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.

Objective To modify YC-1,an inhibitor of hypoxia-inducible factor-1α(HIF-1α)into nanoparticles and explore its effect on reversing chemoresistance of colorectal cancer cells.Methods Nano-inhibitor mPEG350-YC-1(MYC-1)was prepared by the carboxyl condensation reaction of the active functional group hydroxyl(-OH)in the YC-1 molecule with methoxy polyethylene glycol carboxylic acid,and was verified by 1H nuclear magnetic resonance(1H-NMR).Its morphology was analyzed by transmission electron microscope(TEM),and its particle size distribution was analyzed by dynamic light scattering(DLS).By interacting FITC-labeled MYC-1(FYC-1)with HCT15 cells,the uptake ability of FYC-1 by the cells was observed using laser confocal microscopy.The cytotoxicity of MYC-1 was measured with CCK-8 assay.The sensitization effect of MYC-1 on the chemotherapy drug 5-Fu was detected through toxicity tests of resistant HCT15 cells(resistant HCT15,R-HCT15)and live/dead cell staining.The mechanism of MYC-1 reversing drug resistance was determined with immunofluorescence staining of HIF-1α and western blotting.Finally,a subcutaneous transplanted tumor model of R-HCT15 cells was constructed.The tumor bearing mice were randomly divided into PBS,5-Fu,MYC-1,and MYF groups,with 3 mice in each group.The tumor volume and weight were observed after treatment in each group to evaluate the ability of MYC-1 to reverse 5-Fu resistance in colorectal cancer.Results The nano-inhibitor MYC-1 was successfully prepared.TEM and DLS showed that MYC-1 could self-assemble into nanoparticles with a diameter of approximately 19.96±2.97 nm in aqueous solution.FYC-1 was also successfully prepared.When FYC-1 was interacted with HCT15 cells,FYC-1 could be better taken up by the cells,indicating that the amphiphilic MYC-1 could be better endocytosed into the cells to exert its function.When MYC-1 and 5-Fu acted in combination in colon cancer R-HCT15 cells,the resistance index(RI)was decreased from 7.99 to 1.55,and the relative reversal rate(RRR)of RI was 80.6%.Live(green)/dead(red)cell staining revealed that MYC-1 increased the toxicity of 5-Fu to R-HCT15 cells.Immunofluorescence staining(P<0.01)and Western blotting indicated that MYC-1 effectively inhibited the intracellular expression of HIF-1α.The combined action of MYC-1 and 5-Fu on mice with R-HCT15 subcutaneous transplanted tumors had the best therapeutic effect when compared with PBS(P<0.001),5-Fu(P<0.01),and MYC-1(P<0.01).Immunofluorescence staining of HIF-1α in tumor tissues displayed that the expression of HIF-1α was decreased in the MYC-1 and MYF groups.HE staining showed that there was no obvious damage to the important organs in each treatment group.Conclusion Nano-inhibitor MYC-1 can reverse the drug resistance of colorectal cancer to 5-Fu chemotherapy by reducing the expression of HIF-1α protein in colorectal cancer cells,thereby effectively improving the therapeutic effect of 5-Fu.

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.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Acute urinary retention(AUR)occurs frequently among astronauts on orbit.The current treatment is complex and easy to damage the urethra,which seriously affects the life and work of astronauts.In contrast,acupuncture,a traditional Chinese remedy,has shown promising results in managing urinary retention.However,the specific acupoints that could potentially prevent AUR remain uncertain due to the unique physiological conditions experienced by individuals in space compared to those on Earth.To address this gap,our research delved into the mechanisms of AUR and acupuncture within both traditional Chinese medicine and modern medical practices.We conducted a thorough literature review from Pubmed,Web of Science,CNKI,Wanfang database,VIP database and Chinese Medical Code database.A hierarchical evidence-scoring approach was utilized to analyze the included literatures,thus devised acupuncture protocols for the treatment of AUR.The outcomes of our study aim to establish a foundation for the application of acupuncture in managing AUR.

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.

The 2024 World Conference on Lung Cancer (WCLC) and the European Society for Medical Oncology (ESMO) Annual Meeting, two of the most prestigious events in oncology, have concluded sequentially. As the most authoritative annual gatherings in lung cancer and the entire oncology field, the WCLC and ESMO conferences brought together top oncology experts and scientists from around the world to share, discuss, and publish the latest cutting-edge advancements in oncology. In both conferences, lung cancer immunotherapy remained a hot topic of considerable interest. This article aims to summarize and discuss the important research progress on perioperative immunotherapy for non-small cell lung cancer reported at the two conferences.