Childhood simple obesity has become a significant public health issue in China. Modern medicine primarily relies on lifestyle interventions and often suffers from poor long-term compliance， while pharmacological options are limited and associated with potential adverse effects. Traditional Chinese Medicine （TCM） has a long history in the prevention and management of this condition， demonstrating eight distinct advantages， including systematic theoretical foundation， diversified therapeutic approaches， definite therapeutic efficacy， high safety profile， good patient compliance， comprehensive intervention strategies， emphasis on prevention， and stepwise treatment protocols. Additionally， TCM is characterized by six distinctive features： the use of natural medicinal substances， non-invasive external therapies， integration of medicinal dietetics， simple exercise regimens， precise syndrome differentiation， and diverse dosage forms. By combining internal and external treatments， TCM facilitates individualized regimen adjustment and holistic regulation， demonstrating remarkable effects in improving obesity-related metabolic indicators， regulating constitutional imbalance， and promoting healthy behaviors. However， challenges remain， such as inconsistent operational standards， insufficient high-quality clinical evidence， and a gap between basic research and clinical application. Future efforts should focus on accelerating the standardization of TCM diagnosis and treatment， conducting multicenter randomized controlled trials， and fostering interdisciplinary integration， so as to enhance the scientific validity and international recognition of TCM in the prevention and treatment of childhood obesity.

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.

The current clinical treatment of bladder cancer (BCa) is mainly surgical treatment，supplemented by postoperative chemotherapy and immunotherapy.However，due to the lack of specificity，targeting and other reasons，the therapeutic effect is not satisfactory.In recent years，it has been found that metal nanoparticles (MNPs) prepared by gold，silver，and so on，as bladder infusion drugs or drug carriers，can not only accurately target BCa cells，but also have high stability and drug release rate，thereby reducing the side-effects of chemotherapy drugs.Based on domestic and foreign studies，this paper reviews the progress of MNPs in the treatment of BCa，including gold，silver，copper and other MNPs，and prospects the trend of bladder perfusion combined with nanomedical drugs.

Objective To compare the long-term survival of elderly patients with esophageal squamous cell carcinoma (ESCC) treated with surgical versus non-surgical treatment. Methods A retrospective analysis was conducted on the clinical data of elderly patients aged ≥70 years with ESCC who underwent esophagectomy or radiotherapy/chemotherapy at Sichuan Cancer Hospital from January 2009 to September 2017. Patients were divided into a surgical group (S group) and a non-surgical group (NS group) according to the treatment method. The propensity score matching method was used to match the two groups of patients at a ratio of 1∶1, and the survival of the two groups before and after matching was analyzed. Results A total of 726 elderly patients with ESCC were included, including 552 males and 174 females, with 651 patients aged ≥70-80 years and 75 patients aged ≥80-90 years. There were 515 patients in the S group and 211 patients in the NS group. The median follow-up time was 60.8 months, and the median overall survival of the S group was 41.9 months [95%CI (35.2, 48.5)], while that of the NS group was only 24.0 months [95%CI (19.8, 28.3)]. The 1-, 3-, and 5-year overall survival rates of the S group were 84%, 54%, and 40%, respectively, while those of the NS group were 72%, 40%, and 30%, respectively [HR=0.689, 95%CI (0.559, 0.849), P<0.001]. After matching, 138 patients were included in each group, and there was no statistical difference in the overall survival between the two groups [HR=0.871, 95%CI (0.649, 1.167), P=0.352]. Conclusion Compared with conservative treatment, there is no significant difference in the long-term survival of elderly patients aged ≥70 years who undergo esophagectomy for ESCC. Neoadjuvant therapy combined with surgery is still an important choice to potentially improve the survival of elderly patients with ESCC.

Objective:To evaluate the value of spectral CT quantitative parameters in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:A total of 100 HCC patients who underwent surgical resection and were pathologically diagnosed in the Affiliated Cancer Hospital of Xiangya Medical College of Central South University from January 2020 to January 2023 were retrospectively enrolled. According to pathological grading, the patients were divided into the microvascular invasion group (invasion group, n=60) and the non-vascular invasion group (non-invasion group, n=40). Serological indicators and spectral CT quantitative parameters were compared between the two groups. Receiver operating characteristic (ROC) curve was used to analyze the value of spectral CT quantitative parameters in predicting MVI of HCC. Results:The serum alpha-fetoprotein (AFP) level in the invasion group was higher than that in the non-invasion group, with a statistically significant difference ( P<0.05). There were no statistically significant differences in serum carcinoembryonic antigen (CEA) and carbohydrate antigen 199 (CA-199) levels between the two groups (all P>0.05). In the invasion group, arterial phase iodine concentration, arterial phase normalized iodine concentration, venous phase iodine uptake reduction rate, arterial phase effective atomic number, and energy spectrum curve slope were all higher than those in the non-invasion group, with statistically significant differences (all P<0.05); there were no statistically significant differences in venous phase iodine concentration, venous phase normalized iodine concentration, and venous phase effective atomic number between the two groups (all P>0.05). The rates of peritumoral enhancement in the arterial phase and irregular tumor margin in the invasion group were higher than those in the non-invasion group, with statistically significant differences (all P<0.05); there was no statistically significant difference in tumor capsule between the two groups ( P>0.05). ROC curve analysis showed that the areas under the curve (AUC) of arterial phase iodine concentration, arterial phase normalized iodine concentration, venous phase iodine uptake reduction rate, arterial phase effective atomic number, and energy spectrum curve slope for predicting MVI in HCC were 0.812, 0.885, 0.726, 0.823, and 0.788, respectively. Conclusions:Spectral CT quantitative parameters are helpful to improve the preoperative diagnostic efficiency of MVI in HCC and can effectively predict MVI in HCC. Especially, arterial phase normalized iodine concentration has high application value in judging whether there is MVI in HCC.

Objective:To explore the equity and driving path of primary healthcare human resource allocation across 31 provinces in China,providing references for optimizing such allocations.Methods:Using data from the 2022 primary healthcare human resources in 31 provinces in China,the Health Resource Density Index(HRDI)was used to measure the equity of primary healthcare human resource allocation.The fuzzy-set Qualitative Comparative Analysis(fsQCA)was applied to construct configurational pathways influencing on allocation patterns.Results:In 2022,the HRDI for primary healthcare human resources in China was 2.349 0 in the East,1.198 6 in the Central region,and 0.775 2 in the West.Configurational analysis revealed three paths that promote high equity:the internal-external balance-driven model(H1),the government-led model(H2),and the economic-demand combined model(H3),with overall consistency and coverage of 0.955 and 0.794,respectively.Seven paths lead to low equity:internal-external constraint models(L1,L2),economic constraint models(L3),and demand constraint models(L4,L5,L6,L7),with overall consistency and coverage of 0.967 and 0.795,respectively.Conclusions:Significant regional disparities exist in the equity of primary healthcare human resource allocation in China.Population density is a critical factor influencing allocation equity.The collaboration of various factors contributes to enhancing the equity of primary healthcare human resources distribution.Therefore,future efforts to enhance equity should focus on promoting inter-regional mobility and resource sharing,while precisely addressing regional shortcomings to achieve high equity in primary healthcare human resource allocation.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To evaluate the utility of neoadjuvant vesical imaging-reporting and data system (NeoVI-RADS) in predicting tumor residuals and diagnosing muscle-invasive bladder cancer (MIBC) in patients undergoing neoadjuvant therapy, as well as its application in prognostic stratification.Methods:A retrospective case series analysis was conducted on the clinical data of 91 patients with bladder cancer who received neoadjuvant therapy at the Department of Urology, First Affiliated Hospital of Nanjing Medical University from July 2014 to June 2024. There were 84 male cases and 7 female cases, with an age of (66±9) years (range:45 to 85 years). The clinical staging of the patients was ≥T2 based on imaging. All of them underwent three or more cycles of neoadjuvant therapy, and had post-treatment multiparametric MRI (mp-MRI) evaluation. Based on the results of mp-MRI, the NeoVI-RADS was established and employed to assess tumor residuals and muscle invasion. The receiver operating characteristic curve was plotted, and the area under the curve (AUC) was calculated. Kaplan-Meier survival curves based on overall survival (OS) and cancer-specific survival (CSS) were plotted, and the Log-rank test was used for survival analysis comparison between groups.Results:In the neoadjuvant treatment cohort, the AUC for predicting tumor residuals post-neoadjuvant therapy using NeoVI-RADS was 0.900, with an accuracy of 93.4%, sensitivity of 95.8%, and a specificity of 85.0%. The NeoVI-RADS demonstrated strong diagnostic performance for MIBC, achieving an AUC of 0.900. At a NeoVI-RADS score cutoff of 4, the accuracy was 84.5%, with a sensitivity of 87.5% and a specificity of 72.9%. Additionally, compared to patients with NeoVI-RADS scores of 0 (5-year OS and CSS rates both 100%) or scores of 1 to 3 (5-year OS and CSS rates both 90.9%), patients with scores of 4 to 5 had significantly worse OS (5-year rate 63.0%) and CSS (5-year rate 66.3%) (all P<0.05). There was no statistically significant difference in OS or CSS between patients with NeoVI-RADS scores of 0 and those with scores of 1 to 3 (all P>0.05). Conclusion:NeoVI-RADS demonstrates significant diagnostic and prognostic value in the context of neoadjuvant treatment for bladder cancer, effectively assessing tumor residuals and muscle invasion, thereby enhancing patient management and facilitating personalized treatment approaches.