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 challenges in blood cell recognition caused by diverse morphology, dense distribution, and the abundance of small target information, this paper proposes a blood cell detection algorithm - the "You Only Look Once" model based on hybrid mixing attention and deep over-parameters (HADO-YOLO). First, a hybrid attention mechanism is introduced into the backbone network to enhance the model's sensitivity to detailed features. Second, the standard convolution layers with downsampling in the neck network are replaced with deep over-parameterized convolutions to expand the receptive field and improve feature representation. Finally, the detection head is decoupled to enhance the model's robustness for detecting abnormal cells. Experimental results on the Blood Cell Counting Dataset (BCCD) demonstrate that the HADO-YOLO algorithm achieves a mean average precision of 90.2% and a precision of 93.8%, outperforming the baseline YOLO model. Compared with existing blood cell detection methods, the proposed algorithm achieves state-of-the-art detection performance. In conclusion, HADO-YOLO offers a more efficient and accurate solution for identifying various types of blood cells, providing valuable technical support for future clinical diagnostic applications.
Algorithms ; Humans ; Blood Cells/cytology* ; Blood Cell Count/methods* ; Neural Networks, Computer ; Deep Learning ; Detection Algorithms

In recent years, rituximab has been gradually used in the treatment of idiopathic membranous nephropathy (IMN). Compared with traditional treatments, the safety and effectiveness of rituximab in the treatment of IMN have been confirmed, which induces remission in 60%-80% of patients. For the remaining 20%-40% patients, several mechanisms can explain rituximab resistance: decreased rituximab bioavailability; internalized by targeted B cells; the generation of anti-rituximab antibody; chronic and irreversible damage to the glomerular filtration barrier; autoreactive B-cell clones in secondary lymphoid organs that cannot be effectively eliminated. The treatment of patients with rituximab-refractory IMN remains controversial and challenging. The recognition of IMN as an antibody-mediated autoimmune disease has rationalized the use of immunosuppressive drugs such as B cell-targeted therapies, plasma cell-targeted therapies, and complement inhibitors. This review mainly summarizes recent advances in the understanding of the physiological mechanisms of rituximab resistance, and in the management of rituximab-refractory IMN, aiming to aid in the clinical management of IMN.

Objective:To explore effects of different lower limb placement angles on venous drainage among patients undergoing thoracolumbar surgery in prone position.Methods:From January 2017 to January 2018, we selected patients underwent elective thoracolumbar surgery in prone position of Operating Room in Beijing Hospital as research objects. According to the need of surgery, it was divided into 51 cases in low-angle group and 54 cases in traditional-angle group. We compared effects of different lower limb placement angles on patients by taking the changes of lower limb venous blood flow under ultrasound before and after surgery as main observation indicators and taking changes of lower limb skin temperature and leg circumference as the secondary observation indicator.Results:There were statistical differences in the changes of blood flow on the left and right popliteal veins, the left and right posterior tibial veins of patients under ultrasound between two groups before and after surgery ( P<0.05) . There were no statistical differences in the changes of the leg circumference of 10 cm above the left and right ankles and the changes of the leg circumference of 15 cm above the left and right patellae of patients between two groups ( P>0.05) ; there was also no statistical difference in the change of bilateral skin temperature ( P>0.05) . Conclusions:The lower limb placement angles among patients undergoing thoracolumbar surgery in prone position may affect the venous drainage which nurses of Operating Room should pay attention to when placing positions.

Chemotherapy completion rate can reflect the tolerance and compliance of patients to chemotherapy. Poor tolerance may result in delay or suspension of the comprehensive treatment plan, thus affect the efficacy of cancer treatment. Evaluating methods to improve the completion rate of chemotherapy and reduce the occurrence of delayed chemotherapy has gained increasing attention and is the significant area of study in the field of cancer treatment. Studies have shown that Chinese medicine combined with chemotherapy could improve the quality of life in patients with stage IIIB/IV non-small-cell lung cancer (NSCLC).

Recently the maintenance therapy of non-small-cell lung cancer (NSCLC) patients who completed required treatment cycles has caused widespread interests in the medical field. Traditional Chinese medicine may be a useful complement in maintenance treatment of mid-to-late stage NSCLC.