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.

Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Objective To compare the performance of 7 Chinese-developed GenAI models (DeepSeek, Wenxin Yiyan, Tongyi, Doubao, Kimi, Hunyuan, and iFLYTEK Spark) in responding to common diseases of the pediatric genitourinary system and to explore their potential applications.Methods A total of 80 questions covering basic disease information, diagnosis, treatment, and prognosis related to “hypospadias, hydronephrosis, and cryptorchidism” were developed as prompts.The models were tested under 3 modes:“quick thinking, ”“deep thinking, ” and “deep thinking + internet access.” The results were evaluated in a blinded review by two pediatric urologists.The responses were graded into 4 levels—A, B, C, and D—based on accuracy, completeness, and relevance.The proportions of responses rated as A and A+B were statistically analyzed and compared across the different models/modes.Results The “deep thinking” mode of DeepSeek demonstrated the best performance, with 88% of responses rated as A and 96% rated as A+B.DeepSeek (deep thinking + internet access), Doubao (quick thinking), Hunyuan (deep thinking), and Doubao (deep thinking + internet access) followed closely, also showing excellent response capabilities.Among the top 5 models/modes, significant differences were observed in the proportion of Alevel responses (P＜0.05), while differences in the proportion of A+B-level responses were not statistically significant (P＞0.05).Conclusion Certain Chinese-developed GenAI models, represented by DeepSeek (deep thinking), demonstrate significant application potential.However, they still cannot consistently provide recommendations rated at the A+B level (recommended grade or higher) with 100% accuracy.In clinical practice, both doctors and patients should view GenAI as an auxiliary tool for education and information acquisition rather than as a final decision-making authority.All information must be reviewed and confirmed by professional physicians.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Idiopathic pulmonary fibrosis (IPF) is a progressive disease lacking effective therapy. Metformin, an antidiabetic medication, has shown promising therapeutic properties in preclinical fibrosis models; however, its precise cellular targets and associated mechanisms in fibrosis resolution remain incompletely defined. Most research on metformin's effects has focused on mesenchymal and inflammatory responses with limited attention to epithelial cells. In this study, we utilized Sftpc lineage-traced and Fgfr2b conditional knockout mice, along with BMP2/PPARγ and AMPK inhibitors, to explore metformin's impact on alveolar epithelial cells in a bleomycin-induced pulmonary fibrosis model and cell culture. We found that metformin increased the proliferation and differentiation of alveolar type 2 (AT2) cells, particularly the recently identified injury-activated alveolar progenitors (IAAPs)-a subpopulation characterized by low SFTPC expression but enriched for PD-L1. Single-cell RNA sequencing revealed a reduction in apoptosis among mature AT2 cells. Interestingly, metformin's therapeutic effects were not significantly affected by BMP2 or PPARγ inhibition, which blocked the lipogenic differentiation of myofibroblasts. However, Fgfr2b deletion in Sftpc lineage cells significantly impaired metformin's ability to promote fibrosis resolution, a process linked to AMPK signaling. In conclusion, metformin alleviates fibrosis by directly activating AT2 cells, especially the IAAPs, through a mechanism that involves AMPK and FGFR2b signaling, but is largely independent of BMP2/PPARγ pathways.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Computational approaches for predicting drug-target interactions (DTIs) are pivotal in advancing drug discovery. Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information. To comprehensively consider network information, we propose DHGT-DTI, a novel deep learning-based approach for DTI prediction. Specifically, we capture the local and global structural information of the network from both neighborhood and meta-path perspectives. In the neighborhood perspective, we employ a heterogeneous graph neural network (HGNN), which extends Graph Sample and Aggregate (GraphSAGE) to handle diverse node and edge types, effectively learning local network structures. In the meta-path perspective, we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths, such as "drug-disease-drug", and use an attention mechanism to fuse information across multiple meta-paths. The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method. Furthermore, DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy. Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods. Additionally, case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.

OBJECTIVE: To observe the clinical efficacy of 3D printing spinal external fixator combined with McKenzie therapy for patients with lumbar dics herniation (LDH). METHODS: Sixty patients with LDH between January 2022 and January 2023 were enrolled. Among them, 30 patients were given McKinsey training. According to different treatment methods, all patients were divided into McKenzie group and McKenzie + 3D printing group, 30 patients in each group. The McKenzie group provided McKenzie therapy. The McKenzie + 3D printing group were treated with 3D printing spinal external fixation brace on the basis of McKenzie therapy. Patients in both groups were between 25 and 60 years of age and had their first illness. In the McKenzie group, there were 19 males and 11 females, with an average age of (48.57±5.86) years old, and the disease duration was (7.03 ±2.39) months. The McKenzie + 3D printing group, there were 21 males and 9 females, with an average age of (48.80±5.92) years old, and the disease duration was(7.30±2.56) months. Pain was evaluated using the visual analogue scale (VAS), and lumbar spine function was assessed using the Oswestry disability index (ODI) and the Japanese Orthopaedic Association (JOA) score. VAS, ODI and JOA scores were compared between two groups before treatment and at 1, 3, 6, 9 and 12 months after treatment. RESULTS: All patients were followed up for 12 months. The VAS for the McKenzie combined with 3D printing group before treatment and at 1, 3, 6, 9, and 12 months post-treatment were(6.533±0.860), (5.133±1.008), (3.933±0.868), (2.900±0.759), (2.067±0.640), (1.433±0.504), respectively. In the McKenzie group, the corresponding scores were (6.467±0.860), (5.067±1.048), (4.600±0.968), (3.533±1.008), (2.567±0.728), (1.967±0.809), respectively. The ODI of the McKenzie group before treatment and at 1, 3, 6, 9, and 12 months post-treatment were (41.033±6.810)%, (37.933±6.209)%, (35.467±6.962)%, (27.567±10.081)%, (20.800±7.531)%, (13.533±5.158)%, respectively. For the McKenzie combined with 3D printing group, the corresponding ODI were(38.033±5.605)%, (33.000±6.192)%, (28.767±7.045)%, (22.200±5.517)%, (17.700±4.836)%, (11.900±2.771)%, respectively. The JOA scores of the McKenzie combined with 3D printing group before treatment and at 1, 3, 6, 9, and 12 months post-treatment were(8.900±2.074), (13.133±2.330), (15.700±3.583), (20.400±3.480), (22.267±3.084), (24.833±2.640), respectively. In the McKenzie group, the corresponding scores were(9.200±2.091), (12.267±2.406), (15.333±3.198), (18.467±2.240), (20.133±2.751), (22.467±2.849), respectively. Before the initiation of treatment, no statistically significant differences were observed in the VAS, ODI, and JOA scores between two groups (P>0.05). At 3, 6, 9, and 12 months post-treatment, the VAS in the McKenzie combined with 3D printing group was significantly lower than that in the McKenzie group, and the difference was statistically significant (P<0.05). The comparison of ODI between two groups at 1, 3, 6, 9, and 12 months post-treatment revealed statistically significant differences (P<0.05). At 6, 9, and 12 months post-treatment, the JOA score in the McKenzie combined with 3D printing group was significantly higher than that in the McKenzie-only group, and the difference was statistically significant (P<0.05). CONCLUSION The combination of 3D printed functional spinal external fixation brace with McKenzie therapy can significantly improve and maintain lumbar function in patients with LDH.
Humans ; Male ; Female ; Middle Aged ; Printing, Three-Dimensional ; Intervertebral Disc Displacement/surgery* ; External Fixators ; Lumbar Vertebrae/surgery* ; Adult ; Braces ; Treatment Outcome