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Advances in artificial intelligence-based microbiome for PMI estimation

Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regulari...

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Autores principales: Wang, Ziwei, Zhang, Fuyuan, Wang, Linlin, Yuan, Huiya, Guan, Dawei, Zhao, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577360/
https://www.ncbi.nlm.nih.gov/pubmed/36267183
http://dx.doi.org/10.3389/fmicb.2022.1034051
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author Wang, Ziwei
Zhang, Fuyuan
Wang, Linlin
Yuan, Huiya
Guan, Dawei
Zhao, Rui
author_facet Wang, Ziwei
Zhang, Fuyuan
Wang, Linlin
Yuan, Huiya
Guan, Dawei
Zhao, Rui
author_sort Wang, Ziwei
collection PubMed
description Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation.
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spelling pubmed-95773602022-10-19 Advances in artificial intelligence-based microbiome for PMI estimation Wang, Ziwei Zhang, Fuyuan Wang, Linlin Yuan, Huiya Guan, Dawei Zhao, Rui Front Microbiol Microbiology Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation. Frontiers Media S.A. 2022-10-04 /pmc/articles/PMC9577360/ /pubmed/36267183 http://dx.doi.org/10.3389/fmicb.2022.1034051 Text en Copyright © 2022 Wang, Zhang, Wang, Yuan, Guan and Zhao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Wang, Ziwei
Zhang, Fuyuan
Wang, Linlin
Yuan, Huiya
Guan, Dawei
Zhao, Rui
Advances in artificial intelligence-based microbiome for PMI estimation
title Advances in artificial intelligence-based microbiome for PMI estimation
title_full Advances in artificial intelligence-based microbiome for PMI estimation
title_fullStr Advances in artificial intelligence-based microbiome for PMI estimation
title_full_unstemmed Advances in artificial intelligence-based microbiome for PMI estimation
title_short Advances in artificial intelligence-based microbiome for PMI estimation
title_sort advances in artificial intelligence-based microbiome for pmi estimation
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577360/
https://www.ncbi.nlm.nih.gov/pubmed/36267183
http://dx.doi.org/10.3389/fmicb.2022.1034051
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