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Early postmortem interval estimation based on Cdc25b mRNA in rat cardiac tissue.

Li Tao1, Jianlong Ma2, Liujun Han1

  • 1Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, 131Dongan Road, Shanghai 200032, China.

Legal Medicine (Tokyo, Japan)
|September 22, 2018
PubMed
Summary

This study identifies cell division cycle 25 homolog B (Cdc25b) as a sensitive mRNA marker for estimating early postmortem intervals up to 24 hours. This molecular approach offers improved accuracy for determining time since death in forensic investigations.

Keywords:
Early postmortem intervalForensic medicineMicroarray expression profileR softwaremRNAqPCR

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Area of Science:

  • Forensic Science
  • Molecular Biology
  • Biochemistry

Background:

  • Accurate postmortem interval (PMI) estimation is crucial in forensic investigations.
  • Current methods for PMI assessment are limited to days to weeks.
  • There is a need for reliable molecular markers to determine early PMI (within 24 hours).

Purpose of the Study:

  • To identify sensitive mRNA markers for estimating early postmortem intervals (up to 24 hours).
  • To investigate the potential of mRNA degradation as an indicator of early PMI.
  • To develop a predictive model for early PMI estimation using molecular markers.

Main Methods:

  • Screening of 217 mRNA markers in rat cardiac tissue using microarrays.
  • Validation of candidate markers using real-time quantitative PCR (qPCR) at various time points and temperatures.
  • Development and verification of a mathematical model for PMI estimation using R software.

Main Results:

  • Cell division cycle 25 homolog B (Cdc25b) demonstrated the strongest correlation with early PMI.
  • A predictive model based on Cdc25b achieved an error rate of less than 15%.
  • Rpl27 was identified as a suitable endogenous control for normalization.

Conclusions:

  • Cdc25b is a sensitive molecular marker for estimating early postmortem intervals.
  • The developed mathematical model shows high predictive power for early PMI.
  • This study provides novel molecular approaches for early PMI estimation using mRNA markers.