Bone age is an important indicator of human growth and development, which can objectively reflect the growth level and maturity of individuals. Traditional manual bone age assessment usually compares the X-ray of the left wrist with the reference standard to obtain the corresponding bone age. This method is time-consuming and its results vary with different observers. In recent years, with the continuous development of computer science, bone age assessment has began to change from traditional manual assessment to automatic assessment. Although there has already been numerous researches on automatic bone age assessment, most of them are still in the experimental stage. This paper reviews related research and progress on automatic bone age assessment at home and abroad in recent years, in order to provide reference and research ideas for relevant researchers.Mitochondria are the special organelle in eukaryotic cells. Their main functions are to synthesize energy required for cell activity by oxidative phosphorylation. Most of the oxygen absorbed by the body is consumed in the mitochondria. The precise diagnosis of mechanical asphyxia is one of the difficulties in forensic pathology practice. Forensic pathologists have been trying to find a reliable and sensitive marker for the diagnosis of mechanical asphyxia. Mitochondria are very sensitive to hypoxic environments, and the markers of mitochondrion damage can be used as a basis for the diagnosis of mechanical asphyxia. The purpose of this paper is to review the research progress on mitochondrial damage in hypoxic environments and to explore the possibility of using markers of mitochondrion damage in forensic pathological practice.Objective To discuss the application of artificial intelligence automatic diatom identification system in practical cases, to provide reference for quantitative diatom analysis using the system and to validate the deep learning model incorporated into the system. Methods Organs from 10 corpses in water were collected and digested with diatom nitric acid; then the smears were digitally scanned using a digital slide scanner and the diatoms were tested qualitatively and quantitatively by artificial intelligence automatic diatom identification system. Results The area under the curve （AUC） of the receiver operator characteristic （ROC） curve of the deep learning model incorporated into the artificial intelligence automatic diatom identification system, reached 98.22% and the precision of diatom identification reached 92.45%. Conclusion The artificial intelligence automatic diatom identification system is able to automatically identify diatoms, and can be used as an auxiliary tool in diatom testing in practical cases, to provide reference to drowning diagnosis.Objective To analyze the differences in accuracy of different eye movement parameters in distinguishing the cooperation and non-cooperation during image completion test of patients with mental disorders caused by craniocerebral trauma. Methods One hundred and forty cases of patients with mental disorders caused by craniocerebral trauma who took psychiatric impairment assessments were collected. The 21 pictures from "image completion" of Wechsler intelligence test were used as stimulating pictures, then divided into cooperation group and non-cooperation group according to binomial forced-choice digit memory test and expert opinions. The eye movement parameters of research subjects during completion of images were obtained by the SMI eye-tracker. The accuracy of eye movement parameters in distinguishing the cooperation or non-cooperation of patients with mental disorders caused by craniocerebral trauma in psychiatric impairment assessments were evaluated by the ROC curve. Results During the process of the image completion test, the area under curve （AUC） value of frequency of blink, frequency of fixation, pupil size, frequency of saccade, latency of saccade, average acceleration of saccade, the average and peak longitudinal velocity of saccade was above 0.5. When it comed to a specific stimulating picture, the AUC value of frequency of blink in looking at a specific stimulating picture could be above 0.8, and the AUC value of X axis diameter of pupil size could be above 0.7. https://www.selleckchem.com/TGF-beta.html Conclusion The accuracy of eye movement parameters in distinguishing the cooperation or disguise of patients with mental disorders caused by craniocerebral trauma is related with the stimulating picture. The accuracy of frequency of blink in distinguishing cooperation and non-cooperation is better than that of other eye movement parameters.Objective To investigate the application value of eye tracking in lie detection. Methods The 40 subjects were randomly divided into two groups. The pupil diameter, fixation duration, points of fixation and blink frequency of the subjects in the experimental group in observing target stimulation and non-target stimulation were recorded with eye tracker after they accomplished the mock crime. The eye movement parameters of subjects in the control group were directly collected. The differences in eye movement parameters of the experimental group and the control group in observing target stimulation and non-target stimulation were analyzed by t-test. Pearson coefficient analysis of correlation between eye movement parameters that had differences was conducted. The effectiveness of eye movement parameters to distinguish between the experimental group and the control group was calculated by the receiver operator characteristic （ROC） curve. Results Participants from the experimental group had shorter average pupil diameter, longer average fixation duration and fewer fixation points （P less then 0.05）, but the differences in blink frequency had no statistical significance. The differences in the above indicators of the control group in observing target stimulation and non-target stimulation had no statistical significance. The average fixation duration showed a negative correlation with fixation points （r=-0.255, P less then 0.05）; the average fixation duration showed a negative correlation with average pupil diameter （r=-0.218, P less then 0.05）; the fixation points showed a positive correlation with average pupil diameter （r=0.09, P less then 0.05）. The area under the curve of average pupil diameter, average fixation duration and fixation points was 0.603, 0.621 and 0.580, respectively. Conclusion The average pupil diameter, average fixation duration and fixation points obtained by the eye tracker under laboratory conditions can be used to detect lies.