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.

Effective symptom management can alleviate the physical and psychological distress experienced by patients with cancer, improve quality of life, and contribute to treatment adherence and improve clinical outcomes. However, most existing guidelines are developed for healthcare professionals, and patients and the public have limited access to standardized and comprehensible guidance on symptom management. To address this gap and to facilitate effective communication and shared decision-making, this study proposes the development of a patient version of the cancer symptom management guideline. The development process will adhere to the methodological framework recommended by the Guidelines International Network and the World Health Organization. The GRADE approach will be employed to assess the certainty of evidence and to formulate recommendations. In addition, the process will be informed by the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument and the Reporting Items for Practice Guidelines in Healthcare-Public or Patient Versions of Guidelines (RIGHT-PVG). This protocol outlines the establishment of the guideline working group, the identification and prioritization of key questions, evidence retrieval and appraisal, and the formulation of recommendations, with the aim of ensuring methodological rigor and transparency in the development of the patient guideline and providing methodological reference for similar guideline initiatives.

Objective To explore the safety and feasibility of 5G remote robot-assisted thoracoscopic anatomic lobectomy and segmentectomy. Methods A retrospective analysis was conducted on patients who underwent 5G remote robot-assisted thoracoscopic anatomic lobectomy or segmentectomy between July and September 2024. The surgeries were performed collaboratively by Shanghai Chest Hospital, Affiliated Hospital of Yangzhou University, and The First Affiliated Hospital of Nanchang University. Perioperative outcomes were recorded and analyzed. Results Ten patients were included, comprising 4 anatomic lobectomies and 6 segmentectomies. The median age was 61.0 (56.0, 72.0) years. The median intraoperative bi-directional network latency was 52.5 (39.0, 54.0) ms. There were no instances of network interruption, robot-related adverse events, or conversions to open thoracotomy. The median postoperative chest tube drainage volume was 500.0 (375.0, 600.0) mL, and the median hospital stay was 5.5 (4.0, 6.0) days. No complications of Clavien-Dindo grade Ⅱor higher occurred. All patients were recurrence-free and alive during the 6-month postoperative follow-up period. Conclusion 5G remote robot-assisted thoracoscopic surgery demonstrates high safety and operational stability. This technology shows promising potential for clinical application and is worthy of further development and utilization.

Definitive chemoradiotherapy (dCRT) has become a cornerstone in the treatment of locally advanced esophageal cancer; however, local control remains suboptimal, and persistent lesions or locoregional recurrences after treatment are not uncommon. For patients without distant metastases but with local failure, whether surgical intervention can still offer curative potential remains a major clinical dilemma. Salvage esophagectomy (SE) offers potential long-term survival for selected patients, but this procedure is performed in the context of severe fibrosis, impaired local blood supply, and obscured anatomical planes following chemoradiotherapy, resulting in significantly higher perioperative risk compared to primary esophagectomy. Consequently, controversies exist regarding patient selection, preoperative restaging, choice of surgical approach, extent of lymphadenectomy, gastrointestinal reconstruction, and perioperative management. In recent years, with the refinement of restaging modalities such as PET/CT, the accumulation of experience in high-volume centers, and emerging evidence from clinical studies, the clinical role of SE has gradually shifted from a "high-risk salvage measure" to a "selective curative strategy aimed at achieving long-term survival in carefully selected patients". Nevertheless, standardized guidelines for patient selection, technical approaches, and perioperative management are still lacking. Based on current evidence and clinical experience, experts organized by the Integrated Esophageal Cancer Committee of Chinese Anti-Cancer Association systematically reviewed key issues regarding SE, including its definition, indications, preoperative evaluation, choice of surgical approach, lymphadenectomy, gastrointestinal reconstruction, and perioperative management, and formulated a Chinese expert consensus. This consensus aims to provide guidance for standardized assessment, appropriate referral, individualized surgical decision-making, and optimized perioperative management of patients with locoregional failure after dCRT. Ultimately, this will increase the likelihood of R0 resection, reduce the risk of severe complications, and promote the safer, more judicious, and standardized implementation of SE in high-risk scenarios.

Nowadays, lung cancer is the most common and lethal invasive tumor type in Chinese population, challenging overall health level. However, personalized early-stage treatment is currently still not widely implemented, and the choice of treatment highly depends on experience of physician. Based on deep learning and radiomics principles, deep-radiomics is important for establishing objective and promotable precision medicine plans. Among all aspects, the explainability of a model is critical for its usage in clinical practice. This paper discusses the technical aspects of explainable deep-radiomics in lung cancer, and analyzes challenges we are facing. Non-fully supervised learning methods, as a current hotspot in deep learning technology, can construct more trustworthy and practically valuable deep learning models through the co-design method of performance-interpretability. Medical artificial intelligence faces three core challenges in transitioning from the laboratory to hospitals: high-level cognitive demands, data privacy and generalization capabilities, and regulatory compliance. However, with appropriate design, non-fully supervised learning holds the greatest potential to bridge the gap between design and application, enabling broader adoption.

This study aimed to investigate the mechanism of liquiritin (LQ) in the improvement of ventricular remodeling (VR) after acute myocardial infarction (AMI). Molecular docking was used to predict the binding affinity of liquiritin to thioredoxin-interacting protein (TXNIP). After 2 weeks of modeling, the rats were randomly divided into a model group, a low-dose liquiritin group (20 mg/kg LQ), and a high-dose liquiritin group (40 mg/kg LQ). Liquiritin was administered by gavage once a day, and the sham group and model group were given the same volume of 0.5% sodium carboxymethylcellulose (CMC-Na), with intervention of 4 consecutive weeks. Echocardiography was employed to detect the cardiac function, HE staining was used to observe cardiological changes, ELISA was used to detect the activity of serum creatine kinase-MB (CK-MB) activity, and the colorimetric method was adopted to detect serum malondialdehyde (MDA), total superoxide dismutase (T-SOD) and catalase (CAT) activities. RT-qPCR was used to detect the gene expressions of TXNIP, thioredoxin (TRX) and NACHT, LRR, and PYD domains-containing protein 3(NLRP3). Western blot was used to detect the protein expressions of TXNIP, TRX and NLRP3 in rat myocardial tissue. Molecular docking results showed that liquiritin had a good binding affinity to TNXIP target. After 20 and 40 mg/kg liquiritin intervention, the levels of ejection fraction (EF) and fractional shortening (FS) were significantly increased (P<0.01), and the levels of LVIDs, LVIDd, LVESV, and LVEDV were decreased (P<0.01). The myocardial structure was significantly improved, the cell arrangement tended to be regular, and the area of inflammatory cell infiltration and necrosis was reduced. Liquiritin significantly reduced the level of CK-MB (P<0.01), decreased the activity of MDA, and increased the activities of CAT and T-SOD (P<0.01). Liquiritin effectively inhibited the overexpression of TXNIP and NLRP3 genes and proteins, and enhanced the expression of TRX genes and proteins in the myocardial tissues of AMI rats. In conclusion, liquiritin has a regulatory effect on the TXNIP/TRX signaling pathway, inhibits the activation of the NLRP3 inflammasome, and thus improves ventricular remodeling after acute myocardial infarction.

Background/Aims: Biliary tract cancer (BTC) frequently harbors KRAS mutations, which are associated with resistance to traditional treatment and a poor prognosis. Synthetic lethality (SL) strategy may provide other targets of KRAS. Therefore, we aim to identify and validate potential therapeutic targets of KRAS for the treatment of BTC via SL. Methods: The dependency (DepMap) projects were used to predict the synthetic lethal gene of KRAS. FDA-approved anticancer drug library was applied to screen potential drugs effective against KRAS-mutant BTC. Furthermore, the synthetic lethal effects or corresponding mechanisms of potential genes and drugs on BTC were investigated using KRAS-mutant and KRAS-wild type BTC cell lines, patient-derived xenografts (PDX), and KRAS oncogene-driven tumor models, as well as other KRAS-mutant cancer cell lines. Results: Initially, we discovered that the loss of GATA2 reduced the viability of KRAS-mutant but not KRAS-wild-type BTC. Subsequently, the drug library screened out disulfiram, which primarily exerts a synthetic lethal effect by inhibiting interleukin-1β (IL-1β) in KRAS-mutant BTC. Mechanistically, GATA2 specifically enhanced the transcription of IL-1β to promote NF-κB signaling in KRAS-mutant BTC. IL-1β inhibition phenocopied GATA2 deficiency, leading to reduced KRAS-mutant BTC viability. These synthetically lethal effects were confirmed using PDX, a KRAS oncogene-driven tumor model, as well as in other KRAS-mutant cancer cell lines. Conclusions In summary, these results indicate that inhibiting GATA2/IL1β could be a therapeutic strategy in KRAS-mutant BTC and potentially other cancers.

[摘 要] 目的：构建了一种靶向成纤维细胞活化蛋白（FAP）且能分泌CTLA-4单链抗体（scFv）的CAR-T细胞，并探讨其联合血管靶向药物阿帕替尼对胶质母细胞瘤（GBM）的抗肿瘤效果及机制。方法：构建FAP-CTLA-4 CAR过表达载体，转导至T细胞中制备FAP-CTLA-4 CAR-T细胞。流式细胞术检测FAP-CTLA-4 CAR-T细胞表面分子CD69表达，ELISPOT检测其分泌IFN-γ的能力，细胞增殖实验分析FAP-CTLA-4 CAR-T细胞的增殖情况，杀伤实验分析其联合阿帕替尼处理对人脑星形胶质母细胞瘤U-87 MG细胞治疗效果的影响。建立小鼠U-87 MG细胞异种移植瘤模型。分别用FAP-CTLA-4 CAR-T细胞单独治疗或联合阿帕替尼治疗荷瘤小鼠，命名为FAP-CTLA-4 CAR-T组、阿帕替尼组和FAP-CTLA-4 CAR-T + 阿帕替尼组，观察各组小鼠肿瘤生长情况，免疫组化、TUNEL染色与免疫荧光法检测各组肿瘤组织中Ki-67、CD31表达及细胞凋亡情况，流式细胞术分析FAP-CTLA-4 CAR-T细胞在荷瘤小鼠体内的存活状况，H-E染色和血清生化分析评估联合治疗的安全性。结果：与FAP-CTLA-4 CAR-T组、阿帕替尼组相比，FAP-CTLA-4 CAR-T + 阿帕替尼组中CAR-T细胞的活化水平显著提高，细胞增殖能力明显增强，IFN-γ分泌细胞比例及对靶细胞的杀伤效率均提高（P < 0.01或P < 0.05）。在荷瘤小鼠模型中，联合治疗显著抑制肿瘤生长并延长小鼠生存时间（P < 0.01或P < 0.05），同时抑制肿瘤细胞增殖、降低微血管密度、促进肿瘤细胞凋亡，并延长CAR-T细胞在肿瘤组织中的存活时间（P < 0.01或P < 0.05），且未对主要组织器官造成毒性损伤。结论：FAP-CTLA-4 CAR-T细胞与阿帕替尼联合应用在GBM治疗中显示出协同增效作用与良好的安全性。

Although global brain science research has progressed rapidly in recent decades, several fundamental questions in neuroscience remain unresolved. In particular, the physical mechanism underlying neural signal transmission remains controversial, and the carriers responsible for neural information storage and retrieval have not yet been fully clarified. These unresolved issues motivate us to re-examine the processes of neural information generation, transmission, integration, storage, and retrieval from multiple perspectives. A key observation is that neural electromagnetic activities are closely associated with time. Their duration, temporal structure, and dynamic evolution play crucial roles in neural information processing. In this work, we analyze neural electromagnetic activities from the perspective of temporal scales (referred to here as the “time course”). By reviewing and integrating findings from previous studies, we examine the characteristic time requirements and dynamic features of neural processes occurring at different stages of information processing. These stages include neural signal generation, signal transmission along axons, synaptic integration, synaptic plasticity, and memory formation and retrieval. Based on this temporal analysis, we outline a framework describing neural electromagnetic activities across a wide range of time scales, spanning from microseconds to minutes, hours, or even longer periods associated with long-term memory, which suggests that neural information processing involves multiple physical processes operating at different time levels. Rapid electromagnetic events may occur on microsecond scales, whereas electrophysiological phenomena such as action potentials typically last on the order of milliseconds. Longer time scales are associated with synaptic plasticity and memory-related processes. From this perspective, we propose that the physical carrier of neural information may be transient electromagnetic pulses with durations on the microsecond scale. In this framework, action potentials can be interpreted as the macroscopic electrophysiological manifestation of underlying electromagnetic processes triggered by ionic currents across neuronal membranes. Rather than being the fundamental neural signal itself, the action potential may represent a measurable membrane-level response associated with the successful activation of these electromagnetic events. Moreover, we discuss a possible mechanism for long-term memory storage. Considering the apparent temporal contradiction between the millisecond-scale excitation of neurons and the long-term persistence of memories, we believe that long-term memory information may be stored within neural network topologies formed by electrical synapse coupling. Such structures, referred to as electrically coupled memory networks (ECMNs), may enable neurons within the same network to respond rapidly and synchronously to stimuli, thereby facilitating efficient memory retrieval. Overall, this study emphasizes the importance of considering the temporal organization of neural electromagnetic activities when interpreting neural signaling mechanisms. It may provide new insights into the physical nature of neural information carriers and the mechanisms of memory storage and retrieval. Furthermore, highlighting the potential role of electromagnetic interactions in neural activity may contribute to the development of new theoretical frameworks and experimental approaches in neuroscience. Such perspectives may also offer valuable references for future research on neural coding, brain function mechanisms, and neuromodulation technologies.