Advances in microbial metagenomics and artificial intelligence analysis in forensic identification

He, Qing; Niu, Xueli; Qi, Ruiqun; Liu, Min

doi:10.3389/fmicb.2022.1046733

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…Metagenomics has defined the study of whole genetic material recovered directly from a sample and through sequencing of marker amplicons –metabarcoding– or whole DNA random fragmentation –shotgun– ( Garza and Dutilh, 2015 ). Recent advances in high-throughput sequencing and the considerable reduction of analysis costs have boosted the development of microbiology and metagenomics ( He et al, 2022 ). Next-generation sequencing (NGS) technologies have made it possible to improve the coverage and depth of studies, allowing the rigorous analysis of complex microbial communities ( Almeida and de Martinis, 2019 ).…”

Section: Metagenomic As Revolution Microbiome Study Approachmentioning

confidence: 99%

Mammals’ sperm microbiome: current knowledge, challenges, and perspectives on metagenomics of seminal samples

et al. 2023

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Bacterial growth is highly detrimental to sperm quality and functionality. However, during the last few years, using sequencing techniques with a metagenomic approach, it has been possible to deepen the study of bacteria-sperm relationships and describe non-culturable species and synergistic and antagonistic relationships between the different species in mammalian animals. We compile the recent metagenomics studies performed on mammalian semen samples and provide updated evidence to understand the importance of the microbial communities in the results of sperm quality and sperm functionality of males, looking for future perspectives on how these technologies can collaborate in the development of andrological knowledge.

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Section: Metagenomic As Revolution Microbiome Study Approachmentioning

confidence: 99%

Mammals’ sperm microbiome: current knowledge, challenges, and perspectives on metagenomics of seminal samples

et al. 2023

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“…AI with machine learning already started transforming the field of forensic microbiology. Microbial succession during postmortem decay can be a promising tool for PMI detection [ 21 ]. Johnson et al have developed an AI algorithm to effectively investigate PMI by taking skin microbiota samples from nasal and ear canals [ 22 ].…”

Section: Reviewmentioning

confidence: 99%

“…Johnson et al have developed an AI algorithm to effectively investigate PMI by taking skin microbiota samples from nasal and ear canals [ 22 ]. Microbiome characterization can be a potential tool for performing one of the most critical tasks of individual identification, as, theoretically, every individual carries a unique set of microbial communities that remains relatively stable over long periods [ 21 ]. Yang et al identified individuals with an accuracy of 90% using random forest machine learning by integrating Cutibacterium acnes 16S rRNA genotype with skin microbiome profile data [ 23 ].…”

Section: Reviewmentioning

confidence: 99%

Evolution of Diagnostic and Forensic Microbiology in the Era of Artificial Intelligence

Mishra,

Khan,

Das

et al. 2023

Cureus

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Diagnostic microbiology plays a vital role in managing infectious diseases, combating antimicrobial resistance, and containment of outbreaks. During the fourth industrial revolution, when artificial intelligence (AI) became an essential part of our day-to-day lives, its integration into healthcare would further revolutionize our knowledge and potential. Although in the budding stage, AI with machine learning is being increasingly utilized in various aspects of diagnostic microbiology. It can handle large datasets that are difficult to analyze manually. Researchers have developed and demonstrated several machine-learning algorithms for interpreting bacterial cultures, conducting image analysis for microbial detection, and predicting antimicrobial susceptibility patterns. Thus, AI may most likely be the ultimate solution to the ever-increasing demand for improved results with shorter turnaround times. AI can also assist forensic microbiologists in crime scene investigations, as it can guide individual identification, cause and time since death, and manner of death. This review summarizes the application of AI in diagnostic microbiology for performing diverse sets of microbial investigations and is an essential aid in forensic microbiology.

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“…It is widely recognized that microbiota can be utilized for PMI estimation [6][7][8][9][10][11][12][13][14]. During the fresh stage, cellular macromolecules are released shortly after death, and the microbiota play a crucial role in breaking down these macromolecules into simpler compounds [15].…”

Section: Introductionmentioning

confidence: 99%

“…Numerous researchers have conducted comprehensive investigations into microbial succession, focusing predominantly on murine models [8,13,[19][20][21][22][23][24][25], specifically rats and mice [2,26]. Metcalf et al found a "microbial clock" with the capacity to estimate the PMI with a margin of error approximating ±3 days [2], while the experiment was carried out under rigorously controlled circumstances, utilizing experimental mouse models, thus necessitating judicious interpretation when extrapolating these findings to authentic, realworld scenarios [27].…”

Section: Introductionmentioning

confidence: 99%

Changes in Microbial Communities Using Pigs as a Model for Postmortem Interval Estimation

Yang,

Zhang,

et al. 2023

Microorganisms

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Microbial communities can undergo significant successional changes during decay and decomposition, potentially providing valuable insights for determining the postmortem interval (PMI). The microbiota produce various gases that cause cadaver bloating, and rupture releases nutrient-rich bodily fluids into the environment, altering the soil microbiota around the carcasses. In this study, we aimed to investigate the underlying principles governing the succession of microbial communities during the decomposition of pig carcasses and the soil beneath the carcasses. At early decay, the phylum Firmicutes and Bacteroidota were the most abundant in both the winter and summer pig rectum. However, Proteobacteria became the most abundant in the winter pig rectum in late decay. Using genus as a biomarker to estimate the PMI could get the MAE from 1.375 days to 2.478 days based on the RF model. The abundance of bacterial communities showed a decreasing trend with prolonged decomposition time. There were statistically significant differences in microbial diversity in the two periods (pre-rupture and post-rupture) of the four groups (WPG 0–8Dvs. WPG 16–40D, p < 0.0001; WPS 0–16Dvs. WPS 24–40D, p = 0.003; SPG 0D vs. SPG 8–40D, p = 0.0005; and SPS 0D vs. SPS 8–40D, p = 0.0208). Most of the biomarkers in the pre-rupture period belong to obligate anaerobes. In contrast, the biomarkers in the post-rupture period belong to aerobic bacteria. Furthermore, the genus Vagococcus shows a similar increase trend, whether in winter or summer. Together, these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating the PMI.

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Advances in microbial metagenomics and artificial intelligence analysis in forensic identification

Cited by 8 publications

References 26 publications

Mammals’ sperm microbiome: current knowledge, challenges, and perspectives on metagenomics of seminal samples

Mammals’ sperm microbiome: current knowledge, challenges, and perspectives on metagenomics of seminal samples

Evolution of Diagnostic and Forensic Microbiology in the Era of Artificial Intelligence

Changes in Microbial Communities Using Pigs as a Model for Postmortem Interval Estimation

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