通过使用全基因组测序数据,将Salmonella enterica归因于食物来源
Emerging infectious diseases
|March 25, 2025
概括
预测沙门氏菌 (Salmonella enterica) 感染的来源是一项挑战. 一个随机的森林模型准确地确定了肉和蔬菜是美国零星食物传播疾病的最常见来源.
科学领域:
- 食物传播病原体研究
- 计算流行病学计算流行病学
- 基因组流行病学 基因组流行病学
背景情况:
- 沙门氏菌 (Salmonella enterica) 细菌是美国食物传播疾病的主要原因.
- 大多数沙门氏菌感染是零星的,因此很难识别出源.
研究的目的:
- 开发一种预测模型,用于识别人类沙门氏菌病例的来源.
- 为了确定沙门氏菌来源归因的最有影响力的遗传标记.
主要方法:
- 利用了来自单一食物来源的18661个沙门氏菌分离物的全基因组多部位序列类型化 (wgMLST) 数据.
- 采用监督随机森林模型,用于源预测的特征选择.
- 验证了模型的准确性,使用一组独立的6,470个来自人类的沙门氏菌分离物.
主要成果:
- 随机森林模型在预测沙门氏菌源方面实现了91%的整体出袋准确度.
- 肉被确定为最准确的预测来源,准确率为97%.
- 该模型预测肉是超过33%的人类分离物的来源,蔬菜占27%.
结论:
- 全基因组多部位序列类型数据和随机森林建模对于将沙门氏菌感染归因于食物来源是有效的.
- 肉和蔬菜是美国偶尔出现的人类沙门氏菌病的重要原因.
- 这种方法可以帮助制定有针对性的食品安全干预措施和公共卫生战略.
关键词:
沙门氏菌 (Salmonella enterica) 已经成为一种常见的疾病.细菌 细菌 细菌是一种细菌.食品安全 食品安全食物传播疾病 食物传播疾病随机森林模型随机森林模型来源归因来源归因全基因组序列数据的数据.更多相关视频
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