Purpose: The aim of this study is to determine the impact of solute carrier family 3 member 2 (SLC3A2) on the malignant phenotype of gliomas and its role in regulating ferroptosis sensitivity. Materials and Methods: The malignant phenotype of glioma was assessed by cell proliferation assay, colony formation assay, EdU assay, wound healing, and Transwell experiments. We further validated the impact of reduced SLC3A2 expression on the sensitivity to ferroptosis in glioma cells through Cell Counting Kit-8 assays, flow cytometry, western blotting, and transmission electron microscopy. Western blot was used to explore how SLC3A2 affects glioma sensitivity to ferroptosis through the AKT/NF-E2-related factor 2 (NRF2)/glutathione peroxidase 4 (GPX4) axis. By establishing a subcutaneous xenograft tumor model in BALB/c-nude mice, we investigated the growth of tumors following the knockout of SLC3A2 in glioma cells. Results: Downregulation of SLC3A2 suppressed the malignant phenotype of glioma by blocking the cell cycle and epithelial-mesenchymal transition processes. On the other hand, loss of SLC3A2 not only downregulated SLC7A11 but also prevented the activation of the AKT/NRF2/GPX4 axis. These lead to increased accumulation of reactive oxygen species and lipid peroxides, ultimately enhancing the susceptibility of glioma to ferroptosis. Conclusion Our findings suggest that SLC3A2 is an oncogene in gliomas, promoting their occurrence and development. It plays a critical role in ferroptosis resistance through the AKT/NRF2/GPX4 axis.

ObjectiveTo systematically characterize the in vitro chemical constituents of Entada phaseoloides and its absorbed prototype compounds and metabolites in a neuropathic pain （NP） model， thereby elucidating the potential pharmacological material basis underlying its analgesic activity. MethodsUltra-high-performance liquid chromatography coupled with quadrupole-Orbitrap high-resolution mass spectrometry （UHPLC-Q-Orbitrap HRMS） was used to analyze the constituents of the 80% methanol extract of E. phaseoloides. Thirty-six mice were divided into a blank group， low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， an aspirin group （400 mg·kg^-1）， and a morphine group （10 mg·kg^-1）， with six mice in each group. The tail-flick threshold was measured. Twenty-four mice were randomly divided into a blank group and low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， with six mice in each group， and motor coordination was evaluated. A spinal nerve ligation （SNL） model was established in mice. Twenty-four mice were randomly divided into a sham-operated （Sham） group， an SNL group， an SNL + E. phaseoloides extract group （750 mg·kg^-1）， and an SNL + pregabalin group （20 mg·kg^-1）， with 6 mice in each group. Mechanical allodynia and thermal hyperalgesia were evaluated. Behavioral assessments were combined to determine the optimal onset time of the analgesic effect， and plasma samples were collected at this time point. Absorbed components and metabolites were systematically identified based on accurate mass， MS/MS fragmentation patterns， retention time， and comparisons with literature data and reference standards. Meanwhile， network pharmacology， target similarity analysis， and molecular docking validation were integrated to evaluate the potential effects of absorbed components on disease intervention. ResultsCompared with the blank group， the high-dose E. phaseoloides extract group and morphine group showed significantly increased pain response thresholds （P<0.01）， without affecting motor coordination in normal mice. Compared with the Sham group， the SNL group exhibited significantly decreased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. Compared with the SNL group， the SNL + E. phaseoloides extract group and SNL + pregabalin group showed significantly increased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. The analgesic effect of E. phaseoloides was most significant at 2 h after administration. A total of 70 constituents were identified in vitro， and 22 prototype compounds and 29 metabolites were detected in plasma. The targets of the absorbed components were mainly enriched in biological processes associated with pain signal perception and transmission and showed a high degree of overlap with disease genes related to neuropathic pain. In addition， multiple components exhibited strong binding energies with core pain-related targets. Based on integrated analysis of in vitro content， plasma exposure levels， and target associations， ketanthamide A-β-D-pyranoside and ketengzide were identified as the most representative pharmacologically active substances responsible for the analgesic effect. ConclusionThis study systematically elucidates the material basis of E. phaseoloides intervention in neuropathic pain from three aspects， including chemical constituent characteristics， in vivo behavioral characteristics， and potential targets， providing important theoretical foundations and experimental support for further studies on its pharmacological mechanism and analgesic drug development.

Objective: To investigate follow-up participation situation, negative conversion, and tuberculosis incidence among boarding junior and senior high school freshmen with latent tuberculosis infection(LTBI) in Zhuhai, so as to provide a reference for better LTBI management in school settings. Methods: A prospective cohort study enrolled 413 LTBI and 27 184 non LTBI boarding junior and senior high school freshmen in Zhuhai from September to October 2023. The LTBI group underwent follow up retesting at >3-6, >6-12, >12-24, >24 months after screening. The followup content included mycobacterium tuberculosis infection testing,chest X ray examination,and questionaire survey(basic demographic information, lifestyle, history of close contact with tuberculosis, and suspected symptoms of pulmonary tuberculosis). Follow-up participation, negative conversion, and tuberculosis incidence were analyzed. Rate was compared by using the Chi-square test, and factors associated with negative conversion were assessed by multivariate Logistic regression analysis. Results: Follow-up participation rates increased initially and then declined, with rates of 25.67% within >3-6 months, 37.05% within >6-12 months, 82.02% within >12-24 months, and 52.21% after 24 months. The overall negative conversion rate was 61.03%. The differences in the conversion rate to negative of mycobacterium tuberculosis infection detection among junior and senior high school freshmen in the LTBI group by different testing methods, school types, smoking history, sleep status, and academic stress were all statistically significant( χ 2=124.96, 22.70, 4.71, 6.31, 6.08, all P <0.05). Multivariate Logistic regression analysis identified testing methods, smoking history, and sleep status as factors associated with negative conversion ( OR =1.90-21.67, all P <0.05). The cumulative incidence of tuberculosis was 726.39 per 100 000 in the LTBI group versus 14.71 per 100 000 in the non-LTBI group ( RR =49.37, 95% CI =11.08-219.87, P <0.01). Conclusions Boarding junior and senior high school freshmen with LTBI in Zhuhai have a higher risk of tuberculosis than concurrent non-LTBI students; they therefore represent a priority group for tuberculosis prevention and control. Centralized follow up is recommended to improve participation, and high specificity methods should be used in follow up retesting and maintain consistency across assessments.

Objective:This study identifies independent predictive indicators to distinguish autoimmune gastritis from Helicobacter pylori ( H. pylori)-induced atrophic gastritis and validates their diagnostic performance to compare laboratory indicators of autoimmune gastritis and H. pylori-induced atrophic gastritis. Methods:A retrospective comparison of laboratory examination indicators was conducted for chronic atrophic gastritis patients with involvement of the gastric fundus and corpus, who were followed up at the Department of Gastroenterology, Xijing Hospital, from January 2014 to September 2024. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cutoff points and corresponding diagnostic thresholds. In addition, multivariate logistic regression analysis was conducted to identify independent predictive indicators for autoimmune gastritis, with further assessment in a validation cohort.Results:A total of 139 patients with autoimmune gastritis and 209 patients with H. pylori-induced atrophic gastritis were included. Pepsinogen (PG) Ⅰ levels and the PG Ⅰ/PG Ⅱ ratio in patients with autoimmune gastritis were significantly lower than in those with H. pylori-induced atrophic gastritis [11.0 (4.8, 22.5) vs. 41.8 (32.2, 59.9) μg/L, U=722.00, P<0.001; 1.24 (0.75, 3.54) vs. 5.76 (4.31, 7.12), U=817.00, P<0.001], while gastrin levels were significantly higher [375 (84, 738) vs. 49 (35, 81) ng/L, U=378.00, P<0.001]. PG Ⅰ was identified as an independent predictive variable, with an area under the ROC curve of 0.847 (95% CI 0.791-0.904), sensitivity of 77.6%, specificity of 91.8%, positive predictive value of 80.5%, and negative predictive value of 90.5%. Conclusions:Significant differences in laboratory indicators were observed between autoimmune gastritis and H. pylori-induced atrophic gastritis in chronic atrophic gastritis involving gastric fundus and corpus. Besides, PG Ⅰ demonstrated good diagnostic performance in identifying autoimmune gastritis and can effectively differentiate between different types of atrophic gastritis.

This study aims to describe the population and regional distribution characteristics of smoking behavior among adult twins in the China Twin Registry (CNTR), as well as the concordance rates for smoking behavior in monozygotic and dizygotic twins, and estimate the heritability. The study population included adult twins in CNTR who had smoking questionnaire data. A random-effects regression model was used to describe the distribution of smoking behavior among different subgroups based on various characteristics. The concordance of smoking behavior between different zygosity groups was calculated, and heritability was estimated. A total of 28 444 twin pairs were included in this study, with an average age of (36.6±12.0) years. Among male twins, 41.2% were current smokers, while only 1.2% of females smoked. Higher smoking rates were observed among male smokers in the 50-59 age group ( z=23.0, P<0.001), northern regions ( z=2.9, P<0.01), rural areas ( z=-5.2, P<0.001), those who were divorced/widowed ( z=3.8, P<0.001), and first-born twins ( z=-4.3, P<0.001), while lower smoking rates were found in those with higher education ( z=-16.1, P<0.001) and unmarried individuals ( z=-16.0, P<0.001). The smoking concordance rate for male monozygotic twins was 69.6%, significantly higher than the 57.3% concordance rate for dizygotic twins ( χ 2=105.0, P<0.05). The heritability of smoking behavior in male twins was estimated at 28.9% (95% CI: 24.3%-33.4%). Stratified analyses showed differences in heritability across regions and age groups: the heritability in northern regions was 32.6% (95% CI: 27.3%-38.0%), higher than the 21.0% (95% CI: 12.4%-29.5%) observed in southern regions; the highest heritability of 35.1% (95% CI: 26.3%-43.9%) was found in the 18-29 age group, with heritability decreasing with age. In conclusion, the smoking rate and influencing factors in the twin population are similar to those in the general population, with unique characteristics, such as higher smoking rates in first-born twins. Genetic factors have a significant impact on smoking behavior.

Theory and technology of Medical Immunology are widely used in scientific research.Our teaching and research group uses experimental teaching of Medical Immunology as a platform to carry out practice of scientific research project-based experi-mental system among"5+3"integration students.By completing a mini-project research including experimental design-experimental operation-research article writing,students cultivated scientific research thinking and exercised scientific research practice ability,and generally reported that the course is very difficult,but after completing it,it is very rewarding.

Objective To explore the expression pattern and molecular function of ubiquitin-like containing PHD and RING finger domains 1(UHRF1)gene in soft tissue sarcoma(STS),as well as its correlation with clinical characteristics and prognosis of STS.Methods RNA data and related clinical data of 263 STS tissues were obtained from Cancer Genome Atlas(TCGA).Wilcoxon rank-sum test was used to analyze correlation between two groups of data;Spearman correlation coefficient analyzed the top 35 co-expressed genes positively and negatively correlated with UHRF1 expression in STS database,ggplot2 statistical package displayed co-expressed gene heatmap,Pearson correlation coefficient showed correlation between UHRF1 expression and expression of the top 10 genes in the heatmap;different UHRF1 gene expression groups in STS were analyzed using DESeq2 package,ggplot2 package was used to draw volcano plots,gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyzed differentially expressed genes(DEGs)and protein functions,ggplot2 package for visualization,and cluster Profiler package for statistical analysis;STRING web was used to establish PPI network of DEGs,and the MCC algorithm in CytoHubba of Cytoscape was used to analyze hub genes.Results In STS,UHRF1 gene was significantly correlated with its histological type(liposarcoma 22.2％,synovial sarcoma 3.8％,leiomyosarcoma 40.7％,malignant peripheral nerve sheath tumor 3.8％,myxofibrosarcoma 9.6％,pleomorphic sarcoma 19.9％,P=0.001),tumor necrosis(none 38.8％,focal necrosis 20.8％,moderate necrosis 33.8％,extensive necrosis 6.6％,P=0.010),and tumor metastasis(no metastasis 67％,metastasis 33％,P＜0.001).In different clinical subgroups(age,gender,histological type,residual tumor,tumor necrosis,tumor depth,margin status,tumor multifocality,radiotherapy),high expression of UHRF1 led to poor prognosis of overall survival(OS),disease-specific survival(DSS),and progression-free interval(PFI);Three prognostic factors above were simultaneously shortened in the following five subgroups:namely residual tumor R0 and R1,tumor necrosis extensive,focal and moderate,tumor depth deep,positive margin status,tumor without multifocality.Analysis of the top 10 co-expressed genes associated with UHRF1 expression revealed that the associated positive genes were PAGE5,LINC01425,LCEP3,SERPINB7,AC074031.1,LCE3A,LCE2A,PAGE2B,MYF5,and AC037486.1(P＜0.05);the associated negative genes were CDH19,CSN1S1,TAC3,AC103563.7,SAA1,CHST8,PRLHR,MIR202HG,IGHV1-24,and ART4(P＜0.05).A total of 3 029 DEGs of UHRF1 in STS were obtained with a threshold of|log2 fold-change(FC)|＞1.0 and adjusted P value＜0.05,in which 1 228 genes were up-regulated and 1 801 genes were down-regulated;GO enrichment analyed primary biological processes(BP),original cellular components(CC),and original molecular functions(MF),and KEGG enrichment analyed signaling pathways.A total of 343 DEGs including 133 up-regulated genes and 210 down-regulated genes,were obtained with a threshold of|log2 fold-change(FC)|＞2.0 and adjusted P value＜0.05.The top 10 hub genes were analyzed.The top 3 hub genes were GCG,SST and SHH,respectively.Conclusion UHRF1 is significantly correlated with histological type,tumor necrosis,metastasis,OS,DSS,and PFI events in STS.In co expressed genes model and molecular functions of related positive and negative genes involved in multiple biological processes;The network of differentially expressed genes and protein product interactions involved in mechanisms of occurrence and development of the disease,and provided new ideas for in-depth researches on STS.

With the rapid advancement of artificial intelligence (AI) technology, particularly breakthroughs in large models, AI applications in healthcare are becoming increasingly widespread.Ophthalmology, as a critical branch of medicine, has emerged as a clinical specialty with comprehensive AI research and applications, especially in the AI-driven analysis of ophthalmic imaging.Currently, AI in ophthalmology is undergoing profound transformation driven by large model technology.AI large models, with their robust data comprehension and multimodal interaction capabilities, offer new possibilities for the diagnosis, treatment, and health education of ophthalmic diseases, powerfully propelling the evolution of current AI paradigms in ophthalmology.This article explores the novel opportunities brought by the extensive application of AI large models in ophthalmic research and practice, and provides recommendations for ophthalmologists and insights for industry development.

In recent years, generative artificial intelligence (AI) technologies have achieved remarkable progress in the early screening, risk prediction, disease progression assessment, and clinical trial design of glaucoma.Using advanced algorithms, such as generative adversarial networks, variational autoencoders, and diffusion models, researchers have synthesized high-quality structural images of the optic disc, macular region, and retinal nerve fiber layer, which effectively alleviates the limitations of scarce clinical imaging data and label imbalance.These methods have substantially improved the accuracy and generalization of deep learning models in visual field defect prediction, structure-function mapping, and longitudinal disease progression simulation.Meanwhile, multimodal generative approaches that integrate imaging data, visual field tests, and clinical features have facilitated individualized prediction of glaucoma progression.In addition, large language models have shown preliminary potential in ophthalmic image interpretation, clinical text information extraction, and decision support, providing new insights into intelligent ophthalmic diagnosis and treatment.However, the clinical implementation of generative AI in glaucoma faces challenges.The pathological authenticity and cross-device consistency of generated images require further validation, which may affect the reliability of early glaucoma detection.The heterogeneous characteristics of different glaucoma subtypes, such as open-angle and angle-closure glaucoma, also limit the generalization of synthetic data.Moreover, issues related to model interpretability (" black-box" nature), artifact generation, data privacy, and ethical governance remain key barriers to clinical translation.In the future, it is expected that establishing large-scale training frameworks that incorporate multicenter, multimodal, and multiethnic datasets will enhance model robustness and clinical applicability.Furthermore, generative AI may contribute to remote ophthalmic care and personalized precision therapy by enhancing low-quality image, reconstructing missing data, and simulating dynamic disease courses.This article reviews the current applications, core technologies, and challenges of generative AI in glaucoma diagnosis and management, and discusses its future directions and translational potential in clinical ophthalmology.

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*