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Xu G, Teng X, Gao XH, Zhang L, Yan H, Qi RQ. Advances in machine learning-based bacteria analysis for forensic identification: identity, ethnicity, and site of occurrence. Front Microbiol 2023; 14:1332857. [PMID: 38179452 PMCID: PMC10764511 DOI: 10.3389/fmicb.2023.1332857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/05/2023] [Indexed: 01/06/2024] Open
Abstract
When faced with an unidentified body, identifying the victim can be challenging, particularly if physical characteristics are obscured or masked. In recent years, microbiological analysis in forensic science has emerged as a cutting-edge technology. It not only exhibits individual specificity, distinguishing different human biotraces from various sites of occurrence (e.g., gastrointestinal, oral, skin, respiratory, and genitourinary tracts), each hosting distinct bacterial species, but also offers insights into the accident's location and the surrounding environment. The integration of machine learning with microbiomics provides a substantial improvement in classifying bacterial species compares to traditional sequencing techniques. This review discusses the use of machine learning algorithms such as RF, SVM, ANN, DNN, regression, and BN for the detection and identification of various bacteria, including Bacillus anthracis, Acetobacter aceti, Staphylococcus aureus, and Streptococcus, among others. Deep leaning techniques, such as Convolutional Neural Networks (CNN) models and derivatives, are also employed to predict the victim's age, gender, lifestyle, and racial characteristics. It is anticipated that big data analytics and artificial intelligence will play a pivotal role in advancing forensic microbiology in the future.
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Affiliation(s)
- Geyao Xu
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Xianzhuo Teng
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xing-Hua Gao
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Li Zhang
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Hongwei Yan
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Rui-Qun Qi
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
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Dinis-Oliveira RJ, Azevedo RMS. ChatGPT in forensic sciences: a new Pandora's box with advantages and challenges to pay attention. Forensic Sci Res 2023; 8:275-279. [PMID: 38405625 PMCID: PMC10894065 DOI: 10.1093/fsr/owad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 10/19/2023] [Indexed: 02/27/2024] Open
Abstract
ChatGPT is a variant of the generative pre-trained transformer (GPT) language model that uses large amounts of text-based training data and a transformer architecture to generate human-like text adjusted to the received prompts. ChatGPT presents several advantages in forensic sciences, namely, constituting a virtual assistant to aid lawyers, judges, and victims in managing and interpreting forensic expert data. But what would happen if ChatGPT began to be used to produce forensic expertise reports? Despite its potential applications, the use of ChatGPT and other Large Language Models and artificial intelligence tools in forensic writing also poses ethical and legal concerns, which are discussed in this perspective together with some expected future perspectives.
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Affiliation(s)
- Ricardo J Dinis-Oliveira
- 1H-TOXRUN—One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS-CESPU), CESPU, CRL, Gandra, Portugal
- Department of Public Health and Forensic Sciences and Medical Education, Faculty of Medicine, University of Porto, Porto, Portugal
- UCIBIO/REQUIMTE, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, R. Jorge Viterbo Ferreira, n° 33-A, Lisboa, Portugal
- FOREN—Forensic Science Experts, Dr. Mário Moutinho Avenue, n.° 33-A, Lisbon, Portugal
| | - Rui M S Azevedo
- 1H-TOXRUN—One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS-CESPU), CESPU, CRL, Gandra, Portugal
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Yang F, Zhang X, Hu S, Nie H, Gui P, Zhong Z, Guo Y, Zhao X. Changes in Microbial Communities Using Pigs as a Model for Postmortem Interval Estimation. Microorganisms 2023; 11:2811. [PMID: 38004822 PMCID: PMC10672931 DOI: 10.3390/microorganisms11112811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/06/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
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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Affiliation(s)
- Fan Yang
- Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China; (F.Y.); (S.H.); (H.N.)
| | - Xiangyan Zhang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (X.Z.); (Y.G.)
| | - Sheng Hu
- Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China; (F.Y.); (S.H.); (H.N.)
| | - Hao Nie
- Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China; (F.Y.); (S.H.); (H.N.)
| | - Peng Gui
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China; (P.G.); (Z.Z.)
| | - Zengtao Zhong
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China; (P.G.); (Z.Z.)
| | - Yadong Guo
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (X.Z.); (Y.G.)
| | - Xingchun Zhao
- Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China; (F.Y.); (S.H.); (H.N.)
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Cláudia-Ferreira A, Barbosa DJ, Saegeman V, Fernández-Rodríguez A, Dinis-Oliveira RJ, Freitas AR. The Future Is Now: Unraveling the Expanding Potential of Human (Necro)Microbiome in Forensic Investigations. Microorganisms 2023; 11:2509. [PMID: 37894167 PMCID: PMC10608847 DOI: 10.3390/microorganisms11102509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/24/2023] [Accepted: 10/03/2023] [Indexed: 10/29/2023] Open
Abstract
The relevance of postmortem microbiological examinations has been controversial for decades, but the boom in advanced sequencing techniques over the last decade is increasingly demonstrating their usefulness, namely for the estimation of the postmortem interval. This comprehensive review aims to present the current knowledge about the human postmortem microbiome (the necrobiome), highlighting the main factors influencing this complex process and discussing the principal applications in the field of forensic sciences. Several limitations still hindering the implementation of forensic microbiology, such as small-scale studies, the lack of a universal/harmonized workflow for DNA extraction and sequencing technology, variability in the human microbiome, and limited access to human cadavers, are discussed. Future research in the field should focus on identifying stable biomarkers within the dominant Bacillota and Pseudomonadota phyla, which are prevalent during postmortem periods and for which standardization, method consolidation, and establishment of a forensic microbial bank are crucial for consistency and comparability. Given the complexity of identifying unique postmortem microbial signatures for robust databases, a promising future approach may involve deepening our understanding of specific bacterial species/strains that can serve as reliable postmortem interval indicators during the process of body decomposition. Microorganisms might have the potential to complement routine forensic tests in judicial processes, requiring robust investigations and machine-learning models to bridge knowledge gaps and adhere to Locard's principle of trace evidence.
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Affiliation(s)
- Ana Cláudia-Ferreira
- 1H-TOXRUN, One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS), CESPU, CRL, 4585-116 Gandra, Portugal; (A.C.-F.); (R.J.D.-O.)
| | - Daniel José Barbosa
- 1H-TOXRUN, One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS), CESPU, CRL, 4585-116 Gandra, Portugal; (A.C.-F.); (R.J.D.-O.)
- Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, 4200-135 Porto, Portugal
| | - Veroniek Saegeman
- Department of Infection Control and Prevention, University Hospitals Leuven, 3000 Leuven, Belgium;
| | - Amparo Fernández-Rodríguez
- Microbiology Laboratory, Biology Service, Institute of Toxicology and Forensic Sciences, 28232 Madrid, Spain;
| | - Ricardo Jorge Dinis-Oliveira
- 1H-TOXRUN, One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS), CESPU, CRL, 4585-116 Gandra, Portugal; (A.C.-F.); (R.J.D.-O.)
- Department of Public Health and Forensic Sciences, and Medical Education, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
- UCIBIO—Applied Molecular Biosciences Unit, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Ana R. Freitas
- 1H-TOXRUN, One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS), CESPU, CRL, 4585-116 Gandra, Portugal; (A.C.-F.); (R.J.D.-O.)
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- UCIBIO—Applied Molecular Biosciences Unit, Laboratory of Microbiology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
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Piraianu AI, Fulga A, Musat CL, Ciobotaru OR, Poalelungi DG, Stamate E, Ciobotaru O, Fulga I. Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine. Diagnostics (Basel) 2023; 13:2992. [PMID: 37761359 PMCID: PMC10529115 DOI: 10.3390/diagnostics13182992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize the world. In forensic medicine and pathology, algorithms play a crucial role in data analysis, pattern recognition, anomaly identification, and decision making. This review explores the diverse applications of AI in forensic medicine, encompassing fields such as forensic identification, ballistics, traumatic injuries, postmortem interval estimation, forensic toxicology, and more. RESULTS A thorough review of 113 articles revealed a subset of 32 papers directly relevant to the research, covering a wide range of applications. These included forensic identification, ballistics and additional factors of shooting, traumatic injuries, post-mortem interval estimation, forensic toxicology, sexual assaults/rape, crime scene reconstruction, virtual autopsy, and medical act quality evaluation. The studies demonstrated the feasibility and advantages of employing AI technology in various facets of forensic medicine and pathology. CONCLUSIONS The integration of AI in forensic medicine and pathology offers promising prospects for improving accuracy and efficiency in medico-legal practices. From forensic identification to post-mortem interval estimation, AI algorithms have shown the potential to reduce human subjectivity, mitigate errors, and provide cost-effective solutions. While challenges surrounding ethical considerations, data security, and algorithmic correctness persist, continued research and technological advancements hold the key to realizing the full potential of AI in forensic applications. As the field of AI continues to evolve, it is poised to play an increasingly pivotal role in the future of forensic medicine and pathology.
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Affiliation(s)
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania; (A.-I.P.); (C.L.M.); (O.-R.C.); (D.G.P.); (O.C.); (I.F.)
| | | | | | | | - Elena Stamate
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania; (A.-I.P.); (C.L.M.); (O.-R.C.); (D.G.P.); (O.C.); (I.F.)
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