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Molecular biology research progress in post-mortem interval (PMI) estimation in forensic medicine
Ting He1,2, Binghui Song1,2,3, Junjiang Fu4,5,6
1Key Laboratory of Epigenetics and Oncology, the Research Center for Preclinical Medicine, Southwest Medical University, Luzhou, Sichuan, 646000, China.
Abstract:
In forensic practice, accurately estimating post-mortem interval (PMI) is a crucially significant task, as it can provide key clues for cases in forensic medicine. However, it has also been a major challenge since ancient times. Currently, the traditional methods used in forensic medicine to infer PMI mainly include early post-mortem phenomena, corneal opacity, degree of gastric content digestion, and entomological analysis, but are significantly influenced by environmental factors and individual differences, presenting certain defects in terms of precision and applicability. With the advancement of modern molecular biology techniques, the application of gene expression analysis in the area of forensic medicine has gradually become a research hotspot. Moreover, the integration of machine learning algorithms and artificial intelligence (AI) can analyze multi-source data to construct prediction models, thereby improving the correctness of PMI inference and expanding its application scenarios. In this review, we elaborate on the research advancements, mainly in molecular biology or forensic molecular genetics of PMI estimation in forensic medicine. By systematically reviewing the latest research findings of molecular biology in PMI estimation and exploring its future directions, this review also endeavors to offer valuable references for forensic practitioners to improve the reliability of PMI inference in practical forensic potential applications in the future.
Insights
Estimating the time of death (post-mortem interval or PMI) is vital in forensic science. Molecular genetics and AI offer more precise methods than traditional techniques, improving forensic case analysis.
Area of Science:
- Forensic Science
- Molecular Genetics
- Computational Biology
Background:
- Accurate post-mortem interval (PMI) estimation is critical in forensic medicine but challenging.
- Traditional PMI methods (e.g., gastric digestion, entomology) have limitations due to environmental and individual variability.
- Molecular biology offers new avenues for precise PMI determination.
Purpose of the Study:
- To review advancements in molecular biology and forensic molecular genetics for PMI estimation.
- To explore the integration of artificial intelligence (AI) and machine learning (ML) in PMI prediction.
- To provide references for improving PMI inference reliability in forensic practice.
Main Methods:
- Systematic review of molecular biology research in PMI estimation.
- Analysis of gene expression patterns for time since death.
- Application of AI and ML algorithms to multi-source forensic data.
Main Results:
- Molecular techniques, particularly gene expression analysis, show promise for accurate PMI estimation.
- AI and ML models can enhance prediction accuracy by integrating diverse data.
- These advanced methods address limitations of traditional forensic approaches.
Conclusions:
- Molecular genetics and AI represent the future of reliable PMI estimation in forensic science.
- Further research can refine these methods for practical forensic applications.
- Improved PMI accuracy aids in solving forensic cases.
