Estimation of the human postmortem interval using an established rat mathematical model and multi-RNA markers

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• 作者：Ye-Hui Lv ; Jian-Long Ma ; Hui Pan ; Yan Zeng…
• 关键词：Forensic science ; Postmortem interval (PMI) ; RNA degradation ; Mathematical model ; Real ; time quantitative polymerase chain reaction (RT ; qPCR)
• 刊名：Forensic Science, Medicine, and Pathology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：13
• 期：1
• 页码：20-27
• 全文大小：
• 刊物主题：Pathology; Forensic Medicine; Criminology and Criminal Justice, general;
• 出版者：Springer US
• ISSN：1556-2891
• 卷排序：13

文摘

In our previous study, a R code-based mathematical model using RNA degradation patterns was developed for PMI determination in rat brain specimens. However, the postmortem changes of RNA are much more complicated in real cases, and there is still a huge challenge in efficiently applying information in animal data to real cases. In the present study, different RNA markers in both rat and human tissues were collected to screen valid biomarkers and the corresponding mathematical models were established and validated. With the same methodology, multi-RNA markers of myocardium and liver tissues were detected by qPCR and the Ct values of ten biomarkers generally increased with prolonged PMIs. 5S, miR-1 and miR-133a were shown to be optimum reference biomarkers that were not affected by a PMI of up to 5 or more days; however, liver-specific miR-122 began to degrade under higher temperatures and only 5S was selected as an endogenous control in the liver. Among the tested target RNAs, similar to our previous study in brain tissue, β-actin (ΔCt) was found to exhibit the best correlation coefficient with PMI and was employed to build mathematical models using R software. Following validation, the relatively low estimated error demonstrated that PMIs can be accurately predicted in human cases through comprehensive consideration of various factors and using effective biomarkers.