Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

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.

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.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

AIM To study the chemical constituents from the stems and leaves of Dendrobium formosum Roxb.ex Lindl.and their biological activities.METHODS The 95％ethanol extract from the stems and leaves of D.formosum was isolated and purified by silica gel,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their inhibitory activities onα-glucosidase were determined by PNPG method,and their in vitro anti-inflammatory activities were evaluated by RAW264.7 model.RESULTS Fifteen compounds were isolated and identified as coniferyl p-coumarate(1),(-)-pinoresinol(2),2,5,7-trihydroxy-4-methoxy-9,10-dihydrophenanthrene(3),naringenin(4),spiropreussomerin A(5),7-hydroxy-14-de-O-methyl-lasiodiplodin(6),(4S,5S,6Z,8E)-5-hydroxydeca-6,8-dien-4-olide(7),(6S,9R)-blumenol C(8),p-hydroxybenzoic acid(9),m-hydroxybenzoic acid(10),p-hydroxy benzenepropanoic acid(11),5,7-dihydroxy-isobenzofuran(12),2-(4-hydroxyphenyl)-ethanol(13),β-sitostenone(14),β-sitosterol(15).The IC50 values of compounds 1 and 4 on α-glucosidase inhibition were(65.60±3.31)and(98.95±2.53)μmol/L,respectively.Compound 3 presented inhibitory activity on NO production in RAW 264.7 cells,with IC50 value of(3.97±0.12)μmol/L.CONCLUSION Compounds 5-6,8 and 12 are isolated from Orchidacae family for the first time,and 2-15 are first isolated from this plant.Compounds 1 and 4 have α-glucosidase inhibitory activities,and 3 has anti-inflammatory activity.

AIM To study the chemical constituents from the stems and leaves of Dendrobium formosum Roxb.ex Lindl.and their biological activities.METHODS The 95％ethanol extract from the stems and leaves of D.formosum was isolated and purified by silica gel,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their inhibitory activities onα-glucosidase were determined by PNPG method,and their in vitro anti-inflammatory activities were evaluated by RAW264.7 model.RESULTS Fifteen compounds were isolated and identified as coniferyl p-coumarate(1),(-)-pinoresinol(2),2,5,7-trihydroxy-4-methoxy-9,10-dihydrophenanthrene(3),naringenin(4),spiropreussomerin A(5),7-hydroxy-14-de-O-methyl-lasiodiplodin(6),(4S,5S,6Z,8E)-5-hydroxydeca-6,8-dien-4-olide(7),(6S,9R)-blumenol C(8),p-hydroxybenzoic acid(9),m-hydroxybenzoic acid(10),p-hydroxy benzenepropanoic acid(11),5,7-dihydroxy-isobenzofuran(12),2-(4-hydroxyphenyl)-ethanol(13),β-sitostenone(14),β-sitosterol(15).The IC50 values of compounds 1 and 4 on α-glucosidase inhibition were(65.60±3.31)and(98.95±2.53)μmol/L,respectively.Compound 3 presented inhibitory activity on NO production in RAW 264.7 cells,with IC50 value of(3.97±0.12)μmol/L.CONCLUSION Compounds 5-6,8 and 12 are isolated from Orchidacae family for the first time,and 2-15 are first isolated from this plant.Compounds 1 and 4 have α-glucosidase inhibitory activities,and 3 has anti-inflammatory activity.

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."

Objective:To investigate the epidemiological dynamics and spatiotemporal diffusion trend of brucellosis in China from 2010 to 2024.Methods:Data on reported human brucellosis cases in mainland China from January 1, 2010, to December 31, 2024, were collected via the"China Information System for Disease Control and Prevention", including detailed information on the date of onset, gender, age, occupation, and residential address of the cases. The Joinpoint regression and spatial interpolation techniques were used to investigate the spatiotemporal dynamics and population distribution characteristics of human brucellosis in pastoral/semi-pastoral areas and other regions, as well as urban and rural areas, and explore the epidemic trends of the disease.Results:From 2010 to 2024, pastoral/semi-pastoral regions reported 252 094 brucellosis cases, with a mean annual incidence rate of 36.57±7.28 per 100 000. In contrast, other regions cumulatively recorded 519 748 cases during the same period, demonstrating a significantly lower mean annual incidence rate of 2.54±0.74 per 100 000. The incidence rate of human brucellosis in pastoral/semi-pastoral regions exhibited a declining-rebounding-declining trend. Specifically, the incidence rate decreased significantly from 2010 to 2017 (APC=-7.20; P<0.001) and increased notably from 2017 to 2021 (APC=18.00; P=0.015) with a decline again from 2021 to 2024 (APC=-7.53; P=0.027). In other regions, the incidence rate showed a fluctuating upward trend. Specifically, the incidence rate increased significantly from 2010 to 2015 (APC=20.37; P<0.001) and decreased notably from 2015 to 2018 (APC=-21.78; P<0.001), followed by an increase again from 2018 to 2024, a significant upward trend in incidence rate from 2018 to 2021 (APC=26.73; P<0.001) and a non-significant decline from 2021 to 2024 (APC=-0.99; P=0.735), resulting in the maintenance of a relatively high incidence level. Rural areas demonstrated significantly higher brucellosis incidence rates than urban settings (all P<0.001). Brucellosis exhibited a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions between 2010 and 2024. The age structure of patients in pastoral/semi-pastoral areas differed significantly from that in other regions. Specifically, in pastoral/semi-pastoral areas, the incidence rate was higher among the 35-49 age groups, while in other regions, the incidence rate was higher among those aged 55-64. Conclusion:There are notable disparities in the incidence of human brucellosis between pastoral/semi-pastoral areas and other regions in China. Human brucellosis exhibits a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions.

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.

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.