法医学杂志最新文献

In the process of murder investigation, it is of great significance to find the discarded and buried human remains accurately. The main methods of searching for human remains include human visual search, aerial detection, geophysical technology, remote imaging technology and canine olfactory search technique. Canine olfactory search for human remains is a recognized time-effective and non-invasive search method, making dogs the most valuable search tool in forensic investigation. By systematically reviewing and summarizing relevant literature, and based on the theory of volatile organic compound produced by the decomposition of human remains, this paper explores the basic principle of the canine olfactory search technique for human remains. This paper also reviews the application of training canine search technique for human remains in forensic investigation by using human blood, tissue, cadaver putrefying fluid and odor substitutes as sniffing sources. The application prospect of canine olfactory search for human remains was prospected from the perspectives of detection of volatile organic compound during cadaver decay, development of odor substitutes and adsorption devices, and technology tactics used in canine training and use, to provide references for the relevant research of canine olfactory search for human remains in China.

The quality of Japanese forensic experts has been widely recognized around the world, which cannot be separated from the "ripple effect" caused by the rapid rise of the modern forensic education in Japan. By continuously adopting foreign forensic education resources and teaching experience, it has finally formed a forensic professional talent training model with a clear hierarchy of basic education and professional training, as well as classroom teaching and case studies complementing each other; and it continuously improves the comprehensive quality of practitioners through domestic training and international exchange and cooperation, providing talented professionals for the development of the Japanese forensic industry. In this context, this article takes the development history of Japanese forensic medicine as the starting point to study how it gradually formed the embryonic form of forensic education in modern times. Based on this, it analyzes the characteristics of Japan's modern forensic medicine talent training model, summarizes excellent experiences for localized transformation, such as emphasizing the role of practical teaching, exerting the effectiveness of vocational skills training, and promoting international exchange and cooperation, to provide reference and inspiration for the training of relevant professional talents in China.

Objectives: To explore the association between violent behaviors and emotions in individuals with mental disorders, to evaluate the application value of facial expression analysis technology in violence risk assessment of individuals with mental disorders in supervised settings, and to provide a reference for violence risk assessment.

Methods: Thirty-nine male individuals with mental disorders in supervised settings were selected, the participant risk of violence, cognitive function, psychiatric symptoms and severity were assessed using the Modified Overt Aggression Scale (MOAS), the Historical, Clinical, Risk Management-Chinese version(HCR-CV), the Positive and Negative Syndrome Scale (PANSS) and the Brief Psychiatric Rating Scale (BPRS). An emotional arousal was performed on the participants and the intensity of their emotions and facial expression action units was recorded before, during and after the arousal. One-way analysis of variance (ANOVA) was used to compare the differences in the intensity of emotions and facial expression action units before, during and after the arousal. Pearson correlation analysis was used to calculate the correlations between the intensity of the seven basic emotional facial expressions and the scores of the assessment scales.

Results: The intensity difference of sadness, surprise and fear in different time periods was statistically significant (P<0.05). The intensity of the left medial eyebrow lift action unit was found significantly different before and after the emotional arousal (P<0.05). The intensity of anger was positively correlated with the Modified Overt Aggression Scale score throughout the experiment (P<0.05).

Conclusions: Eye action units such as eyebrow lifting, eyelid tightening and upper eyelid lifting can be used as effective action units to identify sadness, anger and other negative emotions associated with violent behaviors. Facial expression analysis technology can be used as an auxiliary tool to assess the potential risk of violence in individuals with mental disorders in supervised settings.

Objectives: To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography (CBCT) images, and to compare and analyze the estimation results.

Methods: A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms (K-nearest neighbor, ridge regression, and decision tree) and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed.

Results: The K-nearest neighbor model (R²=0.779) and the ridge regression model (R²=0.729) outperformed stepwise regression (R²=0.617), while the decision tree model (R²=0.494) showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume.

Conclusions: Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research.