Changes of cecal microbial community in seawater drowning rats and estimation of postmortem interval in combination with random forest machine learning algorithm

Xu Yuzhao, Qin Xiaoshi, Zhou Yunchao, Ha Shan, Lu Jianghuan, Ma Yixin, Duan Zhiao, Chen Jianhua, Deng Jianqiang

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Journal of Chongqing Medical University ›› 2024, Vol. 49 ›› Issue (12) : 1508-1519. DOI: 10.13406/j.cnki.cyxb.003687
Forensic pathology

Changes of cecal microbial community in seawater drowning rats and estimation of postmortem interval in combination with random forest machine learning algorithm

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Abstract

Objective To investigate the succession patterns of cecal microbiota in rats drowned in seawater and those submerged after death by CO2 asphyxiation，and to infer postmortem submersion interval（PMSI） using the random forest（RF） machine learning algorithm. Methods In this study，70 specific pathogen-free healthy adult Sprague-Dawley rats were randomly divided into seawater drowning group（D group） and postmortem submersion group（PS group）. The rats were placed in a constant temperature chamber at 25°C after modeling，and samples of cecal contents were collected at various postmortem time points（0，12，24，36，48，72，and 96 hours）. The CTAB method was used to extract DNA，and the V3-V4 region of bacterial 16S rDNA was specifically amplified； high-throughput sequencing was performed to obtain sequencing data and analyze the diversity of cecal microbiota community. Related biomarkers were obtained based on the results of microbiome sequencing and the RF machine learning algorithm，and then PMSI prediction models were established for the bodies of rats in both the D group and the PS group. Results There were differences in the characteristics of cecal microbiota community between the D group and the PS group. A total of 16 potential biomarkers were obtained by the RF machine learning algorithm，and a model for inferring PMSI was established for the D group and the PS group. For the D group，the predictive model had a mean absolute error（MAE） of 13.272 hours and an R2 value of 0.798，while for the PS group，the predictive model had an MAE value of 8.445 hours and an R2 value of 0.847. Conclusion This study shows that there are regular differences in cecal microbiota community between seawater drowning and postmortem submersion，and it proves that the succession of cecal content microbiota based on the RF machine learning algorithm can be used an effective biomarker for inferring the PMSI of bodies in seawater.

Key words

drowning / postmortem submersion interval / random forest / microbiome / estimation of postmortem interval

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Xu Yuzhao , Qin Xiaoshi , Zhou Yunchao , et al . Changes of cecal microbial community in seawater drowning rats and estimation of postmortem interval in combination with random forest machine learning algorithm. Journal of Chongqing Medical University. 2024, 49(12): 1508-1519 https://doi.org/10.13406/j.cnki.cyxb.003687

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