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使用机器学习利用多层次生物标志物:识别Bd耐药两动物的生理和皮肤微生物动态

Jun-Kyu Park¹, Ji-Eun Lee¹, Yuno Do¹

¹Department of Biological Sciences, Kongju National University, Chungcheongnam-do, Republic of Korea.

Integrative zoology

|July 21, 2025

概括

两动物种群面临着来自菌菌病 (Bd) 的威胁. 这项研究使用机器学习来分析Pelophylax nigromaculatus的生理和微生物组数据,揭示皮质水平和杀死细菌的能力作为关键感染指标.

科学领域:

两动物生态和疾病动态.
微生物学和免疫学微生物学和免疫学
机器学习在野生动物健康中的应用.

背景情况:

全球两动物的数量下降是由压力因素驱动的,包括由Batrachochytrium dendrobatidis (Bd) 引起的状菌病.
像Pelophylax nigromaculatus这样的韩国两动物物种对Bd表现出耐药性,使其精确影响的评估变得复杂.
了解耐药物种中的Bd感染动态对于两动物保护工作至关重要.

研究的目的:

为了研究Batrachochytrium dendrobatidis (Bd) 在Pelophylax nigromaculatus感染的动态.
整合生理学,微生物学和形态学的数据与机器学习进行感染分析.
为了确定可靠的生物标志物Bd感染状态在一个Bd耐药的两动物群体.

主要方法:

收集了关于Bd患病率,体型,体重,皮质 (CORT) 水平,先天性免疫功能 (杀死细菌的测试) 和皮肤微生物组合的数据.
应用机器学习方法,包括光梯度增强机和用于数据增强的生成对抗网络.
分析了生理和微生物组数据,以区分感染者和未感染者.

主要成果:

在感染和未感染的P. nigromaculatus之间观察到显著的生理差异,特别是高的CORT水平和改变的细菌杀伤能力.
皮肤微生物组分析显示了细微的变化,但感染群体和非感染群体之间的α或β多样性没有显著差异.
机器学习模型,特别是带有数据增强的光梯度增强机,实现了对分类感染状态的高预测性能.

结论:

皮质水平和杀死细菌的能力是Pelophylax nigromaculatus中Bd感染状态的有效预测因素.
机器学习可以有效地整合多层次的生物标志物来评估两动物的健康状况,即使疾病抵抗力和感染负载的变化也存在.
这种综合方法对于理解和减轻多种威胁对全球两动物种群的影响至关重要.

关键词:

两动物两动物内分泌干扰干扰内分泌系统这是先天免疫力.机器学习是机器学习.皮肤的微生物群.

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