计算机科学
人工智能
死后变化
微生物群
法医学
机器学习
法医人类学
钥匙(锁)
专家系统
区间(图论)
数据科学
区间估计
模式识别(心理学)
客观性(哲学)
尸僵
数据挖掘
法医昆虫学
构造(python库)
作者
Gulgena R. Mustafina,Kirill O. Kuznetsov,Svetlana A. Kosobutskaya,Maksim A. Sokolovskiy,Alvina I. Semenova,Valeriy N. Korotun,Maksim A. Sokolovkiy,Valery N. Korotun
出处
期刊:Судебная медицина
[Association of Forensic Medical Experts]
日期:2025-10-27
卷期号:11 (3): 276-288
摘要
Determination of the postmortem interval remains one of the key tasks in forensic practice, as the accuracy of this assessment directly affects the objectivity of expert conclusions and the effectiveness of investigative procedures. Traditional postmortem interval estimation methods based on morphological indicators and thermometry have limited reliability, especially in the late postmortem period. Current research focuses on developing innovative approaches employing molecular technologies, microbiome analysis, multiomic strategies, and the integration of artificial intelligence for large-scale data processing. This review summarizes modern methods for estimating the postmortem interval, including nucleic acid (DNA and RNA) analysis, proteomic and metabolomic approaches, and the study of microbiome changes. Particular attention is given to immunohistochemical markers, mass spectrometry, and nuclear magnetic resonance for quantification of biochemical processes in tissues and biological fluids. The prospects of applying molecular and chemical methods in forensic entomology during the late postmortem period are highlighted. A separate section discusses the use of machine and deep learning algorithms to construct predictive models based on multimodal data, including microbiome profiles, imaging features, and environmental parameters. Examples of combined approaches integrating biomolecular markers and computational technologies are presented, enabling more accurate estimation of the postmortem interval during both early and late postmortem periods.
科研通智能强力驱动
Strongly Powered by AbleSci AI