Background/Aims: Lenvatinib resistance remains a critical barrier in advanced hepatocellular carcinoma (HCC) therapy. However, the underlying mechanisms and strategies for reversing resistance remain incompletely understood. Methods: Integrated transcriptomics of lenvatinib-resistant patient tumors and an acquired-resistance murine model identified a novel macrophage subpopulation. Functional validation employed CRISPR-SAM screening, conditioned medium (CM) assays, subcutaneous/orthotopic xenografts, patient-derived organoids (PDOs), and patient-derived xenografts (PDXs). Mechanistic studies included ChIP-qPCR, co-immunoprecipitation, and pharmacologic targeting. Clinical relevance was assessed in a retrospective cohort. Results: Resistant HCC exhibited significant enrichment of a COLEC12high TAM subset , which correlated with poor survival and treatment response. These TAMs secreted neuregulin-1 (NRG1) , activating HER2/HER3-AKT signaling in tumor cells to drive cancer stemness and lenvatinib resistance. Mechanistically, in TAMs COLEC12 sequestered STAT1 in the cytoplasm, preventing its phosphorylation, and thereby derepressing STAT3-mediated NRG1 transcription. Depletion of NRG1 reversed the stemness phenotypes and resensitized tumors to lenvatinib both in vitro and in vivo. Clinically, high NRG1 expression predicted an inferior lenvatinib response and shorter survival. Crucially, the bispecific anti-HER2/HER3 antibody zenocutuzumab restored lenvatinib efficacy in PDOs, PDXs, and murine models. Conclusions Our work establishes the COLEC12high TAM/NRG1 axis as a master regulator of therapeutic resistance and identifies NRG1 as a predictive biomarker, providing a clinically actionable strategy to overcome lenvatinib resistance in HCC.

Driven by the widespread adoption of minimally invasive thoracic surgical techniques and the deepening integration of enhanced recovery after surgery (ERAS) protocols, evidence-based perioperative airway management has emerged as a pivotal determinant of surgical safety, pulmonary functional recovery, and long-term prognosis. Currently, substantial regional disparities in thoracic surgical expertise persist across China, manifesting as heterogeneity in preoperative assessment standardization, inconsistent airway preparation strategies, divergent approaches to complication prevention, and limited penetration of advanced anesthetic technologies. To promote the standardization of clinical practices in this domain among hospitals at all levels, a multidisciplinary expert consortium has initiated the development of the Chinese Guidelines for Perioperative Airway Management in Thoracic Surgery (2026 edition). This protocol systematically delineates the methodological framework and structural blueprint underpinning the guideline’s formulation. Compared to the 2020 edition, the updated guideline places specific emphasis on refining stratified criteria for preoperative cardiopulmonary reserve assessment, establishing standardized management pathways for non-intubated spontaneous-ventilation techniques, formulating individualized multimodal perioperative pulmonary rehabilitation regimens, and defining precise intervention workflows for persistent postoperative cough. Collectively, these measures aim to further reduce the baseline incidence of perioperative pulmonary complications and to provide robust support for the clinical translation of enhanced recovery principles within the field of thoracic surgery.

Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

Viral infection has always been a significant challenge to human health. Transplant recipients, including those who have undergone solid organ transplantation and allogeneic hematopoietic stem cell transplantation, are at high risk of viral infection due to their weak immune function under immunosuppressive therapy. Unlike the general population, transplant recipients are prone to pneumonia and even severe pneumonia after respiratory viral infection, which requires close attention from clinicians. Therefore, this article reviews the clinical characteristics and special management of viral infection in this population, focusing on the epidemiological features of common respiratory viral infection in transplant recipients, early diagnosis and intervention after infection, severe warning signs and drug treatment strategies, for the reference of clinical colleagues.

Lung transplantation is a key therapeutic approach for patients with end-stage lung diseases. Although its clinical outcomes have significantly improved, multidimensional injuries sustained by donor lungs during procurement, preservation, and transplantation remain major challenges affecting graft survival and long-term prognosis. This article proposes the "Guangzhou Classification" for full-course management of donor lung injury, characterized by spatiotemporal dynamics. Based on the progression of disease stages, donor lung injuries are systematically divided into three types: primary injuries (including donor ICU-related lung injury, pathogen colonization, and cold ischemia injury), secondary injuries (such as ventilator-induced lung injury after transplantation, ischemia-reperfusion inflammatory storm, and early rejection), and accompanying injuries (organ toxicity caused by accumulation of postoperative sedatives, analgesics, and vasoactive drugs). Drawing on previous studies and the clinical experience of our center, this paper elaborates the temporal evolution, key risk factors, and prevention and treatment strategies of each injury category, and discusses future research directions. By targeting critical injury factors at each stage, this classification aims to optimize both short-term and long-term outcomes of lung transplantation.

In recent years, lung function assessment has attracted increasing attention in the perioperative management of thoracic surgery. However, traditional pulmonary function testing methods remain limited in clinical practice due to high equipment requirements and complex procedures. With the rapid development of artificial intelligence (AI) technology, lung function assessment based on multimodal chest imaging (such as X-rays, CT, and MRI) has become a new research focus. Through deep learning algorithms, AI models can accurately extract imaging features of patients and have made significant progress in quantitative analysis of pulmonary ventilation, evaluation of diffusion capacity, measurement of lung volumes, and prediction of lung function decline. Previous studies have demonstrated that AI models perform well in predicting key indicators such as forced expiratory volume in one second, diffusing capacity for carbon monoxide, and total lung capacity. Despite these promising prospects, challenges remain in clinical translation, including insufficient data standardization, limited model interpretability, and the lack of prediction models for postoperative complications. In the future, greater emphasis should be placed on multicenter collaboration, the construction of high-quality databases, the promotion of multimodal data integration, and clinical validation to further enhance the application value of AI technology in precision decision-making for thoracic surgery.

Objective To construct a combined model integrating ultrasonic features and radiomics derived from ultrasound images,and to evaluate its diagnostic performance in re-assessing the benign or malignant nature of Bethesda Ⅲ nodules.Methods A retrospective study was carried out on 442 patients with thyroid nodules classified as Bethesda Ⅲ after fine-needle aspiration biopsy(FNAB)between January 2019 and September 2024.All patients had undergone surgical pathology.The patients were randomly allocated into a training set and a testing set at a ratio of 7∶3.Relevant clinical characteristics were gathered,and regions of interest(ROI)were outlined on the most suspicious slice of the lesion prior to biopsy.Ultrasound radiomics features were extracted,key radiomics features were selected,and radiomics scores(Rad-score)were computed.The ultrasound model,radiomics model,and combined model were constructed.Subsequently,the diagnostic efficacy and clinical application value of each model were evaluated using the area under the receiver operating characteristic curve(AUC)and decision curve analysis(DCA).Results Univariate analysis and multivariate logistic regression analysis findings indicated that microcalci-fication,irregular margin,and Rad-score were independent risk factors for the malignant transformation of BethesdaⅢ nodules.In the testing set,the AUC values of the ultrasound model,radiomics model,and combined model were 0.76,0.71,and 0.81,respectively.The calibration curve of the combined model revealed a good consistency between the predicted values and the actual outcomes.The DCA of the testing set demonstrated that the combined model exhibited high clinical utility.Conclusion The combined model,established based on ultrasonic features and ultrasound radiomics,provides a higher predictive value for evaluating the malignancy risk of Bethesda Ⅲ nodules.

Lung transplantation is a key therapeutic approach for patients with end-stage lung diseases. Although its clinical outcomes have significantly improved, multidimensional injuries sustained by donor lungs during procurement, preservation, and transplantation remain major challenges affecting graft survival and long-term prognosis. This article proposes the "Guangzhou Classification" for full-course management of donor lung injury, characterized by spatiotemporal dynamics. Based on the progression of disease stages, donor lung injuries are systematically divided into three types: primary injuries (including donor ICU-related lung injury, pathogen colonization, and cold ischemia injury), secondary injuries (such as ventilator-induced lung injury after transplantation, ischemia-reperfusion inflammatory storm, and early rejection), and accompanying injuries (organ toxicity caused by accumulation of postoperative sedatives, analgesics, and vasoactive drugs). Drawing on previous studies and the clinical experience of our center, this paper elaborates the temporal evolution, key risk factors, and prevention and treatment strategies of each injury category, and discusses future research directions. By targeting critical injury factors at each stage, this classification aims to optimize both short-term and long-term outcomes of lung transplantation.

Objective To construct a combined model integrating ultrasonic features and radiomics derived from ultrasound images,and to evaluate its diagnostic performance in re-assessing the benign or malignant nature of Bethesda Ⅲ nodules.Methods A retrospective study was carried out on 442 patients with thyroid nodules classified as Bethesda Ⅲ after fine-needle aspiration biopsy(FNAB)between January 2019 and September 2024.All patients had undergone surgical pathology.The patients were randomly allocated into a training set and a testing set at a ratio of 7∶3.Relevant clinical characteristics were gathered,and regions of interest(ROI)were outlined on the most suspicious slice of the lesion prior to biopsy.Ultrasound radiomics features were extracted,key radiomics features were selected,and radiomics scores(Rad-score)were computed.The ultrasound model,radiomics model,and combined model were constructed.Subsequently,the diagnostic efficacy and clinical application value of each model were evaluated using the area under the receiver operating characteristic curve(AUC)and decision curve analysis(DCA).Results Univariate analysis and multivariate logistic regression analysis findings indicated that microcalci-fication,irregular margin,and Rad-score were independent risk factors for the malignant transformation of BethesdaⅢ nodules.In the testing set,the AUC values of the ultrasound model,radiomics model,and combined model were 0.76,0.71,and 0.81,respectively.The calibration curve of the combined model revealed a good consistency between the predicted values and the actual outcomes.The DCA of the testing set demonstrated that the combined model exhibited high clinical utility.Conclusion The combined model,established based on ultrasonic features and ultrasound radiomics,provides a higher predictive value for evaluating the malignancy risk of Bethesda Ⅲ nodules.