在宿主内贝叶斯联合建模Leishmania感染的纵向和时间到事件数据
Felix M Pabon-Rodriguez1, Grant D Brown1, Breanna M Scorza2,3
1Department of Biostatistics, The University of Iowa College of Public Health, Iowa City, Iowa, United States.
bioRxiv : the preprint server for biology
|September 25, 2023
概括
我们开发了一种贝叶斯模型,以了解病原体负载,抗体和免疫因素如何影响犬类莱什曼病 (CanL) 的进展和死亡风险. 该模型预测了感染莱什马尼亚的狗的个体疾病轨迹.
科学领域:
- 免疫学 免疫学 免疫学
- 数学生物学 数学生物学
- 兽医医学 兽医医学 兽医医学
背景情况:
- 宿主免疫反应对于管理感染至关重要,但模型复杂.
- 数学和统计模型用于研究宿主-病原体动态.
- 由于其复杂的因素相互作用,Leishmania感染带来了重大的建模挑战.
研究的目的:
- 介绍一个贝叶斯联合模型,用于莱什马尼亚感染的纵向和时间到事件数据.
- 研究病原体负载,抗体水平和疾病进展之间的相互作用.
- 模拟影响疾病严重程度的炎症和调节性免疫因素.
主要方法:
- 开发了一个贝叶斯联合模型,整合了纵向 (病原体负载,抗体水平,免疫因素) 和时间到事件 (死亡) 数据.
- 包含了关于CD4+和CD8+T细胞增殖,IL-10,IFN-和PD-1表达的数据.
- 利用了一组自然暴露于Leishmania婴儿时期的狗的数据.
主要成果:
- 从渐进的莱什马尼亚感染中死亡的时间和纵向结果之间的特征关系.
- 描述了驱动疾病进展和影响死亡风险的机制.
- 证明了该模型对个体犬类莱什曼病 (CanL) 进展轨迹的预测能力.
结论:
- 宿主内部模型为了解慢性疾病进展提供了一个框架,例如内脏莱什曼病.
- 这种方法可以解决复杂传染病的重要研究问题.
- 该模型有助于预测个体疾病轨迹,并为疾病管理策略提供信息.
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