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.

Objective:To investigate the short-term efficacy of robotic versus laparoscopic-assisted right hemicolectomy.Methods:The propensity score matching and retrospective cohort study was conducted. The clinicopathological data of 99 patients of right colon cancer who were admitted to Tianjin Medical University General Hospital from January 2020 to December 2023 were collected. There were 50 males and 49 females, aged 69(range, 26?89)years. Of the 99 patients, 41 patients undergoing robotic-assisted right hemicolectomy were divided into the robotic group, and 58 patients undergoing laparoscopic-assisted right hemicolectomy were divided into the lapa-roscopic group. Patients received robotic-assisted or laparoscopic-assisted right hemicolectomy operated by the same major surgeon. Observation indicators:(1) propensity score matching status and com-parison of clinical data of patients between the two groups after matching; (2) intraoperative and postoperative conditions.Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Comparison of ordinal data was conducted using the nonparametic rank sum test. Propensity score matching was performed using the 1∶1 nearest neighbor matching method. The caliper value was set as 0.1. Results:(1) Propensity score matching status and comparison of clinical data of patients between the two groups after matching. Of the 99 patients, 82 patients were successfully matched, with 41 cases in each of the robotic group and the laparoscopic group. After propensity score matching, the elimination of history of abdominal operation confounding bias ensured comparability. (2) Intraoperative and postoperative conditions. After propensity score matching, the operation time of the robotic group was 215(range, 130?340)minutes, the volume of intraoperative blood loss was 50(range, 10?400)mL, the number of lymph node dissected was 21(range, 5?55), the number of intensive care unit stay was 15, time to postoperative first flatus was 3(range, 1?12)days, time to postoperative first food intake was 4(range, 2?14)days, duration of postoperative hospital stay was 8(range, 5?25)days. The above indicators of the laparoscopic group were 210(range, 140?370)minutes, 50(range, 5?150)mL, 19(range, 5?34),20, 3(range, 0?9)days, 5(range, 2?10)days, 8(range, 6?17)days, respectively. There was no significant difference in the above indicators between patients of the two groups ( Z=?0.94, ?1.87, ?1.32, χ2=1.25, Z=0.13, ?0.83, ?0.65, P>0.05). There was no patient converted to open operation in the robotic group, versus 1 patient converted to open operation in the laparoscopic group, showing no significant difference between patients of the two groups ( P>0.05). There were 6 cases in the robotic group and 4 cases in the laparoscopic group with complications, showing no significant difference between the two groups ( χ2=0.46, P>0.05). Both groups of patients achieved R 0 resection and had no readmission 30 days after surgery. The hospital expense was (11.0±1.8)×10 4 yuan of the robotic group, versus (9.0±1.7)×10 4 yuan of the laparoscopic group, showing a significant difference between the two groups ( t=?5.27, P<0.05). Conclusion:Robot-assisted right hemicolectomy is non inferior to laparoscopic-assisted right hemicolectomy in safety and efficacy, but with higher hospitalization costs.

Objective To investigate the status and influencing factors of the coexistence of multiple frailty domains in patients with chronic heart failure(CHF),and to provide a reference for formulating targeted intervention measures.Methods The patients with CHF hospitalized in the cardiovascular department of a tertiary general hospital in Tianjin from November 2021 to December 2022 were conveniently selected as the study subjects.The self-designed general information questionnaire,FRAIL scale,Montreal Cognitive Assessment(MoCA)scale,HALFT Scale,Social Support Rating Scale and Self-Rating Depression Scale were used to investigate the coexistence of physical,cognitive and social frailty in CHF patients,and single factor analysis and binary logistic regression analysis were used to analyze the influencing factors.Results A total of 275 questionnaires were issued and 269 valid questionnaires were collected.Among 269 patients with CHF,12(4.5％),67(24.9％),122(45.4％)and 68(25.3％)patients had 0,1,2 and 3 frailty domains,respectively.According to the patients with CHF whether coexists physical,cognitive,social frailty,divided into coexistence and non-coexistence group.In univariate analysis,there were statistically significant differences in education,residence,family history of cardiovascular disease,NYHA cardiac function grade,atrial fibrillation,BMI,social support score and depression score(P＜0.05).Binary logistic regression analysis showed that high education level(OR=0.567,95％CI 0.397～0.810,P=0.002)was the protective factor for multiple frailty domains in CHF patients,and atrial fibrillation(OR=1.904,95％CI 1.054～3.442,P=0.033)and depression(OR=1.038,95％CI 1.006～1.071,P=0.018)were risk factors.Conclusion The prevalence of the coexistence of multiple frailty domains in CHF patients is high.Education level,atrial fibrillation and depression are potential intervention targets for multiple frailty domains in CHF patients.Targeted intervention measures should be carried out to reduce the incidence of multiple frailty domains and to improve quality of life for CHF patients.

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.

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.

Objective To investigate the status and influencing factors of the coexistence of multiple frailty domains in patients with chronic heart failure(CHF),and to provide a reference for formulating targeted intervention measures.Methods The patients with CHF hospitalized in the cardiovascular department of a tertiary general hospital in Tianjin from November 2021 to December 2022 were conveniently selected as the study subjects.The self-designed general information questionnaire,FRAIL scale,Montreal Cognitive Assessment(MoCA)scale,HALFT Scale,Social Support Rating Scale and Self-Rating Depression Scale were used to investigate the coexistence of physical,cognitive and social frailty in CHF patients,and single factor analysis and binary logistic regression analysis were used to analyze the influencing factors.Results A total of 275 questionnaires were issued and 269 valid questionnaires were collected.Among 269 patients with CHF,12(4.5％),67(24.9％),122(45.4％)and 68(25.3％)patients had 0,1,2 and 3 frailty domains,respectively.According to the patients with CHF whether coexists physical,cognitive,social frailty,divided into coexistence and non-coexistence group.In univariate analysis,there were statistically significant differences in education,residence,family history of cardiovascular disease,NYHA cardiac function grade,atrial fibrillation,BMI,social support score and depression score(P＜0.05).Binary logistic regression analysis showed that high education level(OR=0.567,95％CI 0.397～0.810,P=0.002)was the protective factor for multiple frailty domains in CHF patients,and atrial fibrillation(OR=1.904,95％CI 1.054～3.442,P=0.033)and depression(OR=1.038,95％CI 1.006～1.071,P=0.018)were risk factors.Conclusion The prevalence of the coexistence of multiple frailty domains in CHF patients is high.Education level,atrial fibrillation and depression are potential intervention targets for multiple frailty domains in CHF patients.Targeted intervention measures should be carried out to reduce the incidence of multiple frailty domains and to improve quality of life for CHF patients.

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.

Objective:To investigate the short-term efficacy of robotic versus laparoscopic-assisted right hemicolectomy.Methods:The propensity score matching and retrospective cohort study was conducted. The clinicopathological data of 99 patients of right colon cancer who were admitted to Tianjin Medical University General Hospital from January 2020 to December 2023 were collected. There were 50 males and 49 females, aged 69(range, 26?89)years. Of the 99 patients, 41 patients undergoing robotic-assisted right hemicolectomy were divided into the robotic group, and 58 patients undergoing laparoscopic-assisted right hemicolectomy were divided into the lapa-roscopic group. Patients received robotic-assisted or laparoscopic-assisted right hemicolectomy operated by the same major surgeon. Observation indicators:(1) propensity score matching status and com-parison of clinical data of patients between the two groups after matching; (2) intraoperative and postoperative conditions.Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Comparison of ordinal data was conducted using the nonparametic rank sum test. Propensity score matching was performed using the 1∶1 nearest neighbor matching method. The caliper value was set as 0.1. Results:(1) Propensity score matching status and comparison of clinical data of patients between the two groups after matching. Of the 99 patients, 82 patients were successfully matched, with 41 cases in each of the robotic group and the laparoscopic group. After propensity score matching, the elimination of history of abdominal operation confounding bias ensured comparability. (2) Intraoperative and postoperative conditions. After propensity score matching, the operation time of the robotic group was 215(range, 130?340)minutes, the volume of intraoperative blood loss was 50(range, 10?400)mL, the number of lymph node dissected was 21(range, 5?55), the number of intensive care unit stay was 15, time to postoperative first flatus was 3(range, 1?12)days, time to postoperative first food intake was 4(range, 2?14)days, duration of postoperative hospital stay was 8(range, 5?25)days. The above indicators of the laparoscopic group were 210(range, 140?370)minutes, 50(range, 5?150)mL, 19(range, 5?34),20, 3(range, 0?9)days, 5(range, 2?10)days, 8(range, 6?17)days, respectively. There was no significant difference in the above indicators between patients of the two groups ( Z=?0.94, ?1.87, ?1.32, χ2=1.25, Z=0.13, ?0.83, ?0.65, P>0.05). There was no patient converted to open operation in the robotic group, versus 1 patient converted to open operation in the laparoscopic group, showing no significant difference between patients of the two groups ( P>0.05). There were 6 cases in the robotic group and 4 cases in the laparoscopic group with complications, showing no significant difference between the two groups ( χ2=0.46, P>0.05). Both groups of patients achieved R 0 resection and had no readmission 30 days after surgery. The hospital expense was (11.0±1.8)×10 4 yuan of the robotic group, versus (9.0±1.7)×10 4 yuan of the laparoscopic group, showing a significant difference between the two groups ( t=?5.27, P<0.05). Conclusion:Robot-assisted right hemicolectomy is non inferior to laparoscopic-assisted right hemicolectomy in safety and efficacy, but with higher hospitalization costs.