重新定义死后间隔估计:需要基于证据的研究来建立科学与正义之间的桥梁
Maria J Teixeira1,2, Daniel J Barbosa1,2, Ricardo Jorge Dinis-Oliveira1,2,3,4
1UCIBIO - Applied Molecular Biosciences Research Unit, Translational Toxicology Research Laboratory, University Institute of Health Sciences (1H-TOXRUN, IUCS-CESPU), Gandra, Portugal.
Frontiers in microbiology
|November 3, 2025
概括
估计死后间隔 (PMI) 正在发展. 新的原子微生物学和人工智能方法比经典方法提供了更高的准确性,改善了法医科学和司法.
科学领域:
- 法医科学 法医科学 法医科学
- 生物化学 生物化学
- 微生物学 微生物学
背景情况:
- 经典的死后间隔 (PMI) 估计依赖于可观察到的变化,如死亡硬和分解.
- 这些传统方法需要广泛的专业知识,并受到环境和个体变化的影响.
- 最近的法医研究引入了创新的,资源密集型的方法,以更准确地估计PMI.
研究的目的:
- 提供对死后间隔 (PMI) 估计方法的全面审查.
- 批判性地检查经典方法,并突出尖端的方法.
- 强调多omics和AI在提高PMI准确性的潜力.
主要方法:
- 复习古典法医病理学技术.
- 对新兴的超原子微生物学和超原子化学方法的分析.
- 探索多种omics (metagenomics, metabolomics) 和人工智能 (AI) 的应用.
主要成果:
- 传统的PMI方法是基本的,但由于可变性而受到限制.
- 新兴的方法利用微生物继承 (超微生物组) 和生物化学变化.
- 多omics和AI展示了提高准确性和可靠性的潜力.
结论:
- 整合多主题和人工智能解决了PMI估计的局限性.
- 建议向基于证据的跨学科研究进行范式转变.
- 跨学科的合作和标准化的方法对于将研究转化为法医工具至关重要.
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