Comparison of Application of MD-VF-Auto SEM Method and Plankton Gene Multiplex PCR System in the Diagnosis of Drowning / 法医学杂志

OBJECTIVES@#To compare the application effect of microwave digestion - vacuum filtration - automated scanning electron microscopy (MD-VF-Auto SEM) method and plankton gene multiplex PCR system in the diagnosis of drowning.@*METHODS@#Lung, liver and kidney tissue of 10 non-drowning cases and 50 drowning cases were prepared for further MD-VF-Auto SEM method analysis and plankton gene multiplex PCR system analysis. The positive detection rate of the two methods in each tissue was calculated.@*RESULTS@#The positive rate of the MD-VF-Auto SEM method detecting diatoms in drowning cases was 100%, and few diatoms were detected in the liver and kidney tissues of 6 non-drowning cases. By using the plankton gene multiplex PCR system, the diatom positive rate of drowning cases was 84%, and all the non-drowning cases were negative. There were significant differences in the positive rate of the liver, kidney tissues between MD-VF-Auto SEM method and plankton gene multiplex PCR system (P<0.05), as well as the total positive rate of cases. However, no significant differences were found in the positive rates of lung tissues (P>0.05).@*CONCLUSIONS@#MD-VF-Auto SEM method is more sensitive than plankton gene multiplex PCR system in diatom test. But the plankton gene multiplex PCR system can also detect plankton other than diatoms. Combination of the two methods can provide a more reliable basis for the diagnosis of drowning.

Application of Diatoms Quantitative Analysis in the Diagnosis of Drowning / 法医学杂志

OBJECTIVES@#To retrospectively analyze diatom test cases of corpses in water and discuss the value of quantitative analysis of diatoms in the diagnosis of drowning.@*METHODS@#A total of 490 cases of water-related death were collected. They were divided into drowning group and postmortem immersion group according to the cause of death. Diatoms in lung, liver, kidney tissue and water sample were analyzed quantitatively by microwave digestion-vacuum filtration-automated scanning electron microscopy (MD-VF-Auto SEM) method. The ratios of content of diatoms in lung tissue and water sample (CL/CD) were calculated.@*RESULTS@#The results of diatom test for three organs (lung, liver and kidney) were all positive in 400 cases (85.5%); the content of diatom in lung, liver, kidney tissues, and water samples of drowning group were (113 235.9±317 868.1), (26.7±75.6), (23.3±52.2) and (12 113.3±21 760.0) cells/10 g, respectively; the species of diatom were (7.5±2.8), (2.6±1.9), (2.9±2.1) and (8.9±3.0) types, respectively; the CL/CD of drowning group and postmortem immersion group were (100.6±830.7) and (0.3±0.4), respectively.@*CONCLUSIONS@#Quantitative analysis of diatoms can provide supportive evidence for the diagnosis of drowning, and the parameter CL/CD can be introduced into the analysis to make a more accurate diagnosis of drowning.

Change Rule of Diatom in Zhangweixin River in Dezhou / 法医学杂志

OBJECTIVES@#To study the annual variation of diatoms in Zhangweixin River, to provide theoretical support by using diatom examination to estimate the time and place of the corpse entering water, and to establish a diatom database.@*METHODS@#Samples were taken from 4 sampling sites in Decheng section of Zhangweixin River for 12 consecutive months. Non-metric multi-dimensional scaling (NMDS) analysis was performed on the species and content of diatom samples. Based on the sampling site of Tianqu Road, Sprensen similarity coefficient analysis was conducted with the data of other 3 sites in Decheng section and Leling section of Zhangweixin River and Ningjin section in previous studies.@*RESULTS@#The number of diatom species was positively correlated with diatom content. The average diatom content in different months ranged from 1 054 to 13 041/10 mL, and the species composition ranged from 8 to 16, with differences in dominant species. The similarity coefficient of diatom species within 2 km were all higher than 0.956 52, which could not be distinguished effectively. The similarity coefficients of Leling section and Ningjin section were 0.736 84 and 0.588 24 respectively, which could be effectively distinguished.@*CONCLUSIONS@#The species and content of diatom vary in different months in Zhangweixin River, and the composition of diatom species is different in different basins, which can provide reference for estimating the time and place of the corpse entering water in the river.

Effects of Digestive Temperature and Time on Diatom Test / 法医学杂志

OBJECTIVES@#To study the effects of temperature and time for diatoms digestion and find out suitable digestive temperature and time.@*METHODS@#Eighty pieces of liver tissues were collected, each piece of tissue was 2 g, and 2 mL Pearl River water was added to each piece of tissue. The digestion temperature was set at 100 ℃, 120 ℃, 140 ℃, 160 ℃, 180 ℃ and the digestion time was set at 40, 50, 60, 70, 80 min. The liver tissue and water mixture were divided into 8 portions in each group. All the samples were tested by microwave digestive - vacuum filtration - automated scanning electron microscopy method. The quantity of diatom recovered and the quality of residue on the membrane were recorded.@*RESULTS@#When the digestion time was set to 60 min, there were statistically significant differences in the number of diatoms recovered at different temperatures (P<0.05). The maximum number of diatoms recovered was (28 797.50±6 009.67) at 140 ℃, and the minimum residue was (0.60±0.28) mg at 180 ℃. When the digestion temperature was set at 140 ℃, there were statistically significant differences in the number of diatoms recovered at different digestion times (P<0.05). The number of diatoms recovered was the highest at 40 min, it was up to (20 650.88±1 950.29), and the residue quality of each group had no statistical significance among different digestion time groups(P>0.05).@*CONCLUSIONS@#The effect of diatom digestion is related to temperature and time. When the digestion temperature was 140 ℃ and the digestion time was 40, 50 and 60 min, it is favorable for diatom test.

Construction and Application of YOLOv3-Based Diatom Identification Model of Scanning Electron Microscope Images / 法医学杂志

OBJECTIVES@#To construct a YOLOv3-based model for diatom identification in scanning electron microscope images, explore the application performance in practical cases and discuss the advantages of this model.@*METHODS@#A total of 25 000 scanning electron microscopy images were collected at 1 500× as an initial image set, and input into the YOLOv3 network to train the identification model after experts' annotation and image processing. Diatom scanning electron microscopy images of lung, liver and kidney tissues taken from 8 drowning cases were identified by this model under the threshold of 0.4, 0.6 and 0.8 respectively, and were also identified by experts manually. The application performance of this model was evaluated through the recognition speed, recall rate and precision rate.@*RESULTS@#The mean average precision of the model in the validation set and test set was 94.8% and 94.3%, respectively, and the average recall rate was 81.2% and 81.5%, respectively. The recognition speed of the model is more than 9 times faster than that of manual recognition. Under the threshold of 0.4, the mean recall rate and precision rate of diatoms in lung tissues were 89.6% and 87.8%, respectively. The overall recall rate in liver and kidney tissues was 100% and the precision rate was less than 5%. As the threshold increased, the recall rate in all tissues decreased and the precision rate increased. The F1 score of the model in lung tissues decreased with the increase of threshold, while the F1 score in liver and kidney tissues with the increase of threshold.@*CONCLUSIONS@#The YOLOv3-based diatom electron microscope images automatic identification model works at a rapid speed and shows high recall rates in all tissues and high precision rates in lung tissues under an appropriate threshold. The identification model greatly reduces the workload of manual recognition, and has a good application prospect.

Evaluation of Inspection Efficiency of Diatom Artificial Intelligence Search System Based on Scanning Electron Microscope / 法医学杂志

OBJECTIVES@#To explore the application values of diatom artificial intelligence (AI) search system in the diagnosis of drowning.@*METHODS@#The liver and kidney tissues of 12 drowned corpses were taken and were performed with the diatom test, the view images were obtained by scanning electron microscopy (SEM). Diatom detection and forensic expert manual identification were carried out under the thresholds of 0.5, 0.7 and 0.9 of the diatom AI search system, respectively. Diatom recall rate, precision rate and image exclusion rate were used to detect and compare the efficiency of diatom AI search system.@*RESULTS@#There was no statistical difference between the number of diatoms detected in the target marked by the diatom AI search system and the number of diatoms identified manually (P>0.05); the recall rates of the diatom AI search system were statistically different under different thresholds (P<0.05); the precision rates of the diatom AI system were statistically different under different thresholds(P<0.05), and the highest precision rate was 53.15%; the image exclusion rates of the diatom AI search system were statistically different under different thresholds (P<0.05), and the highest image exclusion rate was 99.72%. For the same sample, the time taken by the diatom AI search system to identify diatoms was only 1/7 of that of manual identification.@*CONCLUSIONS@#Diatom AI search system has a good application prospect in drowning cases. Its automatic diatom search ability is equal to that of experienced forensic experts, and it can greatly reduce the workload of manual observation of images.

Comparison among Four Deep Learning Image Classification Algorithms in AI-based Diatom Test / 法医学杂志

OBJECTIVES@#To select four algorithms with relatively balanced complexity and accuracy among deep learning image classification algorithms for automatic diatom recognition, and to explore the most suitable classification algorithm for diatom recognition to provide data reference for automatic diatom testing research in forensic medicine.@*METHODS@#The "diatom" and "background" small sample size data set (20 000 images) of digestive fluid smear of corpse lung tissue in water were built to train, validate and test four convolutional neural network (CNN) models, including VGG16, ResNet50, InceptionV3 and Inception-ResNet-V2. The receiver operating characteristic curve (ROC) of subjects and confusion matrixes were drawn, recall rate, precision rate, specificity, accuracy rate and F1 score were calculated, and the performance of each model was systematically evaluated.@*RESULTS@#The InceptionV3 model achieved much better results than the other three models with a balanced recall rate of 89.80%, a precision rate of 92.58%. The VGG16 and Inception-ResNet-V2 had similar diatom recognition performance. Although the performance of diatom recall and precision detection could not be balanced, the recognition ability was acceptable. ResNet50 had the lowest diatom recognition performance, with a recall rate of 55.35%. In terms of feature extraction, the four models all extracted the features of diatom and background and mainly focused on diatom region as the main identification basis.@*CONCLUSIONS@#Including the Inception-dependent model, which has stronger directivity and targeting in feature extraction of diatom. The InceptionV3 achieved the best performance on diatom identification and feature extraction compared to the other three models. The InceptionV3 is more suitable for daily forensic diatom examination.

Research Progress of Automatic Diatom Test by Artificial Intelligence / 法医学杂志

Diatom test is the main laboratory test method in the diagnosis of drowning in forensic medicine. It plays an important role in differentiating the antemortem drowning from the postmortem drowning and inferring drowning site. Artificial intelligence (AI) automatic diatom test is a technological innovation in forensic drowning diagnosis which is based on morphological characteristics of diatom, the application of AI algorithm to automatic identification and classification of diatom in tissues and organs. This paper discusses the morphological diatom test methods and reviews the research progress of automatic diatom recognition and classification involving AI algorithms. AI deep learning algorithm can assist diatom testing to obtain objective, accurate, and efficient qualitative and quantitative analysis results, which is expected to become a new direction of diatom testing research in the drowning of forensic medicine in the future.

A real-time qPCR method to identify diatom UPA gene for the drowning diagnosis / 中国法医学杂志

Objective To establish a Real-time quantitative PCR method (qPCR) for the detection of diatom UPA barcoding genes and evaluate its application in the drowning diagnosis. Methods The homologous sequences of diatoms UPA gene was obtained by Blast from GeneBank, based on which the universal primers for diatoms were designed. DNA were extracted from 2 common human symbiotic bacteria (Escherichia coli, Bifidobacterium longum), 3 species of planktonic bacteria, 15 species of planktonic algae, tissue samples (lung, liver and kidney) from human cadavers (28 drowning victims, 1 victims by non-drowning in the water, 3 victims deaths on land) in 32 cases. The specificity, sensitivity and repeatability of the designed primers were tested. The positive rates of diatoms detection in the drowning cases were calculated. The results of the real-time quantitative method were evaluated comparatively by Microwave Digestion-Vacuum Filtration-Automated Scanning Electron Microscopy (MD-VF-Auto SEM) and PCR-Capillary Electrophoresis (PCR-CE). Results The results showed that the primers UPA99 had strong specificity for the diatomaceae (Synedra radians, Navicula sp., Melosira varians, Cyclotella sp. and Nitzschia sp.) DNA. The melting curve of the amplified product was smooth; the peak was narrow; the melting temperature was (87±1)℃. The sensitivity of qPCR method was 1.56×10-5ng/μL with the detection range of 1.56×102ng/mL~1.56×10-5ng/μL, in contrast with the PCR-CE method (1.56×10-3ng/μL). This real-time PCR method showed high repeatability and stability with the coefficient of variation less than 2%. The detection rate of lung, liver and kidney was 89.3%, 71.4% and 64.3% respectively. Conclusion The established qPCR method, based on the universal primers designed for diatom UPA gene, has high specificity, high sensitivity and good repeatability. With a promising prospect for application, qPCR is suitable for drowning diagnosis.