揭开死亡后的时间:对死后间隔估计中的多omics方法的全面审查
Mian Sahib Zar1, Afshan Bibi2, Muhammad Shoaib Akhtar3
1Hamdard College of Health Sciences (HCHS), Buner, Khyber Pakhtunkhwa, Pakistan; School of Biology and Basic Medical Sciences, Soochow University, Suzhou, PR China.
Forensic science international
|December 18, 2025
概括
在法医中,估计死后间隔 (PMI) 是至关重要的. 多omics技术通过分析分子变化,改善死亡时间估计,提供比传统方法更准确的方法.
科学领域:
- 法医科学 法医科学 法医科学
- 分子生物学分子生物学
背景情况:
- 死后间隔 (PMI) 的估计对于法医调查至关重要.
- 传统的PMI方法 (死亡强度,分解) 由于可变性而面临限制.
研究的目的:
- 在完善PMI估计时审查多omics技术的应用.
- 探索机器学习和便携式技术的整合,以加强法医分析.
主要方法:
- 对包括基因组学,蛋白质组学和代谢组学在内的多组学方法的审查.
- 讨论机器学习和便携式技术的整合.
- 在法医中考虑分子数据的伦理方面.
主要成果:
- 与传统方法相比,多组学提供了对死后变化的更细致的理解.
- 将多学科与机器学习和便携式技术相结合,有望提高准确性和可访问性.
- 伦理考虑对于敏感的法医数据至关重要.
结论:
- 多omics技术代表了法医PMI估计的重大进步.
- 跨学科的合作和创新是推动法医科学发展的关键.
- 多omics准备成为现代法医调查的基本工具.
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