ABSTRACT
The succession of microbiota is closely associated with several essential factors, including race, sex, health condition, lifestyle, postmortem interval, etc., and it has great potential application value in forensic medicine. This paper summarizes recent studies on the forensic applications of the microbiome, including individual identification, geographical feature identification, origin identification of the tissue or body fluid, and postmortem interval estimation, and introduces the current machine learning algorithms for microbiology research based on next-generation sequencing data. In addition, the current problems facing forensic microbiomics such as the extraction and preservation of samples, construction of standardization and database, ethical review and practical applicability are discussed. Future multi-omics studies are expected to explore micro ecosystems from a comprehensive and dynamic perspective, to promote the development of forensic microbiomics application.
Subject(s)
Humans , Forensic Medicine , Autopsy , Microbiota/genetics , Algorithms , High-Throughput Nucleotide Sequencing , Postmortem ChangesABSTRACT
Traditional forensic identification relies on forensic experts to manually extract information and provide identification opinions based on medicine, biology and other fields of knowledge combined with personal work experience, which is not only time-consuming and require great effort, but also affected by subjective factors that are difficult to overcome. In the era of big data, the booming development of artificial intelligence brings new ideas to forensic medicine. In recent years, forensic researchers at home and abroad have conducted many studies based on artificial intelligence technology, such as face recognition, age and gender identification, DNA analysis, postmortem interval estimation, injury and cause of death identification, showing the feasibility and advantages of using artificial intelligence technology to solve forensic identification problems. As a new means of technology that has adapted to the development of the times, artificial intelligence has brought new vitality to forensic medicine, but at the same time also some new challenges. How to deal with these challenges scientifically and form a new mode of 'artificial intelligence plus forensic medicine' with artificial intelligence and forensic medicine developing collaboratively is a new direction for the development of forensic medicine in the era of big data.
Subject(s)
Artificial Intelligence , Autopsy , Forensic MedicineABSTRACT
Objective To explore the genetic background and structure of Urumqi Mongolians, the previously developed 39-AIM-InDels panel for ancestry inference was utilized in the present study. Methods The blood samples of 145 unrelated healthy Urumqi Mongolian individuals were collected and genotyped. The compositions of ancestry information of Urumqi Mongolians were studied with 17 different populations from three continents （East Asia, Europe and Africa） as reference populations. Then, multiple population genetics and bioinformatics analysis methods were applied, the Fst and DA values between matched populations were compared and analyzed, PCA analysis was performed and a phylogenetic tree was constructed. The proportions of ancestry information components of Urumqi Mongolians were analyzed with Structure software, etc. Results The ancestry information components of Urumqi Mongolian group in different intercontinental populations accounted for 89%, 7%, and 3% of East Asian, European, and African populations, respectively. Compared with other intercontinental populations, Urumqi Mongolian group and East Asian populations have lower Fst and DA values, and they were in the same cluster in PCA analysis as well. In a phylogenetic tree, the Urumqi Mongolian group was in the same branch as East Asian populations. Conclusion Urumqi Mongolian group had relatively close genetic relationships with East Asian populations, and the proportion of its East Asian ancestry was about 89%.