Research Progress of Metabolomics in Forensic Pathology / 法医学杂志

Metabolomics is an interdisciplinary subject that rose in the post-genomic era, which focuses on quantitative study of the response of living organisms to outside stimulation and pathophysiological changes, as well as multiple dynamic response of the level of in vivo metabolites caused by genetic mutation. It is extensively used in basic research of system biology, materia medica, clinical medicine, etc. In the forensic field, metabolomics mainly focuses on forensic toxicology, but with the generalization of certain techniques, it's foreseeable that metabolomics has a broad research prospect in forensic pathology. This article summarizes the major analysis techniques and methods of metabolomics, describes the research status of metabolomic techniques in the field of forensic pathology application research, including postmortem interval and death cause. Moreover, this article summarizes and discusses the potential applicable areas, in order to provide reference for relative research and application.

Application of Convolutional Neural Network in Identifying Different Levels of Isokinetic Exercise Efforts / 法医学杂志

Objective To develop a convolutional neural network （CNN） that can identify isokinetic knee exercises moment of force-time diagrams under different levels of efforts. Methods The 200 healthy young volunteers performed concentric isokinetic right knee flexion-extension reciprocating exercises with maximal effort as well as half the effort at angular velocities of 30°/s and 60°/s twice, respectively, with an interval of 45 min. The moment of force-time diagrams were collected. The 200 subjects were randomly divided into the training set （140 subjects） and the testing set （60 subjects）. The moment of force-time diagrams from subjects in the training set were used to train CNN model, and then the fully trained model was used to predict types of curves from the testing set. Random sampling of subjects along with subsequent development of models were performed 3 times. Results Under the circumstances of isokinetic knee exercises with maximal effort and half the effort, 2 400 moment of force-time diagrams were produced, respectively. Classification accuracy rates of the CNN models after the 3 trainings were 91.11%, 90.49% and 92.08%, respectively, and the average accuracy rate was 91.23%. Conclusion The CNN models developed in this study have a good effect on differentiating isokinetic moment of force-time diagrams of maximal effort exercises from those made with half the effort, which can contribute to identifying levels of efforts exerted by subjects during isokinetic knee exercises.