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利用牛奶电导率对荷斯坦奶牛乳腺炎病原体进行鉴别。

Use of milk electrical conductivity for the differentiation of mastitis causing pathogens in Holstein cows.

机构信息

Department of Animal Sciences, Colorado State University, 350 W. Pitkin St., Fort Collins, CO 80523, USA.

Department of Clinical Sciences, College of Veterinary Medicine, University of Missouri, 1520 East, Rollins St, Columbia, MO 65201, USA.

出版信息

Animal. 2020 Mar;14(3):588-596. doi: 10.1017/S1751731119002210. Epub 2019 Oct 4.

Abstract

Mastitis is one of the most prevalent and costly diseases in dairy cattle. Key components for adequate mastitis control are the detection of early stages of infection, as well as the selection of appropriate management interventions and therapies based on the causal pathogens associated with the infection. The objective was to characterize the pattern of electrical conductivity (EC) in milk during intramammary infection, considering specific mastitis-causing pathogen groups involvement. Cows (n = 200) identified by an in-line mastitis detection system with a positive deviation ≥15% in the manufacturer's proprietary algorithm for EC (high electrical conductivity (HEC)) were considered cases and enrolled in the study at the subsequent milking. One control (CON) cow, within normal ranges for EC, was matched to each case. A composite milk sample was collected aseptically from each cow for bacteriological culture. Milk yield (MY) and EC were recorded for each milking during ±7 days relative to enrollment. Milk cultures were categorized into gram positive (GP), gram negative (GN), other (OTH) and no growth (NOG). Data were submitted for repeated-measures analysis with EC as the dependent variable and EC status at day -1, bacteriological culture category, parity number, stage of lactation and days relative to sampling as main independent variables. Average (± standard error (SE)) EC was greater in HEC than in CON cows (12.5 ± 0.5 v. 10.8 ± 0.5 mS/cm) on the day of identification (day -1). Milk yield on day -1 was greater in CON than in HEC (37.6 ± 5.1 v. 33.5 ± 5.2 kg). For practical management purposes, average EC on day -1 was similar for the different bacteriological culture categories: 11.4 ± 0.6, 11.7 ± 0.5, 12.3 ± 0.8 and 11.7 ± 0.5 mS/cm in GN, GP, OTH and NOG, respectively. Parity number was only associated with day -1 EC in HEC group, with the greatest EC values in parity 3 (12.3 ± 0.3 mS/cm), followed by parity 2 (11.9 ± 0.2 mS/cm), parity >3 (11.6 ± 0.5 mS/cm) and primiparous cows (11.2 ± 0.2 mS/cm). An effect on EC for the interaction of day relative to identification by pathogen gram category was observed. The same interaction effect was observed on daily MY. Overall, the level of variation for MY and EC between- and within-cows was substantial, and as indicated by the model diagnostic procedures, the magnitude of the variance in the cows in the CON group resulted in deviations from normality in the residuals. We concluded that characteristic temporal patterns in EC and MY in particular pathogen groups may provide indications for differentiation of groups of mastitis-causing pathogens. Further research to build detection models including EC, MY and cow-level factors is required for accurate differentiation.

摘要

乳腺炎是奶牛最常见和最昂贵的疾病之一。充分控制乳腺炎的关键因素包括早期感染的检测,以及根据与感染相关的致病病原体选择适当的管理干预措施和治疗方法。本研究的目的是描述乳腺炎病例和对照牛(CON)在发生乳腺炎时乳汁电导率(EC)的变化模式,并考虑特定的乳腺炎致病病原体组的参与。通过INLINE 乳腺炎检测系统检测到的奶牛(n = 200),其制造商专有的 EC 算法(高电导率(HEC))的阳性偏差≥15%,则被认为是病例,并在随后的挤奶时纳入研究。每头病例牛都匹配了一头在 EC 范围内正常的对照(CON)牛。从每头奶牛无菌采集混合乳样进行细菌培养。记录每次挤奶时的产奶量(MY)和 EC,记录时间为纳入研究前±7 天。牛奶培养物分为革兰氏阳性(GP)、革兰氏阴性(GN)、其他(OTH)和无生长(NOG)。EC 作为因变量,EC 状态在第-1 天、细菌培养物类别、胎次、泌乳阶段和采样相对天数作为主要独立变量,进行重复测量分析。在确定病例的当天(第-1 天),HEC 牛的平均(±标准误差(SE))EC 高于 CON 牛(12.5 ± 0.5 对 10.8 ± 0.5 mS/cm)。第-1 天 CON 牛的产奶量高于 HEC 牛(37.6 ± 5.1 对 33.5 ± 5.2 kg)。为了实际管理目的,不同细菌培养物类别的平均 EC 在第-1 天相似:GN、GP、OTH 和 NOG 分别为 11.4 ± 0.6、11.7 ± 0.5、12.3 ± 0.8 和 11.7 ± 0.5 mS/cm。胎次仅与 HEC 组的第-1 天 EC 相关,第 3 胎的 EC 值最大(12.3 ± 0.3 mS/cm),其次是第 2 胎(11.9 ± 0.2 mS/cm)、第>3 胎(11.6 ± 0.5 mS/cm)和初产牛(11.2 ± 0.2 mS/cm)。还观察到与病原体革兰氏分类有关的相对识别天数对 EC 的交互作用。在每日 MY 上也观察到相同的互作效应。总体而言,产奶量和 EC 之间的牛间和牛内变异程度很大,并且根据模型诊断程序,CON 组牛的方差大小导致残差偏离正态分布。我们得出结论,特定病原体组中 EC 和 MY 的特征时间模式可能为区分乳腺炎病原体群提供指示。需要进一步研究包括 EC、MY 和牛水平因素的检测模型,以进行准确区分。

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