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[Estimating Postmortem Interval by Protein Chip Detection Technology Combined with Multidimensional Statistics].
1School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China.
Fa Yi Xue Za Zhi
|December 9, 2020
Summary
Protein chip analysis of rat liver tissue reveals distinct protein expression profiles that change over time after death. These profiles can accurately predict the postmortem interval, offering new forensic methods.
Area of Science:
- Forensic Science
- Biochemistry
- Proteomics
Background:
- Determining the postmortem interval is crucial in forensic investigations.
- Existing methods for estimating postmortem interval have limitations.
Purpose of the Study:
- To investigate the protein expression profile of rat liver tissue postmortem.
- To establish a correlation between protein expression and postmortem interval.
- To develop a predictive model for postmortem interval estimation.
Main Methods:
- Water-soluble proteins from rat liver tissue were extracted at 14 time points postmortem.
- Protein expression profiles were analyzed using a protein chip and the Agilent 2100 bioanalyzer.
- Statistical analyses including Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Fisher discriminant were employed.
Main Results:
- Protein expression profiles varied significantly with postmortem interval.
- PLS-DA successfully classified postmortem intervals into three groups (0 d, 1-9 d, 12-30 d) with 100% accuracy.
- Fisher discriminant models achieved high prediction accuracy (up to 100% in training and test sets) for postmortem interval estimation, with specific models developed for different time windows.
Conclusions:
- Protein chip technology provides a rapid and effective method for analyzing postmortem protein expression profiles.
- PLS-DA and Fisher discriminant models are valuable tools for classifying and predicting postmortem intervals.
- This approach offers novel insights and methodologies for forensic science in estimating time since death.
