Objective To use finite element methods to conduct a digital simulation of a craniocerebral injury death case in which the manner of injury was questionable. Based on biomechanical data, the injury mechanism of the deceased’s brain injury was quantitatively analyzed to provide a quantitative and visual objective basis for determining the manner of injury. Methods The THUMS 4.02 human finite element model was used to simulate the process of skull and brain tissue injury when the occipital region fell and struck flat ground, with impact velocities of 4.18, 5.00, 5.50 and 6.00 m/s. Combined with the actual autopsy results, the injury mechanism was analyzed based on injury patterns and tissue stress characteristics. Results When the human body fell freely without external force （4.18 m/s） and landed on the occipital region, it caused minor fractures of the occipital bone and mild brain tissue injury; when the body was pushed to the ground by external forces （5.00, 5.50 and 6.00 m/s）, the fracture range of the skull extended to the skull base, causing moderate brain tissue injury. Based on the actual injury findings, the possibility that the deceased fell freely can be ruled out. Conclusion Reconstructing the process of traumatic brain injury using the finite element method can provide new ideas for determining the mechanism of injury in complex forensic cases through biomechanical indicators.

Objective To extract structured injury features of knee trauma from forensic case files, and to calculate the degree of joint mobility limitation based on the injury features using a machine learning model combined with the voting method. Methods A total of 490 forensic cases involving knee trauma leading to motor dysfunction were retrospectively collected and randomly divided into training and testing sets at an 8:2 ratio. Structured injury features were extracted and systematically organized and stored using a MySQL database. Six machine learning models, including support vector classification, random forest, logistic regression, gradient boosting, k- nearest neighbors, and extreme gradient boosting, were applied to select the optimal models. Using a 25% loss of joint range of motion as the threshold, a model for classifying the severity of knee functional impairment was established by combining the selected models with a voting method. The best models were first selected based on their average AUC values, and further validated using 5-fold cross-validation. The SHAP method was used to analyze and interpret the prediction results of the optimal model. In addition, 57 cases were collected as an external validation to evaluate the model's generalization ability. Results The average AUC values for support vector machine, random forest, and extreme gradient boosting all exceeded 0.9. In 5-fold cross-validation, each of the three individual models achieved an average AUC value of 0.89. After integrating these three models using the voting method, the average AUC of 5-fold cross-validation increased to 0.91. Feature contribution plots and decision plots generated based on Shapley values clearly illustrated the model's performance, and the evaluation metrics on the external validation set were comparable to those from internal validation. Conclusion The developed machine learning model based on structured injury features demonstrates good performance in classifying the severity of motor dysfunction following knee trauma, with high model interpretability and strong generalization capability.

Objective To understand the current status and trends in forensic microbiology research using bibliometric analysis methods. Methods A total of 533 papers related to forensic microbiology published in the Web of Science Core Collection from 2010 to 2022 were selected as research objects. Bibliometrix and VOSviewer were used to conduct a visual analysis of the annual publication volume, journals, countries （regions）， institutions, authors, and keywords co-occurrence networks in this field. Results The annual publication volume showed an overall upward trend, reaching a peak of 72 publications in 2021. The literature primarily appeared in journals focusing on forensics, microbiology and comprehensive journals. The top high-yield countries were the United States （231 papers）, China （47 papers）, and Australia （32 papers）. Michigan State University in the United States contributed the highest publication volume （25 papers）, and the author with the highest publication volume was BENBOW ME from the university. Keyword co-occurrence analysis revealed that forensic microbiology was initially mainly applied in the field of biosafety. With the advancement of sequencing technologies, new application areas, such as postmortem interval estimation, have emerged. Current research hotspots in this field primarily focused on biosafety, postmortem interval estimation, and genetic sequencing analysis, demonstrating a trend of multi-institutional collaboration and interdisciplinary integration. Conclusion In recent years, research in forensic microbiology has exhibited rapid growth. The cross-combination of high-throughput sequencing technologies and bioinformatics with forensic microbiology has driven the significant advancement of this field.

Objective To compare the technical performance indicators of six commonly used STR kits, and to evaluate their capabilities in forensic testing. Methods The STR genotyping results of forensic DNA standard sample 9948 with different template quantities were analyzed using Identifiler^TM Plus, AGCU EX28CS, NHID^® 25A, Verifiler^TM Plus, VersaPlex^TM 27PY, and FastDirect^TM 25A. Differen-ces in detection sensitivity, allelic drop-out/in, allele peak height and heterozygote balance were eva-luated. Various PCR inhibitors, including humic acid, heme, indigo, calcium ions and ethylenediaminetetraacetic acid （EDTA）， were added to the multiplex amplification systems to compare the maximum tolerance levels of the six kits to PCR inhibitors. Results The detection sensitivity of Identifiler^TM Plus, AGCU EX28CS and VersaPlex^TM 27PY was as low as 100 pg, whereas the sensitivity of FastDirect^TM 25A, NHID^® 25A and Verifiler^TM Plus reached 50 pg. At different template quantities, the number of STR loci detected by the five kits was greater than that of Identifier^TM Plus. When detecting templates of 50 pg or less, the total number of allelic drop-out/in observed with the Identifiler^TM Plus was lower than that observed with the other five kits. The allele peak heights detected by AGCU EX28CS at 200 and 100 pg template quantities and by VersaPlex^TM 27 PY at 50 pg were significantly lower than those detected by Identifiler^TM Plus （P<0.05）. Regarding resistance to inhibitors, NHID^® 25A showed stronger tolerance to humic acid （≤250 ng/μL）, heme （≤0.6 mmol/L） and calcium ions （≤1.4 mmol/L） than other kits. It was found that VersaPlex^TM 27PY showed the strongest tolerance to indigo （≤0.048 mol/L）; and FastDirect^TM 25A showed the strongest tolerance to EDTA （≤1.0 mmol/L）. For heme, AGCU EX28CS and FastDirect^TM 25A showed lower tolerance than Identifiler^TM Plus, whereas the other three kits exhibited higher tolerance. For other inhibitors, all five kits demonstrated tolerance comparable to or higher than that of Identifiler^TM Plus. Conclusion Compared with Identifiler^TM Plus, AGCU EX28CS, FastDirect^TM 25A, NHID^® 25A, VersaPlex^TM 27PY and Verifiler^TM Plus show no obvious overall disadvantage in detection sensitivity, allelic drop-out/in, allele peak height, heterozygote balance and inhibitor resistance and even demonstrate superior performance in certain technical indicators.

Accurate classification of postmortem decomposition stages is a critical step in estimating the postmortem interval （PMI） and tracing the initial decomposition environment. Research on the decomposition staging methodological system is gradually shifting from empirical observation to the establishment of systems based on multidimensional quantitative indicators. This paper focuses on two key pathways, “macroscopic morphological evolution” and “microscopic molecular succession”, and systema-tically reviews the evolutionary patterns and applicability of the decomposition staging system in three typical environmental media: surface exposure, burial, and aquatic systems. It also summarizes research progress in constructing stage classification models utilizing microbiome and metabolomic features. Furthermore, it highlights the integrated application of decomposition characteristic quantification techniques, multi-omics data integration, and machine learning algorithms in decomposition analysis systems. It analyzes the prospects and challenges of applying these approaches to build a standardized and practical decomposition staging system, aiming to provide theoretical support for establishing a decomposition staging system with high accuracy and strong adaptability to different environments.

In forensic pathological practice, mesenteric injuries are often accompanied by more severe abdominal organ injuries. As a result, examination of the mesentery is sometimes overlooked during autopsy. Mesenteric injury related studies remain relatively limited. In child abuse cases, injuries can occur in various parts of the body and present in diverse forms. Due to the developmental characteristics of the pediatric abdomen, mesenteric injuries caused by abuse are also frequently overlooked or misdia-gnosed in both clinical and postmortem examinations. This article combines practical experience in forensic identification with relevant domestic and international literature to explain key points in the identification of mesenteric injuries. It also explores the relationship between certain mesenteric injuries in child abuse in forensic identification, along with their characteristic manifestations, with the aim of providing a reference for such identifications.

Metabolomics is an emerging technology that uses bioinformatics techniques and methods to comprehensively analyze changes in small-molecule metabolites within organisms to identify biomar-kers. In recent years, hair has gradually gained attention in the field of metabolomics. Compared with blood and urine, hair offers a wider detection window and enables retrospective studies. In addition, segmental analysis based on hair growth rates allows hair metabolomics to provide chronological information on changes in relevant substances. At present, hair metabolomics has established a certain research foundation in areas such as monitoring drug abuse, and it shows broad application prospects in forensic applications. This article reviews the collection and processing of hair samples, analytical techniques, and their applications in forensic research, with the aim of providing a reference for the further development of hair metabolomics and its in-depth application across multiple fields.

Founded in 1985, Fayixue Zazhi is China’s first publicly distributed academic journal in the field of forensic medicine. Over its 40-year history, the journal is consistently tracking and publishing research frontiers in forensic medicine. The journal has continuously improved its academic impact and disciplinary rankings and has been selected multiple times for the World Journal Clout Index （WJCI） Report and the BIBF “China Premium Periodicals Exhibition”. It has also been honored as an Excellent Journal of East China and for its Outstanding Journal Sections, gradually becoming a core journal in forensic medicine and earning high recognition from peers in the field. This article systematically reviews the 40-year development history of Fayixue Zazhi, and summarizes its characteristics and academic achievements across four stages: initial establishment, exploratory development, steady transformation, and distinctive innovation. It also examines existing challenges and potential solutions in the journal’s development under the strategic guidance of national judicial justice, legal construction, and high-quality development of scientific journals, and prospects future development directions from the perspectives of enhancing academic contents, optimizing publication processes, building distinctive branding, and strengthening team development.

Pregabalin is a structural derivative of the inhibitory neurotransmitter gamma-aminobutyric acid. Currently, the number of deaths related to the misuse or abuse of pregabalin is gradually increa-sing, and there is an urgent need for relevant forensic toxicological information. Based on relevant do-mestic and international research, this article systematically reviews the pharmacological effects, toxicity data, poisoning symptoms, in vivo processes of pregabalin, as well as the toxicological data of various existing typical cases, with the aim of providing a reference for forensic identification.