对抗微生物药物耐药性的描述性机器学习方法的探索:沙门氏菌中多种药物耐药性模式
Abdolreza Mosaddegh1, Claudia Cobo Angel1, Maya Craig2
1Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States.
Preventive veterinary medicine
|July 4, 2024
概括
在美国农业中使用抗菌剂的规定减少了沙门氏菌的抗菌素耐药性 (AMR) 和多药耐药性 (MDR). 机器学习揭示了牛分离后监管中的特定MDR模式.
科学领域:
- 兽医微生物学 兽医微生物学
- 食品安全 食品安全
- 计算生物学 计算生物学
背景情况:
- 沙门氏菌病是影响人类和动物的一种主要的全球性食物传播疾病.
- 在 Salmonella enterica 中,抗菌素耐药性 (AMR) 和多药耐药性 (MDR) 引发了重大的公共卫生问题.
- 在畜牧业中使用抗微生物药物 (AMU) 的规定对于减轻食物传播病原体的AMR和MDR至关重要.
研究的目的:
- 在美国农业中限制AMU之前和之后调查沙门氏菌AMR和MDR模式.
- 评估2012年标签外塞法洛斯波林法规对抗药物耐药性趋势的影响.
- 在沙门氏菌血清型中识别特定的MDR模式.
主要方法:
- 使用了一种集成的机器学习方法.
- 通过使用国家抗菌素耐药性监测系统 (NARMS) 数据集分析了来自牛的沙门氏菌分离物.
- 采用等级聚类,网络分析和协会规则挖掘.
主要成果:
- 证明了美国2012年标签外黄素法规对AMR趋势的影响.
- 在都柏林血清型中确定了一个独特的MDR模式.
- 展示了结合描述模型如何为AMR模式提供更深入的见解.
结论:
- 集成机器学习有效地分析复杂的AMR和MDR数据.
- 针对AMU的法规显示,AMU对减少食品传播病原体的AMR和MDR有可衡量的影响.
- 了解血清型特定的MDR模式对于有针对性的干预至关重要.
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