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通过将微生物继承模式与机器学习算法集成,估计埋葬猪尸的死后间隔

Ting Yang¹, Xudong Chen¹, Qihua Xie¹

¹Department of Forensic Science, Xiangya School of Basic Medical Sciences, Central South University, Changsha 410013, China.

|January 28, 2026

概括

埋葬和表面遗骸中的微生物继承有助于估计死后间隔 (PMI). 这项研究发现了不同微生物模式在埋藏与表面分解,在两种情况下都有可能进行PMI估计.

科学领域:

法医科学法医科学法医科学
微生物学微生物学
生物信息学是一种生物信息学.

背景情况:

微生物继承是估计死后间隔 (PMI) 的关键指标.
了解埋葬遗骸中的微生物模式对于法医调查至关重要.
以前的研究主要集中在表面分解上,为埋藏的场景留下了知识上的空白.

研究的目的:

为了研究埋葬和表面猪尸体中的微生物继承模式.
为了比较微生物社区的动态在两个环境中的不同分解阶段.
开发和评估基于微生物数据的死后间隔估计模型.

主要方法:

建立一个猪尸体模型,包括埋葬和表面组.
用于微生物社区分析的16S核糖体RNA (16SrRNA) 基因测序.
使用属级微生物数据进行PMI估计的随机森林建模.

主要成果:

与表面尸体相比,埋葬的尸体表现出较慢的分解速度.
埋葬后遗骸早期分解后的阿尔法多样性减少,类似于表面遗骸.
在埋葬的遗骸中观察到显著的细菌社区转移和蛋白质细菌丰度变化,反映了表面模式.
与PMI相关的独特微生物种群在埋藏和表面群体中都被确定.
随机森林模型在埋藏和表面组中实现了PMI估计的平均绝对误差约为5天.

结论:

微生物继承模式在埋藏和表面分解环境中都是可预测的.
该研究表明,微生物分析在埋葬遗骸中估计PMI的潜力.
需要对更大的样本大小进行进一步的研究,以提高模型的概括性.

关键词:

16S rRNA 基因测序的基因测序他们埋葬了尸体.微生物的继承.在死后的间隔间隔.随机森林算法随机森林算法

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