[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2125":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":61},2125,"Multi-frequency bioelectrical impedance and machine learning for non-invasive detection of Caseous Lymphadenitis in goats: a one health precision surveillance approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102548","Caseous lymphadenitis is a chronic infectious disease of sheep and goats caused by Corynebacterium pseudotuberculosis , with major implications for animal health, farm productivity, carcass value, and occupational exposure risk. Because infected animals may remain clinically inapparent or develop chronic lesions, practical field-based screening tools are needed to improve early detection and herd-level disease management. This study evaluated whether multi-frequency bioelectrical impedance analysis could differentiate healthy\u002Fcontrol goats from CL-positive goats using resistance, reactance, phase angle, and derived spectral features. Measurements were summarized across 50, 100, and 180 kHz, followed by multivariate visualization, Mahalanobis distance analysis, leave-one-subject-out validation, animal-level risk ranking, calibration assessment, and decision curve analysis. CL-positive goats showed distinct frequency-dependent impedance behavior, including altered resistance-reactance profiles and consistently higher phase angle responses across frequencies. Multivariate spectral distance from the healthy centroid was significantly greater in CL-positive animals, suggesting measurable bioelectrical deviation from the healthy impedance profile. A nonlinear model outperformed the linear model in leave-one-subject-out ROC analysis, with an AUC of 0.68 compared with 0.56, indicating moderate discrimination from raw impedance measurements. Animal-level risk ranking further showed enrichment of CL-positive goats among higher predicted-risk animals, while decision curve analysis suggested potential screening value at lower clinical threshold probabilities. These findings support the feasibility of BIA as a rapid, non-invasive adjunct screening tool for CL-associated risk classification in goats. From a One Health perspective, this approach may improve animal welfare, reduce disease persistence in small-ruminant systems, support producer decision-making, and decrease human exposure risk linked to infected animals and contaminated farm environments.","干酪性淋巴结炎是由假结核棒状杆菌（Corynebacterium pseudotuberculosis）引起的一种绵羊和山羊慢性传染病，对动物健康、农场生产力、胴体价值及职业暴露风险均有重大影响。由于感染动物可能保持临床不明显状态或发展为慢性病变，因此需要实用的现场筛查工具，以改善早期检测和群体水平的疾病管理。本研究评估了多频生物电阻抗分析（bioelectrical impedance analysis, BIA）能否利用电阻、电抗、相位角及衍生的频谱特征区分健康\u002F对照山羊与CL阳性山羊。测量结果在50、100和180 kHz频率下进行汇总，随后进行多变量可视化、马氏距离分析、留一受试者法验证、个体水平风险排序、校准评估和决策曲线分析。CL阳性山羊表现出明显的频率依赖性阻抗行为，包括电阻-电抗特征的改变以及各频率下相位角反应持续升高。CL阳性动物与健康质心的多变量频谱距离显著更大，提示其生物电特征偏离健康阻抗谱。在留一受试者法ROC分析中，非线性模型优于线性模型，AUC为0.68，而线性模型为0.56，表明原始阻抗测量具有中等区分能力。个体水平风险排序进一步显示，CL阳性山羊在预测风险较高的动物中富集，而决策曲线分析提示在较低临床阈值概率下具有潜在筛查价值。这些发现支持BIA作为山羊CL相关风险分类的快速、非侵入性辅助筛查工具的可行性。从“同一健康”视角来看，该方法可能改善动物福利，减少小反刍动物系统中疾病的持续存在，支持生产者决策，并降低与感染动物及受污染农场环境相关的人类暴露风险。",null,"Smart Agricultural Technology","2026-09-08T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,9,1,"多频生物阻抗结合机器学习实现山羊干酪性淋巴结炎无创筛查，方法新颖、数据扎实，属智慧畜牧与疫病精准监测的可行探索，但AUC仅0.68、样本有限，尚处早期验证阶段。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","动物疫病防控","One Health","生物阻抗传感",0,"10.1016\u002Fj.atech.2026.102548",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":53,"card":54,"direction":58,"ingested_from":60},"W7211976751",[37,39,41,43,45,47,49,51],{"name":38,"orcid":9},"A. Siddique",{"name":40,"orcid":9},"A. Kingler",{"name":42,"orcid":9},"S. Neelagiri",{"name":44,"orcid":9},"R. Kota",{"name":46,"orcid":9},"D.I. Shapiro",{"name":48,"orcid":9},"C. Pisani",{"name":50,"orcid":9},"P. Batchu",{"name":52,"orcid":9},"T.H. Terrill","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007732\u002Fpdf",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"用多频生物电阻抗结合机器学习，无创筛查山羊干酪性淋巴结炎。","50\u002F100\u002F180 kHz 生物电阻抗测量，非线性模型与留一受试者验证。","患病羊相位角更高、阻抗谱偏离健康中心，非线性模型 AUC 0.68。","智慧农业 \u002F 农业物联网","可扩展多频阻抗传感与可穿戴设备，构建羊群疫病无创实时监测预警系统。","openalex","2026-09-11T23:30:04.019145Z"]