With the advancement of the "New Medical Science" reform, the "Medicine+X" model has emerged as a key direction for the future development of medical education. Multidisciplinary integration places higher demands on both educators and students. Emerging technologies, such as intelligent tutoring systems, adaptive learning platforms, intelligent campus management systems, and ChatGPT, have made personalized learning possible. Such approaches offer notable advantages, including improving learning efficiency, enhancing motivation, eliminating the spatiotemporal constraints of clinical education, and alleviating teachers′ workloads. Nevertheless, the application of artificial intelligence in personalized medical education still faces multiple challenges, such as issues of data quality and reliability, the need for faculty development, shifts in educational paradigms, and ethical considerations. This study explored the current status of artificial intelligence in personalized medical education and offered recommendations to promote its development, including strengthening the integration of technology and education, enhancing the digital literacy of educators, establishing ethical guidelines, and fostering multi-stakeholder collaboration.

In traditional teaching, medical students have limited opportunities to interact with patients, which constrains the development of their clinical skills. Virtual standardized patients offer a potential solution to this limitation. This article analyzes the advantages of virtual standardized patients and their application in clinical teaching.

Medical students often struggle to understand and master the relevant knowledge and skills in teaching, especially in surgical teaching. Emerging 3D printing technology can help students to understand and master surgical techniques. The problem-based learning (PBL) teaching method helps students to develop their independent thinking and teamwork skills. The combination of these methods has already achieved significant success. Therefore, this article discusses the application and combining 3D printing technology with the PBL teaching method in medical teaching, particularly in urological surgery education, and provides new ideas and references for future, more diverse, and high-tech medical education.

Objective:To investigate the effects of cardiomyocyte-specific TSHR knockout on myocardial insulin resistance in a mouse model of heart failure.Methods:A cardiomyocyte-specific TSHR knockout(TSHR CKO) mouse model was generated by crossing TSHR flox/flox mice with α-MHC-Cre transgenic mice. F1 offspring(TSHR flox+ /-α-MHC-Cre+ mice) were interbred to obtain TSHR CKO mice, and littermate TSHR flox/flox mice served as controls. Fasting blood glucose levels were measured using a Roche glucometer, and fasting insulin levels were determined using a mouse insulin ELISA kit. Cardiac function and the expression of ANP, BNP, β-MHC, IRS-1, IRβ, GLUT-4, phosphorylated IRS-1, phosphorylated IRβ, and TSHR in myocardial tissues were assessed by echocardiography, RT-qPCR, Western blot, and immunohistochemistry(IHC). IHC was also used to evaluate the myocardial expression of IRS-1 and GLUT-4, while Masson′s trichrome staining was performed to assess the degree of myocardial fibrosis. Comparisons between groups were made using ANOVA. Results:The insulin resistance index indicated no systemic insulin resistance in all groups. Echocardiography revealed that compared with the FLOX group, the FLOX-ISO group exhibited significant reductions in left ventricular ejection fraction(LVEF), left ventricular fractional shortening(LVFS), left ventricular end-systolic volume(LVESV), and left ventricular end-diastolic volume(LVEDV), along with increases in heart weight-to-body weight(HW/BW), left ventricular end-systolic diameter(LVESD), and left ventricular end-diastolic diameter(LVEDD). Compared with the FLOX-ISO group, the CKO-ISO group showed significantly increased LVEF and decreased LVESV, LVEDV, LVESD, and LVEDD. Immunohistochemistry results demonstrated that myocardial TSHR knockout increased the expression of IRS-1 and GLUT-4. Additionally, RT-qPCR and Western blotting showed that ANP, BNP, and β-MHC expression levels were reduced, while IRS-1, IRβ, and GLUT-4 expression levels were elevated in TSHR CKO mice. Conclusion:Cardiomyocyte-specific TSHR knockout improves myocardial insulin resistance in mice with heart failure.

Objective:To explore the predictive value of hepatitis B surface antibody (HBsAb) quantitative level for achieving hepatitis B surface antigen (HBsAg) seroclearance and serological conversion in patients with chronic hepatitis B (CHB) treated with nucleos(t)ide analogs (NAs) or interferon (IFN).Methods:A two-center prospective cohort study was conducted, including CHB patients from Nanfang Hospital Southern Medical University and Eastern Theater General Hospital treated with NAs and IFN. All patients were followed up once every three to six months. Basic clinical information and test results were collected at each follow-up. The presence or absence of HBsAg seroclearance and serological conversion rate was evaluated. HBsAg serological conversion was defined as HBsAg quantification continuously below the detection limit (<0.05 IU/mL) at two detection time points at least six months apart. HBsAg serological conversion was defined as HBsAb positivity (≥10 IU/L) at the same time as the first HBsAg seroclearance. The Kruskal-Wallis test was used to compare the quantitative data of multiple groups, and the Wilcoxon rank-sum test was used to compare the data between groups. The chi-square test was used for the count data, and the Fisher exact test was used when the chi-square test was not met. Univariate and multivariate Cox analysis was used to determine the predictors of the study endpoints, and stepwise regression was used for variable screening.Results:A total of 2 266 CHB cases were included, of which 86.5% (1 959/2 266) were NA antiviral-received population. The median treatment duration before baseline was 10.5 (2.5, 37.6) months, and the baseline HBsAg quantification was 3.1 (2.6, 3.5) log 10 IU/mL. A total of 68 cases (3.0%) had HBsAg seroclearance, and 44 cases (1.9%) achieved serological conversion after 85.0 (62.7, 97.3) months of prospective follow-up. The level and positivity rate of HBsAb showed a progressive increase 36 months before and significantly after HBsAg seroclearance. Cox regression analysis results showed that baseline HBsAb level was an independent predictor of HBsAg serological conversion ( HR=2.26, P=0.002) in the overall population, especially in the subgroup with HBsAg between 100 and 1 000 IU/mL, suggesting HBsAb level had important predictive value. In addition, the serological conversion development rate was significantly higher in the GOLDEN model favourable patients than in the unfavourable patients (11.5% vs. 0, P<0.001). Conclusion:The baseline HBsAb quantitative level can predict HBsAg seroclearance and serological conversion for patients with CHB receiving antiviral treatment, which is of significant value in long-term treatment monitoring.

Rare diseases pose significant diagnostic and therapeutic challenges,carrying a high disease burden,their management critically reflects a nation's public health resilience.Currently,China faces key challenges such as scarce treatments,fragmented services,and low drug accessibility in rare disease care,which urgently require systemic solutions.Digital-intelligent technology as a key breakthrough are expected to resolve the challenges in this field.Although its application in the field of rare diseases is gradually expanding,there is a lack of systematic compilation of studies to elucidate how to precisely enhance the precision,synergy and sustainability of diagnosis and treatment.The key challenges in rare disease care concentrate in four areas:inefficiency in prenatal screening,uneven distribution of medical resources,low efficiency in social organization collaboration,and ineffective information dissemination.The"4C"strategy,based on digital-intelligent technology,can address these issues:①coordination,boost prenatal screening awareness and capacity via digital-intelligent platforms to strengthen prevention;②cooperation,deepen collaboration within specialist networks,empowering institutions to enhance diagnostic capacity;③co-creation,empower support organizations to optimize resources,efficiency;④cognition,minimize information dissipation through efficient platforms,improving patient and family quality of life.This establishes an integrated digital-intelligent rare disease model encompassing"screening-diagnosis-treatment-care".

Rare diseases pose significant diagnostic and therapeutic challenges,carrying a high disease burden,their management critically reflects a nation's public health resilience.Currently,China faces key challenges such as scarce treatments,fragmented services,and low drug accessibility in rare disease care,which urgently require systemic solutions.Digital-intelligent technology as a key breakthrough are expected to resolve the challenges in this field.Although its application in the field of rare diseases is gradually expanding,there is a lack of systematic compilation of studies to elucidate how to precisely enhance the precision,synergy and sustainability of diagnosis and treatment.The key challenges in rare disease care concentrate in four areas:inefficiency in prenatal screening,uneven distribution of medical resources,low efficiency in social organization collaboration,and ineffective information dissemination.The"4C"strategy,based on digital-intelligent technology,can address these issues:①coordination,boost prenatal screening awareness and capacity via digital-intelligent platforms to strengthen prevention;②cooperation,deepen collaboration within specialist networks,empowering institutions to enhance diagnostic capacity;③co-creation,empower support organizations to optimize resources,efficiency;④cognition,minimize information dissipation through efficient platforms,improving patient and family quality of life.This establishes an integrated digital-intelligent rare disease model encompassing"screening-diagnosis-treatment-care".

With the advancement of the "New Medical Science" reform, the "Medicine+X" model has emerged as a key direction for the future development of medical education. Multidisciplinary integration places higher demands on both educators and students. Emerging technologies, such as intelligent tutoring systems, adaptive learning platforms, intelligent campus management systems, and ChatGPT, have made personalized learning possible. Such approaches offer notable advantages, including improving learning efficiency, enhancing motivation, eliminating the spatiotemporal constraints of clinical education, and alleviating teachers′ workloads. Nevertheless, the application of artificial intelligence in personalized medical education still faces multiple challenges, such as issues of data quality and reliability, the need for faculty development, shifts in educational paradigms, and ethical considerations. This study explored the current status of artificial intelligence in personalized medical education and offered recommendations to promote its development, including strengthening the integration of technology and education, enhancing the digital literacy of educators, establishing ethical guidelines, and fostering multi-stakeholder collaboration.

In traditional teaching, medical students have limited opportunities to interact with patients, which constrains the development of their clinical skills. Virtual standardized patients offer a potential solution to this limitation. This article analyzes the advantages of virtual standardized patients and their application in clinical teaching.

Medical students often struggle to understand and master the relevant knowledge and skills in teaching, especially in surgical teaching. Emerging 3D printing technology can help students to understand and master surgical techniques. The problem-based learning (PBL) teaching method helps students to develop their independent thinking and teamwork skills. The combination of these methods has already achieved significant success. Therefore, this article discusses the application and combining 3D printing technology with the PBL teaching method in medical teaching, particularly in urological surgery education, and provides new ideas and references for future, more diverse, and high-tech medical education.