The 2025 American Society of Clinical Oncology (ASCO) Annual Meeting was held in Chicago. At the meeting, researches on the treatment of epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) once again took the spotlight. Combination therapy strategies have demonstrated the potential to overcome resistance to EGFR tyrosine kinase inhibitor (EGFR-TKI) and prolong survival. Meanwhile, progress has also been made in individualized treatment strategies for young patients and those with fibrotic interstitial lung disease. However, the complexity of resistance mechanisms, special treatment considerations for different populations, and the impact of socioeconomic factors on treatment accessibility remain challenges in the field of EGFR-mutant NSCLC treatment. In the future, it is necessary to further explore more effective treatment regimens and expand the accessibility of precision medicine to maximize patient benefits.

The 26th World Conference on Lung Cancer (WCLC) was held in Barcelona during September 6-9, 2025. As the world's largest and most influential academic meeting in the field of lung cancer, this year's congress unveiled long-term follow-up data from several pivotal studies and significant advances in novel therapeutic strategies. In the realm of targeted therapy, a next-generation combination strategy has been established as the new standard of care for the first-line treatment of patients with advanced epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC), demonstrating a significant improvement in overall survival. In immunotherapy, novel combination regimens have not only addressed the therapeutic challenge of acquired resistance to EGFR targeted therapies, but also shown clear long-term survival benefits in both the perioperative and locally advanced settings. These findings pave the way for shifting the treatment paradigm to earlier stages for patients with NSCLC. Antibody-drug conjugates have made remarkable strides in this field. They have shown outstanding efficacy in patients with specific resistance mutations and those with brain metastases, and have also demonstrated immense potential in treating patients with HER2-aberrant lung cancer and broader NSCLC populations. This offers new therapeutic options for patients with refractory lung cancer.However, significant challenges remain, including the heterogeneity of resistance mechanisms, the selection of optimal treatment regimens, and management strategies for special populations. Future research should focus on identifying novel precision biomarkers and optimizing therapeutic strategies to ultimately improve clinical outcomes for all patients with lung cancer.

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.

The 2025 American Society of Clinical Oncology Gastrointestinal Cancers Symposium (ASCO-GI) was held from January 23 to 25, 2025. Several significant studies on the treatment of esophageal and gastroesophageal junction (GEJ) cancer were presented at the symposium, highlighting notable advances, particularly in the perioperative and advanced settings. Immunotherapy has demonstrated significant promise in the neoadjuvant treatment of esophageal cancer, showing potential to become a standard treatment. Furthermore, the long-term survival benefits of combining immunotherapy with chemotherapy for advanced GEJ cancer were further validated. This article summarizes and interprets the researches presented at the symposium concerning perioperative and advanced treatments for esophageal and GEJ cancers.

The 2025 European Lung Cancer Congress (ELCC) convened in Paris, France, centering on the optimization and innovation of immunotherapy for non-small cell lung cancer (NSCLC). Key topics at the congress included the application strategies for perioperative immunotherapy, breakthroughs in combination therapy models for advanced NSCLC, and the emerging roles of biomarkers in predicting diverse treatment outcomes. This paper integrates data from several key pivotal studies to systematically analyze the clinical value of neoadjuvant therapy within the perioperative setting, the potential of targeted combination regimens, and the challenges of managing drug resistance, thus offering new directions for clinical practice.

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.

Objective The aim of this study was to characterize the basic molecular and biochemical parameters for a cyclophilin protein in Cryptosporidium parvum called CpCyP1.Methods CpCyP1 expression patterns during the parasite life cycle were evaluated using qRT-PCR with total RNA isolated from different developmental stages of C.parvum.Native CpCyP1 protein in sporozoites was detected using western blot.The localization of CpCyP1 was performed using the immunofluorescence assay,with an affinity-purified rabbit polyclonal antibody against a synthetic peptide.The peptidyl-prolyl cis-trans isomerase(PPIase)activity of His-tagged recombinant CpCyP1 was evaluated using absorbance colorimetry,and the effect of cyclosporin A(CsA)on the activity of CpCyP1 was determined.Results CpCyP1 was expressed in all parasite developmental stages,whereas CpCyP1 was present mainly in the cytosol of sporozoites,meronts,and gamonts.CpCyP1 displayed Michaelis-Menten kinetics towards N-succinyl-Ala-Ala-Pro-Phe-p-nitroanilide for its PPIase activity(Km=456.4 μmol/L;Vmax=1.981 U).CsA inhibited PPIase activity,showing lower micromolar inhibitory activity and binding affinity(Kd=5.122 μmol/L;IC50=1.004 μmol/L).Conclusions These results imply that CpCyP1 in the parasite may be the target for the previously reported anti-cryptosporidial efficacy of CsA and suggest that C.parvum cyclophilins could be evaluated as candidate drug targets.