In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.

Objective To evaluate the influence of the wearing position of dosimeters outside lead aprons on effective dose estimation for interventional radiology workers, analyze the differences between single and double dosimeter methods in effective dose estimation, and provide a reference for the personal dose monitoring of interventional radiology workers. Methods This study employed a combined approach of on-site monitoring and Monte Carlo simulation to evaluate the impact of the wearing position of dosimeters outside lead aprons on effective dose estimation, as well as the differences between effective doses measured using single and double dosimeters. Interventional radiology workers wore dosimeters at three positions: the neck outside the lead collar, the left chest outside the lead apron, and inside the lead apron. Effective doses were estimated using the single and double dosimeter methods specified in GBZ 128-2019 Specifications for individual monitoring of occupational external exposure, and the impact of different wearing positions on the estimation results was compared. Geant4 Monte Carlo simulations were used to model dose distributions at the neck outside the lead collar and at the left chest outside the lead apron for operators performing cardiovascular interventions under tube voltages of 70, 80, 90, and 100 kVp and exposure angles of posteroanterior (PA), anteroposterior (AP), and left anterior oblique 45° (LAO45°) positions. The study assessed the impact of dosimeter wearing position on effective dose estimation. Results Monte Carlo simulations demonstrated that neck doses consistently exceeded left chest doses across different tube voltages and exposure angles, with neck-to-chest dose ratios of 0.80-0.90. Under identical tube voltage conditions, AP showed the highest doses, followed by LAO45°, and PA demonstrated the lowest doses. The single and double dosimeter methods exhibited consistent patterns in effective dose estimation. Single dosimeter method generally yielded higher effective doses with relative deviations of 9.9% to 83%, though these deviations decreased under high tube voltages. Field monitoring data indicated that most interventional radiology workers maintained relative deviations between single and double dosimeter calculations below 6%, with neck-to-chest dose ratios of 0.95-1.1. The estimation patterns remained consistent across both methods, though single dosimeter method showed slightly higher results. Conclusion Under PA, AP, or LAO45°, the doses at the neck consistently exceeded those at the left chest. Therefore, when wearing lead protective equipment, the dosimeter should be properly positioned at the neck outside the lead collar to accurately reflect the radiation doses of surgeons. Some interventional radiology workers improperly positioned the dosimeter (intended at the neck outside the lead collar) at the left chest outside the lead apron, and this may result in an underestimation of the effective dose.

Objective To investigate the expression of myelin transcription factor 1-like(MYT1L)during amyotrophic lateral sclerosis(ALS)progression and its association with neuronal degeneration through bioinformatics analysis combined with in vivo and in vitro experiments.Methods Bioinformatics analysis of the GSE106803 dataset from the Gene Expression Omnibus(GEO)database revealed significant down-regulation of MYT1L in spinal cords of ALS transgenic mice carrying the human superoxide dismutase 1 mutant gene(hSOD1G93A)compared to the wild-type(WT)mice.hSOD1G93A transgenic mice and their WT littermates were selected to analyze MYT1L mRNA and protein changes in spinal cord tissues at different disease stages using Real-time PCR and Western blotting.Double immunofluorescent staining was used to determine the distribution and cellular localization of MYT1L in the spinal cord of mice at the middle stage of the disease.An ALS cellular model was established using hSOD1G93A mutant NSC34 cells,with hSOD1WT NSC34 cells as controls.MYT1L expression and distribution were assessed in these cells via Real-time PCR,Western blotting,and immunofluorescent staining.Based on the GSE76220 dataset from the GEO database,differentially expressed genes(DEGs)between MYT1L high-and low-expression groups in lumbar spinal motor neurons of ALS patients were identified,followed by Gene Ontology(GO)functional enrichment analysis.MYT1L overexpression was induced in the ALS cellular model to evaluate alterations in cell viability and neurite outgrowth.Results In the GSE106803 dataset,MYT1L expression was significantly down-regulated in the spinal cord of ALS mice.Animal experiments confirmed progressive reductions in MYT1L mRNA and protein levels in spinal cord tissues of ALS mice during mid-and late-disease stages.Compared to the WT group,MYT1L expression decreased in motor neurons of the lumbar spinal cord gray matter anterior horn in ALS mice,while it increased in astrocytes.In vitro,hSOD1G93Amutant NSC34 cells exhibited significantly reduced MYT1L expression than controls,with MYT1L localized to both the cytoplasm and nucleus.DEGs between MYT1L high-and low-expression groups in lumbar spinal cord motor neurons of ALS patients(GSE76220 dataset)were enriched in synaptic-related functions through GO analysis.Overexpression of MYT1L in hSOD1G93A mutant NSC34 cells enhanced cell viability and promoted neurite outgrowth.Conclusion Aberrantly low expression of MYT1L is closely associated with ALS pathogenesis.Overexpression of MYT1L promotes neurite growth and exerts protective effects on ALS motor neurons,suggesting its therapeutic potential.

Objective To investigate the density,species composition,and seasonal prevalence of domestic rodents in different habitats within Lanzhou garrisons,providing basic information for rodent prevention and control.Methods A total of 12 monitoring sites were sampled across urban,suburban,and rural residential areas from 2014 to 2022.Rodent density was monitored using the night-trapping method in the middle of odd-numbered months.Results From 2014 to 2022,346 domestic rodents were captured using 122 035 effective traps,with an average domestic rodent density of 0.28％.The highest domestic rodent density was 0.63％in 2016,and the lowest was 0.07％in 2020,showing significant differences across years,with an overall trend of initially decreasing and then increasing(χ2=136.555,P<0.001).The dominant species was Rattus norvegicus,accounting for 83.24％of the total rodents captured.Rattus norvegicus accounted for a relatively high proportion across different years,with a statistically significant difference in species composition(χ2=20.931,P<0.05).Rodent densities and species composition also varied significantly among the monitored habitats(P<0.001),with the highest densities observed in rural residential areas and the lowest in urban areas.Seasonal variation in rodent densities showed a bimodal pattern,with smaller peaks in January or March and a larger peak in July.Conclusions Domestic rodent density in Lanzhou garrisons has shown an upward trend in the past few years.Rodent control measures should focus on barracks in rural residential areas,with targeted interventions to reduce the risk of rodent-borne diseases.

Objective To construct a competency evaluation model for forensic practitioners,providing a reference for their training and assessment.Methods Based on the iceberg and onion models of com-petency,and with reference to Spencer's Competency Dictionary,literature research was conducted and focus group interviews were employed to preliminarily construct core indices and measurement items for evaluating the competency of forensic practitioners.The Delphi method was applied for two rounds of expert consultation to further refine the competency evaluation index system.The analytic hierarchy process(AHP)was used to calculate the weights of the indices.Results A competency evaluation model for forensic practitioners was constructed,consisting of 7 core indices,encompassing forensic skills,identification service capabilities,and the ability to apply relevant legal knowledge and 49 mea-surement items.The weights of the core indices and measurement items were determined.Conclusion The constructed competency evaluation model for forensic practitioners is scientifically sound and inno-vative,and has unique characteristics of forensic medicine compared with other medical models.

Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Objective:To provide novel genetic biomarkers for the diagnosis and treatment of sepsis,bioinformatics analysis was used to screen differentially expressed genes and identify Hub genes in sepsis.Methods:Gene Expression Omnibus(GEO)database was used to retrieve gene expression datasets of sepsis and screen for differentially expressed genes(DEGs).Protein-protein interaction(PPI)network analysis,Gene Ontology(GO)analysis,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were used to clarify the molecular mechanism of DEGs,and Hub genes were screened.Results:A total of 361 DEGs were identified,including 163 up-regulated genes and 198 down-regulated genes.Enrichment analysis revealed that these DEGs were primarily involved in antigen processing and presentation,T cell biology,cell adhesion molecules,and T cell receptor signaling pathways.CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1 were determined as optimal diagnostic biomarkers for sepsis.Conclusions:This study elucidated 10 Hub genes(CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1)as potential biomarkers for the diagnosis and treatment of sepsis.However,since the the generalizability of these Hub genes in patients with sepsis remains unvalidated,further experimental verification is still needed in the future.