negative pathogens.
机译:由于精密技术变得更加易于农场,自动化动物健康监测和早期检测乳腺炎等疾病的机会存在。 在健康的奶牛中随着时间的推移仍然存在相对稳定的奶牛(封背etal。2010),但在临床乳腺炎之前已经报告了牛奶组合物和牛活性的变化(cm; tholen,2012)。 这种时间序列的母牛性能和行为的变化可能有助于不仅鉴定乳腺炎,而且还可以指示病原体类型,可以帮助治疗决策。 这尤其重要,因为使用抗微生物的使用增加了审查。 本研究的目的是验证衍生自牛奶和活动措施的算法,对于鉴定由革兰氏阳性和革兰氏阴性病原体引起的厘米的回顾能力。

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