Suppr超能文献

微生物取证:新突破与未来展望。

Microbial forensics: new breakthroughs and future prospects.

机构信息

i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135, Porto, Portugal.

Ipatimup - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Rua Júlio Amaral de Carvalho,45, 4200-135, Porto, Portugal.

出版信息

Appl Microbiol Biotechnol. 2018 Dec;102(24):10377-10391. doi: 10.1007/s00253-018-9414-6. Epub 2018 Oct 9.

Abstract

Recent advances in genetic data generation, through massive parallel sequencing (MPS), storage and analysis have fostered significant progresses in microbial forensics (or forensic microbiology). Initial applications in circumstances of biocrime, bioterrorism and epidemiology are now accompanied by the prospect of using microorganisms (i) as ancillary evidence in criminal cases; (ii) to clarify causes of death (e.g., drownings, toxicology, hospital-acquired infections, sudden infant death and shaken baby syndromes); (iii) to assist human identification (skin, hair and body fluid microbiomes); (iv) for geolocation (soil microbiome); and (v) to estimate postmortem interval (thanatomicrobiome and epinecrotic microbial community). When compared with classical microbiological methods, MPS offers a diverse range of advantages and alternative possibilities. However, prior to its implementation in the forensic context, critical efforts concerning the elaboration of standards and guidelines consolidated by the creation of robust and comprehensive reference databases must be undertaken.

摘要

近年来,通过大规模平行测序(MPS),遗传数据的生成、存储和分析取得了重大进展,促进了法医微生物学(或法医微生物学)的发展。最初在生物犯罪、生物恐怖主义和流行病学方面的应用,现在有望将微生物用作刑事案件中的辅助证据;(ii) 以澄清死因(例如溺水、毒理学、医院获得性感染、婴儿猝死和摇晃婴儿综合征);(iii) 协助身份识别(皮肤、头发和体液微生物组);(iv) 进行地理位置定位(土壤微生物组);(v) 估计死后间隔时间(尸微生物组和尸斑微生物群落)。与经典的微生物学方法相比,MPS 提供了广泛的优势和替代可能性。然而,在将其应用于法医环境之前,必须做出关键性的努力,以制定标准和准则,并通过创建强大和全面的参考数据库来加以巩固。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3ca/7080133/c28a0c52959a/253_2018_9414_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验