Objective To explore the application value of three mainstream large language models in the diagnosis, differential diagnosis, and treatment decision support of the primary diseases related to pediatric liver transplantation. Methods Seventy-nine cases of pediatric liver transplantation-related diseases diagnosed through pathological or clinical follow-up data were collected from Renji Hospital, Shanghai Jiao Tong University School of Medicine or published high-quality case reports. These cases covered 25 types of primary diseases such as cholestatic liver disease, metabolic diseases, and tumors. Standardized prompts were used to input the case information into the DeepSeek-R1, ChatGPT-4o and Grok-3 models, and the accuracy of their preliminary diagnosis and differential diagnosis based on basic clinical data was evaluated. The final diagnosis accuracy and the response time after supplementary examination were also assessed, as well as the completeness and rationality of their analysis of disease treatment principles. Results In the initial diagnosis and differential diagnosis stage, the comprehensive accuracy of DeepSeek-R1 was the highest [72.1%, 95% confidence interval (CI) 61.4% - 80.8%], and there was a statistically significant difference in the comprehensive accuracy of the three models for initial diagnosis (P = 0.008). After adding further examination information, the final diagnosis accuracy of the three models increased, with DeepSeek-R1 at 88.6% (95% CI 79.7% - 93.9%), ChatGPT-4o at 87.3% (95% CI 78.2% - 93.0%), and Grok-3 at 78.5% (95% CI 68.2% - 86.1%). There was no statistically significant difference among the three models (P = 0.05). The scores given by experts for the treatment principles showed good consistency (Kappa = 0.769). In addition, the response time of ChatGPT-4o is shorter than that of the other two models [(24 ± 7) s]. Conclusions Large language models demonstrate good efficacy in the diagnosis and treatment decision-making process of various pediatric liver diseases, have a good application prospect for auxiliary diagnosis and decision support, and are expected to help improve the accuracy and efficiency of clinical diagnosis and treatment of pediatric liver transplantation-related primary diseases.

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.

Liver transplantation is the standard treatment for end-stage liver disease， but it has long been limited by the shortage of donor livers. In clinical practice， three types of donor liver （domino donor livers， small-for-size grafts， and steatotic donor livers） are limited due to issues such as metabolic defects， insufficient volume， and poor quality. This article systematically reviews the definition， clinical application， and bottlenecks of the above three types of donor livers and elaborates on the unique value and clinical practice advances of auxiliary liver transplantation （a special surgical procedure that retains part or all of the recipient’s own liver and superimposes donor liver function support） in avoiding small-for-size syndrome， compensating for metabolic defects， and expanding the application of marginal livers， in order to provide a targeted clinical practice reference for optimizing liver transplantation strategies for end-stage liver disease and improving donor liver utilization rate.

The compilation explanation for the Expert Consensus on Clinical Application of Ruyi Zhenbao pills systematically expounds the formulation methods and evidence-based basis of this consensus. In response to the weak clinical application evidence and ambiguous indications of Ruyi Zhenbao pills， the Institute of Basic Research in Clinical Medicine of China Academy of Chinese Medical Sciences， Wangjing Hospital of China Academy of Chinese Medical Sciences， and Dongzhimen Hospital of Beijing University of Chinese Medicine invited 39 experts from 31 medical institutions across China to complete the compilation through multi-disciplinary collaboration. The compilation strictly adheres to the World Health Organization （WHO） guidelines for guideline development and the GRADE evidence grading system. The work process covered the construction of clinical questions （a total of 30 clinical questions were determined based on expert voting results）， evidence retrieval （covering Chinese and English databases and gray literature）， safety assessment （integrating drug monitoring data and clinical research）， and the formation of recommendation opinions and consensus suggestions （18 recommendation opinions were reached through the GRADE grid method， and 22 consensus suggestions were reached through majority voting rules）. The results show that the consensus clearly states that this medicine is suitable for knee osteoarthritis， gouty arthritis， rheumatoid arthritis， neuropathic pain， and sequelae of stroke. In terms of safety， the adverse reactions are mainly gastrointestinal reactions such as nausea， vomiting， and abdominal pain. Pregnant women and those allergic to the ingredients of this medicine are contraindicated. The compilation process underwent three rounds of expert reviews and adopted methods such as nominal group technique and Delphi method to ensure the rigor of the consensus compilation process. It provides a paradigm for the formulation of expert consensus on clinical application of Tibetan medicine through the academic promotion and dynamic revision.

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.

Intestinal microecological changes are closely related to liver cirrhosis and cirrhosis-related sarcopenia.Studies has demonstrated that interventions targeting the intestinal microbiota could contribute to the treatment of cirrhosis and cirrhosis-related sarcopenia.Here we reviewed the research progress on the alterations in intestinal microbiota during liver cirrhosis and the underlying mechanisms by which these changes impacted the development of the disease.The potential of microbiota-targeted interventions in both preventing and treating liver cirrhosis and related sarcopenia was also discussed,which might provide valuable insights into clinical diagnosis and management of the disease.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To investigate the risk factors for adverse outcomes in elderly patients with nonvalvular atrial fibrilla-tion(AF)after percutaneous left atrial appendage closure(LAAC)combined with radiofrequency ablation(RFA).Meth-ods:A total of 199 elderly AF patients who admitted in Department of Cardiology,Yuhuangding Hospital between Jan 1st,2019 and Aug 17th,2023 and underwent LAAC combined RFA were enrolled.The patients were divided into case group(n=78)and control group(n=121)according to presence of atrial arrhythmia and/or cerebral infarction(CI)within one year after operation.Baseline clinical data,laboratory indexes and color Doppler ultrasound indexes were compared be-tween the two groups,and multivariate Logistic regression was used to analyze the risk factors for postoperative atrial ar-rhythmia and/or CI.Receiver operating characteristic(ROC)curve was constructed to analyze the predictive efficacy of risk factors for atrial arrhythmia and/or CI after operation.Results:Univariate analysis indicated that compared with pa-tients in control group,those in case group had significant higher proportions of preoperative persistent atrial fibrillation(52.60％ vs.37.20％),old myocardial infarction(OMI)(11.50％vs.1.70％)and left atrial diameter(LAD)[(45.47±6.90)mm vs.(43.34±6.64)mm](P＜0.05 or＜0.01).Multivariate Logistic regression analysis indicated history of OMI(OR=8.736,95％CI 1.772～43.069,P=0.008)and LAD(OR=1.053,95％CI 1.006～1.102,P=0.027)were independent risk factors of adverse outcomes in elderly AF patients after LAAC+RFA.ROC analysis indicated that predic-tive efficacy of combined detection of OMI and LAD(AUC=0.663,95％CI 0.593～0.728)for adverse outcomes within 1 year in elderly AF patients after LAAC+RFA was significantly better than those of OMI(AUC=0.549,95％CI 0.477～0.620)and LAD(AUC=0.602,95％CI 0.531～0.671)alone(Z=3.045,2.312,P=0.002,0.021).Conclusion:His-tory of old myocardial infarction and left atrial diameter are independent risk factors for atrial arrhythmia and/or cerebral infarction within 1 year after percutaneous left atrial appendage closure combined with radiofrequency ablation in elderly patients with atrial fibrillation,and the combination has good predictive performance.

[This corrects the article DOI: 10.1016/j.apsb.2018.08.009.].

Objective:To investigate the anatomical characteristics of the atlantoaxial joint associated with nonuniform settlement of the C 2 lateral mass (C 2LM-NUS) and its correlation with atlantoaxial osteoarthritis. Methods:A retrospective analysis was conducted on clinical and imaging data of 522 hospitalized patients (288 males, 234 females; mean age 60.8±11.2 years; range 18-83 years) who underwent CT scans of the head/neck or cervical spine at the Second Affiliated Hospital of Army Medical University between January 1, 2022 and December 31, 2022. Multiplanar reconstruction of CT data was performed to measure the settlement of the C 2 lateral mass (C 2LMS). Patients with a difference in bilateral C 2LMS (d-C 2LMS) >1.4 mm were classified into the C 2LM-NUS group (137 cases; 71 males, 66 females; mean age 63.3±11.6 years), while the normal group included 385 patients (217 males, 168 females; mean age 59.9±11.0 years). Imaging parameters of the atlantoaxial joint were measured, including the C 1, 2 coronal inclination angle (C 1, 2 CI), atlanto-dental interval (ADI), lateral atlanto-dental interval (LADI), coronal deviation angle of the odontoid (Od-CDA), and C 1, 2 relative rotation angle (C 1, 2 RRA). Osteoarthritis prevalence was recorded. A normal C 0-C 3 finite element (FE) model was constructed using CT data from a 48-year-old female in the normal group. A C 2LM-NUS FE model was developed based on anatomical differences between the C 2LM-NUS and normal groups, and stress distribution on the C 2 lateral mass articular surface was analyzed under flexion-extension, lateral bending, and axial rotation torques. Results:The C 2LM-NUS group exhibited asymmetric atlantoaxial joint morphology, with bilateral differences in C 1, 2CI and LADI of 8.5°(5.8°, 11.3°) and 0.8(0.1, 1.4) mm, respectively, significantly greater than those in the normal group [1.7°(0.8°, 2.7°) and 0.2(0.1, 0.5) mm, P<0.05]. Od-CDA and C 1, 2RRA were 3.9°(2.0°, 5.4°) and 7.2°(5.0°, 10.0°) in the C 2LM-NUS group, exceeding the normal group's values [0°(0°, 1.0°) and 0°(0°, 5.5°), P<0.05]. The prevalence of C 2LM-NUS was 37.8% in the atlantoaxial osteoarthritis group, significantly higher than in the non-osteoarthritis group (22.8%, P<0.05). Significant differences were observed in age (68.3±9.4 vs. 58.6±10.8 years), sex distribution (50/69 vs. 238/165), and C 1, 2RRA [5.6°(0°, 8.2°) vs. 3.8°(0°, 6.2°)] between the osteoarthritis and non-osteoarthritis groups ( P<0.05). After adjusting for age, sex, and C 1, 2RRA, binary logistic regression identified C 2LM-NUS as an independent risk factor for atlantoaxial osteoarthritis [ OR=2.024, 95% CI (1.300, 3.150), P<0.001]. FE analysis demonstrated a reduced C 1, 2 range of motion in the C 2LM-NUS model, with elevated stress concentrations on the settled side lateral mass during simulated flexion-extension, lateral bending, and rotation. Conclusions:The study indicated that C 2LM-NUS is associated with asymmetric anatomical changes in the atlantoaxial joint, increasing the risk of osteoarthritis. Stress concentration on the C 2 lateral mass articular surface, caused by C 2LM-NUS, is a biomechanical contributor to this heightened risk.