With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

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.

[摘 要] 目的：探讨环指蛋白8（RNF8）对子宫内膜癌（EC）细胞顺铂（CDDP）敏感性的影响及可能机制。方法：基于在线数据库分析RNF8在EC组织中的表达及拷贝数变异；采用慢病毒介导shRNA稳定敲低Ishikawa细胞中RNF8表达并进行CDDP处理，按sh-NC+溶剂对照、sh-RNF8+溶剂对照、sh-NC + CDDP、sh-RNF8 + CDDP分组处理，并设置sh-RNF8 + CDDP + KU60019抑制实验。采用CCK-8、克隆形成、Annexin V-FITC/PI流式细胞术、Hoechst 33258/PI染色、WB及γ-H2AX免疫荧光检测细胞活力、克隆形成、凋亡、DNA损伤累积及ATM/CHK2通路相关蛋白表达。结果：RNF8在EC组织和细胞中表达升高。RNF8敲低降低细胞存活率和IC50，抑制克隆形成，并进一步增强CDDP诱导的凋亡；同时BAX、Cleaved-Caspase-3和γ-H2AX表达升高，BCL-2、p-ATM、p-CHK2和RAD21表达降低。KU60019处理进一步促进DNA损伤累积和细胞凋亡，并降低细胞活力。结论：RNF8可能通过ATM/CHK2轴促进DNA损伤应答并降低EC细胞对CDDP的敏感性；敲低RNF8可增加DNA损伤累积并增强CDDP敏感性。

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Aim To investigate the effect of hypobaric hypoxia on macrophage necroptosis and atherosclerotic plaque instability and explore the underlying mechanisms.Methods Mouse bone marrow-derived macrophages were i-solated and cultured,and divided into control group(21％oxygen concentration)and hypoxia group(3％oxygen concen-tration).After 48 hours,cell necroptosis was detected,and the expression of cell necroptosis related proteins was deter-mined by Western blot.Healthy male ApoE-/-mice were randomly divided into control group and hypobaric hypoxia group.After the intervention for 16 weeks,the plasma lipids and inflammatory cytokines were measured,the areas of ath-erosclerotic plaque and necrotic core were evaluated by HE staining.The content of plaque collagen was detected by Mas-son staining.The number of macrophages in the plaque and the expression of necrotic apoptosis related proteins were de-tected by immunohistochemical staining and Western blot.Results Hypoxia induced increased necrotic apoptosis of macrophages(P＜0.01),while necroptotic inhibitor necrostatin-1(Nec-1)reduced hypoxia induced cell death(P＜0.05);hypoxia leads to a decrease in the expression of adenosine deaminase acting on RNA 1(ADAR1)in macrophages(P＜0.01),and an increase in the expression of Z-DNA binding protein 1(ZBP1),phosphorylated receptor-interacting serine/threonine-protein kinase(p-RIPK3),and phosphorylated mixed lineage kinase domain-like protein(p-MLKL)(all P＜0.01).Compared with the control group,the plasma lipid levels of ApoE-/-mice in the hypobaric hypoxia group did not change significantly(P＞0.05),the plasma inflammatory cytokines(TNF-α,IL-1β,IL-6 and MCP-1)increased(all P＜0.05),the area of atherosclerotic plaque increased(P＜0.05),the area of plaque necrotic core increased,the content of plaque collagen decreased,the number of macrophages increased,the expression of ADAR1 decreased,and the expres-sion of ZBP1 and p-MLKL increased(all P＜0.01).Conclusion Hypobaric hypoxia causes the imbalance of A-DAR1/ZBP1 expression in macrophages,activates RIPK3/MLKL signaling pathway,promotes macrophage necroptosis,in-creases the area of plaque necrosis core,and leads to increase instability of atherosclerotic plaque.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Compared to traditional machine learning algorithms,which often suffer from low feature extraction efficiency,insufficient nonlinear pattern recognition capabilities and slow training speeds,the broad learning system(BLS)enhances the learning ability and efficiency by horizontally expanding the network structure.BLS offers advantages such as a simple structure,fast training speed,and strong generalization capabilities.While BLS has demonstrated potential in various fields,but its application in identification of complex samples has not been fully explored.This research investigated the feasibility of using BLS algorithm for identification of complex samples based on physicochemical indicators.Using the iris,wine,and breast cancer datasets,the length and width of petals and sepals of iris flowers,the physicochemical properties of wine,and the nuclear characteristics of breast cancer cells were used as input variables to establish BLS models for identifying iris species,wine varieties,and benign versus malignant nucleus.The model performance was evaluated by confusion matrices,accuracy,and runtime.Compared with partial least squares-discriminant analysis(PLS-DA),soft independent modeling of class analogies(SIMCA),and artificial neural networks(ANN),the results indicated that BLS demonstrated significant advantages in computational efficiency and recognition accuracy.Thus,BLS was an efficient and reliable method for identification of complex samples.

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.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Objective To investigate the effects of laparoscopic sleeve gastrectomy(LSG)on the cardiac structure and function in obese patients with heart failure(HF)and compare the efficacy of LSG across obese patients with different HF types.Methods This study included 33 obese patients with HF who underwent LSG.The clinical indicators were compared between before operation and 12 months after operation.Repeated measures analysis of variance was employed to evaluate the changes in echocardiographic parameters before operation and 3,6,and 12 months after operation.Patients were allocated into a HF with preserved ejection fraction group(n=17),a HF with mildly reduced ejection fraction group(n=5)and a HF with reduced ejection fraction(HFrEF)group(n=11)based on left ventricular ejection fraction(LVEF)before operation for subgroup analyses of the effects of LSG on the cardiac structure and function of obese patients with HF.The paired samples t-test was conducted to assess the degree of cardiac structural and functional alterations after LSG.Results The 33 patients included 69.7% males,with an average age of(35.3±9.9)years,and a body mass index(BMI)of(51.2±9.8)kg/m².The median follow-up was 9.0(5.0,13.3)months.Compared with the preoperative values,the postoperative BMI(P=0.002),body surface area(BSA)(P=0.009),waist circumference(P=0.010),hip circumference(P=0.031),body fat content(P=0.007),and percentage of patients with cardiac function grades Ⅲ-IV(P<0.001)decreased.At the 12-month follow-up left atrial diameter(P=0.006),right atrial long-axis inner diameter(RAD1)(P<0.001),right atrial short-axis inner diameter(RAD2)(P<0.001),right ventricular inner diameter(P=0.002),interventricular septal thickness at end-diastolic(P=0.002),and left ventricular end-diastolic volumes(P=0.004)and left ventricular end-systolic volumes(P=0.003) all significantly reduced compared with preoperative values.Additionally,left ventricular fractional shortening and LVEF improved(both P<0.001).Subgroup analyses revealed that cardiac structural parameters significantly decreased in the HF with preserved ejection fraction,HF with mildly reduced ejection fraction,and HFrEF subgroups compared with preoperative values.Notably,the HFrEF group demonstrated the best performance in terms of left atrial diameter(P=0.003),left ventricular inner diameter at end-diastole(P=0.008),RAD1(P<0.001),RAD2(P=0.004),right ventricular inner diameter(P=0.019),left ventricular end-diastolic volume(P=0.004)and left ventricular end-systolic volume(P=0.001),cardiac output(P=0.006),tricuspid regurgitation velocity(P=0.002),and pulmonary artery systolic pressure(P=0.001) compared to preoperatively.Postoperative left ventricular fractional shortening(P<0.001,P=0.003,P<0.001)and LVEF(P<0.001,P=0.011,P=0.001)became higher in all the three subgroups than the preoperative values.Conclusions LSG decreased the body weight,BMI,and BSA,improved the cardiac function grade,reversed the enlargement of the left atrium and left ventricle,reduced the right atrium and right ventricle,and enhanced the left ventricular systolic function.It was effective across obese patients with different HF types.Particularly,LSG demonstrates the best performance in improving the structures of both atria and ventricles in obese patients with HFrEF.
Humans ; Male ; Female ; Gastrectomy/methods* ; Heart Failure/complications* ; Adult ; Obesity/physiopathology* ; Laparoscopy ; Middle Aged ; Heart/physiopathology* ; Stroke Volume