Animal models for understanding microbial decomposition of human remains
Animal models are critical for forensic science research, particularly in studies of decomposition. This review examines the studies that have led to the development of using microbiome tools to predict the time since death, or postmortem interval (PMI), of human remains. Estimating PMI is crucial f...
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Published in | Drug discovery today. Disease models Vol. 28; pp. 117 - 125 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
2018
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Online Access | Get full text |
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Summary: | Animal models are critical for forensic science research, particularly in studies of decomposition. This review examines the studies that have led to the development of using microbiome tools to predict the time since death, or postmortem interval (PMI), of human remains. Estimating PMI is crucial for forensic investigations, and most traditional tools are no longer effective after the first few days postmortem. The development of microbiome tools to estimate PMI has relied on rodents and swine to model human decomposition. The use of these model organisms provides several advantages over studies utilizing human remains, including ease of procurement, large sample sizes, and the ability to control variables. Through studies using model organisms, researchers have been able to answer many fundamental questions regarding postmortem microbial decomposition, including the impacts of soil type, cadaver mass, cadaver clothing, and sampling location. Generally, these studies have been used to provide a proof-of-concept and narrow hypotheses before conducting studies on human remains. Evidence suggests that rodents and swine accurately model human microbial decomposition, but further study should be conducted to directly compare these outcomes. An important open topic that could be addressed with animal models is the role of drugs in changing cadaver-associated microbiomes during decomposition. |
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ISSN: | 1740-6757 1740-6757 |
DOI: | 10.1016/j.ddmod.2019.08.013 |