NGS plus bacterial culture: A more accurate method for diagnosing forensic-related nosocomial infections

Yu, Zhonghao; Xie, Qiankun; Zhao, Yifeng; Duan, Lizhong; Qiu, Pingming; Fan, Haoliang

doi:10.1016/j.legalmed.2021.101910

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Forensic microbiology first gained global recognition in 2001 as a result of the Bacillus anthracis attacks through the USA postal service. In previous studies on forensic microbiology, forensic microbiological technologies were not specifically described, except for agar cultures for bacteria and fungi combined with PCR for certain microorganisms (Aoyagi et al, 2009 ; Huys et al, 2012 ; Uchiyama et al, 2012 ; Tuomisto et al, 2013 ; Can et al, 2014 ; Hauther et al, 2015 ; Yu et al, 2021a , b ). To date, though more than 2,460 different species are presented in The Ribosomal Database Project stores (Maidak et al, 2000 ), most microbes in the environment have not been described and accessed for biotechnology.…”

Section: Development Of Forensic Microbiologymentioning

confidence: 99%

Trends in forensic microbiology: From classical methods to deep learning

Yuan

Wang

et al. 2023

Front. Microbiol.

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Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.

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Section: Development Of Forensic Microbiologymentioning

confidence: 99%

Trends in forensic microbiology: From classical methods to deep learning

Yuan

Wang

et al. 2023

Front. Microbiol.

View full text Add to dashboard Cite

show abstract

“…With the development of massive parallel sequencing (MPS) technology [13][14][15][16][17][18][19][20][21][22][23][24], emerging microhaplotypes (i.e., microhaps or MHs, generally <200 bp and consisting of two or more closely linked single nucleotide polymorphisms (SNPs) with three or more allelic combinations [7,25,26]) exhibit promising characteristics for further enhancing the deconvolution of DNA mixtures. MHs present some advantages over standard STRs: (1) a multiallelic nature and the absence of STR structures, which prevents Taq polymerase slippage and stutter peak generation; (2) balanced PCR amplification due to the similar lengths of MH amplicons (preferential PCR amplification for shorter alleles commonly occurs with STRs); and (3) lower mutation rates than STRs.…”

Section: Introductionmentioning

confidence: 99%