法医学杂志Pub Date : 2024-12-25DOI: 10.12116/j.issn.1004-5619.2024.340705
Zhen-Shuo Guo, Wen-Jia Duan, Yu Liu, Yi-Ling Tang, Hui Yan
{"title":"Research Progress on the Analysis of Anabolic Androgenic Steroids in Biological Samples Based on High Resolution Mass Spectrometry.","authors":"Zhen-Shuo Guo, Wen-Jia Duan, Yu Liu, Yi-Ling Tang, Hui Yan","doi":"10.12116/j.issn.1004-5619.2024.340705","DOIUrl":"10.12116/j.issn.1004-5619.2024.340705","url":null,"abstract":"<p><p>Anabolic androgenic steroids (AASs) are a class of synthetic steroid hormones that mimic androgens, and they rank as the most widely abused doping agents worldwide. High resolution mass spectrometry (HRMS) has unique advantages in the detection of AASs due to its high resolution, high sensitivity, high selectivity and data traceability. HRMS can not only be used for the qualitative and quantitative analysis of AASs and their metabolites in different biological samples, effectively improving the ability to analyze complex samples and increasing the reliability of analytical results, but can also infer AASs metabolites and reveal metabolic pathways by combining <i>in vitro</i> and <i>in vivo</i> metabolic models. This paper reviews the research progress of HRMS in AASs analysis methods, <i>in vitro</i> and <i>in vivo</i> metabolism of AASs, and also explores its application prospects in the field of forensic science.</p>","PeriodicalId":12317,"journal":{"name":"法医学杂志","volume":"40 6","pages":"533-541"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143624031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
法医学杂志Pub Date : 2024-10-25DOI: 10.12116/j.issn.1004-5619.2024.541008
Jing Liu, Zheng Wang, Yi-Ping Hou, Lin-Chuan Liao
{"title":"The Impact of STR Mutations on Kinship Identification.","authors":"Jing Liu, Zheng Wang, Yi-Ping Hou, Lin-Chuan Liao","doi":"10.12116/j.issn.1004-5619.2024.541008","DOIUrl":"10.12116/j.issn.1004-5619.2024.541008","url":null,"abstract":"<p><p>Kinship identification is an important field of forensic genetics research, which can be widely applied in inheritance disputes, criminal investigations, and the identification of victims in major disaster cases. At present, capillary electrophoresis-based STR analysis is still the main method for kinship identification, but the impact of STR mutations on kinship identification needs further exploration. This paper reviews the theoretical basis and research status at home and abroad of kinship identification. The challenge of STR mutation impact on kinship identification is prospected, and possible solutions are discussed in order to obtain a regular understanding of the impact of STR mutation on kinship identification and improve the accuracy of kinship analysis.</p>","PeriodicalId":12317,"journal":{"name":"法医学杂志","volume":"40 5","pages":"484-491"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
法医学杂志Pub Date : 2024-10-25DOI: 10.12116/j.issn.1004-5619.2024.440601
{"title":"Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.","authors":"Hao-Jie Qin, Yuan-Yuan Liu, En-Hao Fu, Ya-Wen Liu, Zhi-Ling Tian, He-Wen Dong, Tai-Ang Liu, Dong-Hua Zou, Yi-Bin Cheng, Ning-Guo Liu","doi":"10.12116/j.issn.1004-5619.2024.440801","DOIUrl":"10.12116/j.issn.1004-5619.2024.440801","url":null,"abstract":"<p><strong>Objectives: </strong>To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as \"segmentation\") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury (BCI), and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.</p><p><strong>Methods: </strong>A total of 5 486 CT images of BCI from living persons were collected as the training set, validation set and test set for model training and performance evaluation. Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to segment the five types of craniocerebral injuries including scalp hematoma, skull fracture, epidural hematoma, subdural hematoma, and brain contusion. Another 340 BCI and 120 normal craniocerebral CT images from cadavers were collected as the new blind test set to explore the application value of the model trained by living CT images in the segmentation of BCI in cadavers. The five types CT images of all BCI except the blind test set were manually labeled; then, each dataset was inputted into the model to train the model. The performance of the model was evaluated and optimized based on the loss function and accuracy curves of the training set and validation set, and the generalization ability was evaluated based on the Dice value of the test set. According to the accuracy, precision and <i>F</i>1 value of the blind test set, the segmentation performance of the model for five types of BCI was evaluated.</p><p><strong>Results: </strong>After training and optimizing the model, the average Dice values of the final optimal model to scalp hematoma, skull fracture, epidural hematoma, subdural hematoma and brain contusion segmentation were 0.766 4, 0.812 3, 0.938 7, 0.782 7 and 0.858 1, respectively, all greater than 0.75, meeting the expected requirements. External validation showed that the <i>F</i>1 values were 93.02%, 89.80%, 87.80%, 92.93% and 86.57% in living CT images, respectively; 83.92%, 44.90%, 76.47%, 64.29% and 48.89% in cadaveric CT images, respectively. The above suggested that the model was able to accurately segment various types of craniocerebral injury on living CT images, while its segmentation ability was relatively poor on cadaveric CT images, but still able to accurately segment scalp hematoma, epidural hematoma and subdural hematoma.</p><p><strong>Conclusions: </strong>Deep learning model trained on CT images can be used for BCI segmentation. However, the direct use of living persons' BCI models for the identification of cadaveric BCI has some limitations. This study provides a new approach for intelligent segmentation of virtual anatomical data for BCI.</p>","PeriodicalId":12317,"journal":{"name":"法医学杂志","volume":"40 5","pages":"419-429"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Establishment of an Acute Karoshi Rat Model and Its Metabolic, Functional and Morphological Changes.","authors":"Xia Liu, Jia-Min Li, Yong-Xia Zheng, Xu-Dong Xiao, Xiao-Jun Yu","doi":"10.12116/j.issn.1004-5619.2022.421007","DOIUrl":"10.12116/j.issn.1004-5619.2022.421007","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the occurrence and mechanism of acute Karoshi and explore its forensic identification.</p><p><strong>Methods: </strong>SD rats were divided into the control group (<i>n</i>=15) and experimental groups (<i>n</i>=45, acute Karoshi group and overwork survival group). A severe fatigue model was established by combining forced swimming under load to exhaustion and sleep deprivation. Their daily activities, diets, weight, respiratory functions, electrocardiogram and echocardiography were recorded. After the rats were sacrificed, samples were collected at autopsies. HE staining was used to observe the pathological morphology, and GC-MS was used to detect the changes of substance metabolism in serum, myocardium and liver.</p><p><strong>Results: </strong>The mortality rate of the experimental group was 33.3%. There were decreases of aminobutyric acid and arachidonic acid in myocardium tissues, decreases of urea and increases of methionine and phenylalanine in serum. In liver tissues, the content of amino acids sush as histidine increased. The blood biochemical testing showed increases of alanine aminotransferase, aspartate aminotransferase, creatine kinase and creatine kinase isoenzymes and decreases of glucose and uric acid. There were interferences of energy metabolism pathways in serum, heart, and liver tissues. After three days, the experimental group developed cardiac conduction block and ventricular arrhythmia. Ventricular fibrillation and ventricular flutter appeared in acute Karoshi group. Echocardiogram showed ejection fraction and left ventricular short axis shortening rate decreased. The histological examination showed granular swelling and sarcoplasmic condensation in myocardium and increased dark neurons in the brain stem. The combination of differential metabolites of serum urea, methionine and phenylalanine was highly correlated with Karoshi with a diagnostic rate of 90.6%.</p><p><strong>Conclusions: </strong>Acute Karoshi can trigger a cascade reaction of metabolic, functional and morphological changes. The mechanism of death, especially central failure and sudden cardiac death, may be associated with multi-organ failure.</p>","PeriodicalId":12317,"journal":{"name":"法医学杂志","volume":"40 5","pages":"439-446"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
法医学杂志Pub Date : 2024-10-25DOI: 10.12116/j.issn.1004-5619.2024.140502
法医学杂志Pub Date : 2024-10-25DOI: 10.12116/j.issn.1004-5619.2024.240304
Zhi Yan, Xun-Ming Ji, Xiao He, Xiao-Jing Zhang, Lei Wan, Hong Zhang, Mei Tian, Bin Cong
{"title":"Progress and Application Prospects of Forensic Molecular Imaging Technology in Living Individual Examination.","authors":"Zhi Yan, Xun-Ming Ji, Xiao He, Xiao-Jing Zhang, Lei Wan, Hong Zhang, Mei Tian, Bin Cong","doi":"10.12116/j.issn.1004-5619.2024.240304","DOIUrl":"10.12116/j.issn.1004-5619.2024.240304","url":null,"abstract":"<p><p>To propose the definition of forensic molecular imaging (FMI) and to utilize molecular imaging techniques to seek effective solutions to important issues in the field of forensic medicine, such as forensic psychiatry, drug-related damages, internet gaming addiction, and stress-induced injuries. FMI is an emerging interdisciplinary field. It is in its infancy in China and faces certain problems and challenges, such as a shortage of skilled professionals and a lack of standardization. Therefore, it is crucial for China to enhance the cultivation of talents, fundamental research, and practical applications in FMI. FMI will play its supporting role in public security, public health, judicial trials, civil mediation and other aspects, to safeguard judicial justice and social stability, and promote the construction of a peaceful and rule-of-law society in China.</p>","PeriodicalId":12317,"journal":{"name":"法医学杂志","volume":"40 5","pages":"476-483"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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