Literature DB >> 33667880

Predicting the postmortem interval of burial cadavers based on microbial community succession.

Jun Zhang1, Mengchun Wang1, Xiaoqin Qi1, Linyu Shi1, Jiarong Zhang1, Xiaomeng Zhang1, Tingting Yang1, Jianbo Ren1, Feng Liu1, Gengqian Zhang2, Jiangwei Yan3.   

Abstract

Previous studies have demonstrated that microbial community succession during the decomposition of cadavers could be used to estimate the postmortem interval (PMI). However, the vast majority of the existing studies focused on exposed cadavers. In fact, burial cadavers are common scenarios for forensic investigations. In this study, the microbial communities from gravesoil, rectum and skin of burial SD rat cadavers during decomposition were characterized using 16S rRNA gene high-throughput sequencing. We predicted PMI based on the microbial community succession. Obvious differences in microbial community structures were observed between different stages of decomposition. Later decay stages had a lower alpha diversity compared to earlier decay stages. Significant linear relationships between similarities of the microbial communities and postmortem intervals were observed, manifesting regular succession over the course of decomposition. Furthermore, we combined random forest models with postmortem microbial features to predict PMI. The model explained 86.83%, 84.55% and 81.67% of the variation in the microbial community, with a mean absolute error of 1.82, 2.06 and 2.13 days within 60 days of decomposition for gravesoil, rectum and skin of burial cadavers, respectively. Overall, our results suggested that postmortem microbial community data could serve as a potential forensic tool to estimate accurate PMI of burial cadavers.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Burial cadavers; Decomposition; Forensic tool; Microbial community; PMI estimation

Mesh:

Substances:

Year:  2021        PMID: 33667880     DOI: 10.1016/j.fsigen.2021.102488

Source DB:  PubMed          Journal:  Forensic Sci Int Genet        ISSN: 1872-4973            Impact factor:   4.882


  3 in total

Review 1.  Animal Forensic Genetics.

Authors:  Adrian Linacre
Journal:  Genes (Basel)       Date:  2021-04-01       Impact factor: 4.096

Review 2.  Advances in artificial intelligence-based microbiome for PMI estimation.

Authors:  Ziwei Wang; Fuyuan Zhang; Linlin Wang; Huiya Yuan; Dawei Guan; Rui Zhao
Journal:  Front Microbiol       Date:  2022-10-04       Impact factor: 6.064

3.  Applications of massively parallel sequencing in forensic genetics.

Authors:  Thássia Mayra Telles Carratto; Vitor Matheus Soares Moraes; Tamara Soledad Frontanilla Recalde; Maria Luiza Guimarães de Oliveira; Celso Teixeira Mendes-Junior
Journal:  Genet Mol Biol       Date:  2022-09-19       Impact factor: 2.087

  3 in total

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