ObjectiveBiochemistry and Molecular Biology, a discipline that elucidates life phenomena at the molecular level, serves as a core foundational course in medical education. It provides the theoretical basis for studying other basic and clinical medical subjects, as well as for understanding pathogenesis, disease diagnosis, and treatment. However, its complex content and highly abstract concepts have posed a dual challenge to traditional teaching models: “inefficient instruction” and “inadequate learning outcomes”. Within limited classroom hours, how to engage students and stimulate their intrinsic motivation, and how to help them recognize, understand, and develop a passion for biochemistry from the perspective of the discipline’s essence, have long been key focuses of curriculum research. MethodsUsing the lipid metabolism chapter as an example, this study employs “Rain Classroom”, a generative artificial intelligence (AI)-assisted platform, to support education in four dimensions: teaching, learning, evaluation, and research. In teaching, it assists instructors through virtual experiments, lesson preparation support, knowledge mapping, and assignment design. For learning, it serves as an intelligent study assistant for students, providing automated assignment review, enabling educational resource sharing, and facilitating personalized learning pathways. In evaluation, the platform automates assignment grading, analyzes student performance data, and offers diagnostic feedback and teaching recommendations. In research, it aids educators in collecting and analyzing teaching data, as well as searching for and summarizing relevant literature. ResultsThe results indicate that an educational model integrating teacher-led instruction, student-centered learning, and generative AI assistance significantly enhances teaching quality, students’ self-directed learning abilities, and knowledge mastery. Furthermore, with the support of generative AI, curriculum-based ideological education—focusing on cutting-edge disciplinary advances and topical medical issues—helps cultivate students’ medical spirit of “honoring life and healing the wounded”, thereby fostering the establishment of appropriate professional values. Finally, while generative AI presents both opportunities and challenges for higher education, this study also analyzes potential risks in its teaching applications, emphasizing the need for both instructors and students to avoid over-reliance and to ensure that technological tools consistently serve the fundamental goals of education. ConclusionThis study demonstrates that integrating generative AI, specifically via the “Rain Classroom” platform, can effectively enhance biochemistry education. By supporting teaching, learning, evaluation, and research, this approach improves both educational effectiveness and student outcomes. It also facilitates the incorporation of cutting-edge knowledge and professional ethics, nurturing a patient-centered mindset. Additionally, the study addresses potential implementation risks to ensure that such technological tools remain aligned with the core purpose of education.

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 explore the methods of continuing education for advanced endoscope operations by diges-tive specialists through the establishment and teaching effect evaluation of the ERCP(endoscope retrograde cholan-giopancreatography)introductory training mode.Methods A total of 26 trainees from 3 sessions of the ERCP intro-ductory training courses at Peking Union Medical College Hospital from September 2023 to September 2024 were in-cluded.The teaching effects of the training courses and its 5 modules were subjectively and objectively evaluated by questionnaires,on-site tests and evaluations by senior ERCP operators.Results Through the ERCP introductory training courses,the trainees'self-evaluated proficiency in duodenoscope structure(pre-training:2.4±2.4,post-training:8.2±1.5,P＜0.001),duodenoscope operation(pre-training:1.2±2.2,post-training:6.6±1.8,P＜0.001),papillary cannulation(pre-training:0.5±1.3,post-training:5.4±1.8,P＜0.001),intra-bile duct operation(pre-training:0.2±0.6,post-training:4.9±2.1,P＜0.001),and identification of intra-bile duct lesions(pre-training:1.7±2.1,post-training:6.0±2.0,P＜0.001)was significantly improved.The accuracy rate of the trainees'theoretical tests and picture recognition before training was 37.2％and then increased up to 62.8％after training.Before training,all trainees were considered by senior operators as not ready to start ERCP training on real patients,while after training,69.2％(18/26)of the trainees were considered ready to start ERCP training on real patients.Conclusions The multi-module ERCP introductory training courses have a significant effect in terms of laying a foundation for trainees to start ERCP training on patients and of providing a reference for the con-tinuing education mode of advanced endoscope operations for digestive specialists in China.

The working principle of the flat panel detector of Siemens Mammomat Inspiration breast machine was explained.The failures of the flat panel detector and its power source were analyzed in terms of the cause and elimination method.References were provided for medical engineers to treat similar failures.[Chinese Medical Equipment Journal,2025,46(7):117-120]

The working principle of the flat panel detector of Siemens Mammomat Inspiration breast machine was explained.The failures of the flat panel detector and its power source were analyzed in terms of the cause and elimination method.References were provided for medical engineers to treat similar failures.[Chinese Medical Equipment Journal,2025,46(7):117-120]

Artificial intelligence(AI)is a strategic technology leading a new round of technological revolution and industrial transformation.It is forward-looking,important and necessary to apply AI technology to the medical field.At present,the research and development of intelligent data monitoring equipment,intelligent medical instruments,disease auxiliary diagnosis and treatment platforms,auxiliary diagnosis and treatment integrated systems and other technologies have been widely carried out,and related products are gradually used in auxiliary medical prevention,diagnosis,treatment,and rehabilitation.Based on the recent development of AI technology in the medical field,the application status of intelligent auxiliary diagnosis and treatment equipment in three fields of intelligent monitoring equipment,virtual psychological diagnosis and treatment platforms,and traditional Chinese medicine auxiliary diagnosis and treatment instruments was summarized to provide reference for AI to connect disease and health,and realize the intersection of AI and medical disciplines.

Objective:To evaluate the diagnostic value of rapid on-site evaluation (ROSE) performed by endoscopists for solid pancreatic lesions requiring tissue for immunohistochemistry (IHC) staining with different approach of endoscopic ultrasound-guided tissue acquisition (EUS-TA).Methods:After screening 1 573 cases who underwent EUS-TA operation at the Endoscopy Center of Peking Union Medical College Hospital between August 2018 and October 2022, a total of 65 cases of solid pancreatic lesions whose diagnosis rely on IHC staining was collected and summarized with clinical data of each case. Among 65 cases, there were 46 cases of pancreatic neuroendocrine tumors (PNETs), 13 cases of pancreatic solid pseudo-papillary tumors (SPTs), and 6 cases of lymphomas and mesenchymal. Patients were categorized into ROSE group (36 cases) and non-ROSE group (29 cases) according to the presence or absence of endoscopists performed ROSE during EUS-TA operation. They were further divided into subgroups of FNA-ROSE (26 cases), FNB-ROSE (10 cases), FNA-non-ROSE (24 cases) and FNB-non-ROSE (5 cases) according to the type of EUS-TA. Diagnostic accuracy and IHC success rate were compared between different groups and subgroups. Binomial logistic multifactorial regression analysis was used to evaluate the influence of ROSE and EUS-TA type on diagnostic accuracy and IHC success rate.Results:There were no statistically significant differences between ROSE group and non-ROSE group in terms of age, gender, bilirubin level, CA19-9 level, lesion site, lesion size, composition ratio of diagnosis, and surgical rate. The differences in mean size of lesions, needle gauge, location of puncturation, and number of needle pass between subgroups were not statistically significant. The diagnostic accuracy was 88.9% in ROSE group and 79.3% in non-ROSE group, and the difference between the two groups was statistically significant ( P=0.023). The diagnostic accuracy of FNA-ROSE group was higher than that of FNA-non-ROSE group (88.5% vs 75.0%), but the difference was not statistically significant ( P>0.100). The differences in diagnostic accuracy and success rate of IHC between FNB-ROSE group and FNB-non-ROSE group were not statistically significant. Binomial logistic multifactorial regression analysis did not reveal any independent influences on diagnostic accuracy. Conclusions:ROSE performed by endoscopists improved diagnostic accuracy of EUS-TA in solid pancreatic lesions requiring IHC staining, and therefore is potentially valuable for improving the diagnostic efficiency of EUS-TA for such diseases.

Stroke is one of the most serious complications of transcatheter aortic valve replacement(TAVR),tremendously increasing mortality and the loss of neurocognitive function.Since TAVR is expected to further spread into lower-risk patient groups,there will be greater emphasis to obviate such serious complications.One possible technique for preventing stroke is using cerebral embolic protection devices(CEPDs).CEPDs are designed for capturing or deflecting emboli that are enter route to the brain and hence to protect the brain from embolism.Since this is a rapidly growing field with recent advances,and the impact of CEPD on preventing neurological events is still limited,there is an urgent need for understanding the role of CEPD in preventing clinically significant strokes.Although their clinical utilization is increasing,the risk factors for stroke related to TAVR and evidence for using CEPDs are not yet clear.In this review,we present an overview of the available literature on TAVR related stroke and CEPD,and outline recent advances within this field.