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.

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In forensic practice, it is difficult to determine whether a dead body in the water resulted from drowning or from disposal after death. Diatom testing is currently an important supporting technique for the determination of death by drowning and of drowning sites, even though it is a time-consuming and laborious task. This article reviews the development of diatom testing over the decades and discusses a new method for the potential application of deep learning in diatom testing.

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