With the rapid development of artificial intelligence(AI),how to digitize the teaching of Medical Immunology is a new challenge posed by the times and education.This study is based on the advanced teaching model of Medical Immunology,which includes lectures-PAD class-flipped classrooms-expert lecture.By introducing knowledge mapping and AI teaching assistant into the entire learning process,the students not only deepen their understanding of the knowledge system of Medical Immunology,but also ex-ercise their ability to apply immunological knowledge to solve practical clinical problems,enhance their self-learning ability,expres-sion ability,communication ability,on-site performance ability,and cultivate a spirit of unity,cooperation,and exploration.The practice of empowering Medical Immunology teaching with digital intelligence achieves the integration of theory and application,the linkage between in class and out of class teaching,the connection between commonalities and individualities,and the union of abili-ties and qualities in Medical Immunology teaching.It also provides practical basis for exploring the implementation path of digital intel-ligence empowerment in Medical Immunology teaching.

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 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.

Objective:To analyze the registration status of clinical trials related to contraceptives based on the data of World Health Organization International Clinical Trials Registry Platform (ICTRP), so as to understand the situation of contraceptives development, and provide reference for improving the quality of clinical trials.Methods:All records in the database of ICTRP before January 1, 2025 was researched to screen out contraceptives clinical trials, and the registration time, registration platform, regional distribution, trial design and research content were analyzed.Results:A total of 206 clinical trials related to contraceptives were included. The projects were mainly carried out in the United States, Germany, and India. About 95.63% (197/206) of the study subjects were women of reproductive age. The　main　type　of　study　was　intervention [89.32% (184/206)], among which random trials accounted for 53.26% (98/184), and parallel group trials were the most common [82.65% (81/98)]. Over 64.56% (133/206) of the trials were indicated with open-label. Most of the clinical trials were in phase Ⅲ [47.57% (98/206)] and phase Ⅳ [16.50% (34/206)]. The interventions in female studies were mainly in three aspects: oral contraceptive [42.64% (84/197)], intrauterine delivery [15.23% (30/197)], and vaginal delivery [15.23% (30/197)], and the primary outcome included pregnancy rate [54.82% (108/197)], adverse drug reactions [13.20% (26/197)], ovulation inhibition [8.63% (17/197)], and vaginal infection [4.06% (8/197)].Conclusion:The　clinical　trials　of contraceptives were unevenly distributed, the research types were mainly randomized interventional trials, the included participants were mainly women of reproductive age, the intervention measures were mostly oral contraceptive, intrauterine delivery and vaginal delivery.

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 Based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses(PRISMA)guidelines,to obtain precise and reliable comprehensive effect conclusions by quantitatively combining pharmacodynamic results from animal experiments investigating Astragali Radix(single-entity Astragali Radix preparation)or its ingredients for treatment of acute pancreatitis(AP)in literature reports through meta-analysis.Methods Databases including China National Knowledge Infrastructure(CNKI),VIP Database for Chinese Technical Periodicals(VIP),Wanfang Data Knowledge Service Platform,China Biomedical Literature Database(CBMdisc),PubMed,and Web of Science(WOS)were searched from inception to March 2025 for animal studies related to Astragali Radix(single-entity Astragali Radix preparation)or its ingredients for AP treatment.Risk of bias for included studies was assessed with SYRCLE tool.Heterogeneity among studies was evaluated according to Cochrane Handbook using Cochrane's Qtest and/2statistic.Sensitivity analysis was performed using the leave-one-out method,and publication bias risk was detected using Egger's test.Results A total of 297 articles were retrieved,and after screening and evaluation,19 animal studies were finally included for meta-analysis.These 19 publications cover SD rats,as well as three breeds of mice:C57BL/6 mice,BALB/c mice,and Kunming mice.SYRCLE scores ranged from 3 to 4.The results of the sensitivity analysis showed that no study significantly affected the heterogeneity index.The results of Egger's test showed a significant publication bias with P＜0.05.Cochrane's Qtest and I2statistic indicated substantial heterogeneity among studies.Meta-analysis results of 19 animal studies showed that single-entity Astragali Radix preparation(Astragali Radix injection)could reduce serum amylase(AMY)levels,an AP-specific indicator.The Astragali Radix ingredients could decrease both AMY and lipase(LPS)levels.Astragali Radix injection or its ingredients could reduce serum levels of tumor necrosis factor-α(TNF-α),interleukin(IL)-6,and IL-1 β,while increasing IL-10 levels;could increase serum levels of superoxide dismutase(SOD)and glutathione peroxidase(GSH-Px),and decrease malondialdehyde(MDA)levels.High-dose groups of Astragali Radix injection or Astragali Radix ingredients were more effective than low-dose groups in reducing AMY,TNF-α,and IL-6 levels and increasing SOD levels,but dosage effect on MDA levels was not demonstrated.Conclusion Evidence-based analysis of animal experiment results shows that in various animal models including SD rats,C57BL/6 mice,BALB/c mice,and Kunming mice,Astragali Radix injection or its ingredients can effectively reduce expression or secretion levels of AP-specific indicators(AMY and LPS).The mechanisms may be related to some inflammatory mediators,including reducing TNF-α,IL-6,and IL-1 β levels and increasing IL-10 levels;They may also intervene in oxidative/antioxidative equilibrium,such as increasing SOD and GSH-Px levels and reducing MDA levels.Except for MDA,dose-response relationships are shown for reducing AMY,TNF-α,and IL-6 levels and increasing SOD levels with Astragali Radix injection or its ingredients.However,due to high heterogeneity,potential publication bias risk,and species differences between animal models and human diseases in existing studies,further high-quality clinical trials or animal experiments are still needed in the future.

AIM:To investigate the association between single nucleotide polymorphisms(SNPs)in the silent information regulator 1(SIRT1)gene and breast cancer risk in the Han Chinese population.METHODS:A total of 105 Han Chinese patients with breast cancer and 90 healthy controls were enrolled.Sequenom MassARRAY was used to detect the genotypes of SIRT1 gene loci,rs3740051,rs3758391,rs12778366 and rs2394443.The Hardy-Weinberg equilibrium(HWE)was analysed using the chi-square test.Multivariate logistic regression was employed to analyze the correlation be-tween each SNP and breast cancer susceptibility,as well as the relationship between the rs3758391 genotype and the clini-copathological characteristics of breast cancer in Han Chinese women.SHEsis software was used to assess linkage disequi-librium and haplotypes of the selected SNPs.The impact of genotypes at rs3758391 locus on SIRT1 protein expression was examined using Western blot.An additional 150 Han Chinese women with breast cancer and 150 healthy controls were en-rolled,and SIRT1 protein expression was assessed using immunohistochemistry.Logistic regression was performed to as-sess the relationship between high and low SIRT1 expression and the clinical characteristics of breast cancer.Kaplan-Mei-er website was used to determine the association between SIRT1 expression and patient prognosis.RESULTS:All four SNP loci conformed to the HWE test(P>0.05).The TC/TC+CC genotype of the SIRT1 rs3758391 locus significantly re-duced the risk of breast cancer compared with the TT genotype(TT vs TC:ORadj=0.348,95%CI:0.157～0.773,Padj=0.010;TT vs TC+CC:ORadj=0.381,95%CI:0.179～0.811,Padj=0.012),and correlated with earlier disease course(stage I/II),smaller tumor volume,and higher SIRT1 protein expression levels(P<0.05).SIRT1 expression was signifi-cantly lower in breast cancer tissues,and low SIRT1 expression was associated with larger tumor size,lymph node metasta-sis,and reduced recurrence-free survival(P<0.05).CONCLUSION:The TC/TC+CC genotype of the SIRT1 rs3758391 locus may be a protective factor for breast cancer in Han Chinese women,potentially reducing the risk of breast cancer and delaying disease progression by regulating SIRT1 expression.In addition,SIRT1 expression level is closely related to the clinical characteristics and prognosis of breast cancer.

Objective:To explore the mediating effect of self-rated health status between living space and social frailty of the elderly in community, and to provide reference for intervention strategies.Methods:From December 2023 to March 2024, 550 community elderly in Yandun Community, Yicheng Community and Binhu Community in Baohe District of Hefei City were selected as the research objects by convenient sampling method. The general information questionnaire, self-rated health questionnaire, social frailty index and living space assessment scale were used to investigate a cross-sectional survey. Spearman correlation analysis was used to explore the correlation between self-rated health, living space and social frailty of community elderly. Bootstrap method was used to test the mediating effect.Results:A total of 542 valid questionnaires were collected. Among the 542 elderly residents in the community, aged 65-94 (71.69 ± 6.54) years, there were 267 males and 275 females. Among the 542 community elderly, the self-rated health score, living space score and social frailty index were (2.52 ± 0.95), (73.39 ± 16.77) and 0.0 (0.0, 3.0), respectively. Spearman analysis showed that the total score of living space was negatively correlated with the scores of self-rated health and social frailty ( r=-0.477, -0.538, both P<0.01), and the score of self-rated health was positively correlated with the score of social frailty ( r=0.711, P<0.01).The mediating effect results showed that the living space could play a partial mediating effect on social frailty through the mediating variable of self-rated health (-0.028), accounting for 49.12% of the total effect. Conclusions:The self-rated health status of the elderly in the community has an indirect mediating effect between living space and social frailty. Medical workers should take the improvement of the living space mobility of the elderly as the starting point, and improve their self-rated health level through diversified intervention means, so as to reduce the occurrence of social frailty and promote active aging.