RESUMEN
OBJECTIVES: To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database, to explore research hotspots and developmental trends. METHODS: A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the literature measuring tool CiteSpace. The authors, institution, country (region), title, journal, keywords, cited references and other information of relevant literatures were analyzed. RESULTS: A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries (regions) were identified, with the number of articles published showing an increasing trend year by year. Among them, the United States had the highest number of publications and China ranked the second. Academy of Forensic Science had the highest number of publications among the institutions. Forensic Science International, Journal of Forensic Sciences, International Journal of Legal Medicine ranked high in publication and citation frequency. Through the analysis of keywords, it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technology for sex and age estimation, cause of death analysis, postmortem interval estimation, individual identification and so on. CONCLUSIONS: It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research. Exploring the combination of advanced artificial intelligence technologies with forensic research will be a hotspot and direction for future research.
Asunto(s)
Inteligencia Artificial , Medicina Legal , Autopsia , China , Ciencias ForensesRESUMEN
OBJECTIVES: To screen biomarkers for forensic identification of acute myocardial infarction (AMI) by non-targeted metabolomic studies on changes of urine metabolites in rats with AMI. METHODS: The rat models of the sham surgery group, AMI group and hyperlipidemia + acute myocardial infarction (HAMI) group were established. Ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) was used to analyze the changes of urine metabolic spectrometry in AMI rats. Principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis were used to screen differential metabolites. The MetaboAnalyst database was used to analyze the metabolic pathway enrichment and access the predictive ability of differential metabolites. RESULTS: A total of 40 and 61 differential metabolites associated with AMI and HAMI were screened, respectively. Among them, 22 metabolites were common in both rat models. These small metabolites were mainly concentrated in the niacin and nicotinamide metabolic pathways. Within the 95% confidence interval, the area under the curve (AUC) values of receiver operator characteristic curve for N8-acetylspermidine, 3-methylhistamine, and thymine were greater than 0.95. CONCLUSIONS: N8-acetylspermidine, 3-methylhistamine, and thymine can be used as potential biomarkers for AMI diagnosis, and abnormal metabolism in niacin and nicotinamide may be the main causes of AMI. This study can provide reference for the mechanism and causes of AMI identification.