Hepatitis B virus （HBV） infection is the primary cause of viral hepatitis and represents a substantial disease burden in China. However， effective and safe agents capable of completely eliminating HBV DNA are still lacking. In modern medicine， anti-HBV strategies mainly target covalently closed circular DNA （cccDNA）， among other mechanisms， and multiple novel drugs are currently under clinical investigation. Traditional medicine has been shown to exert anti-HBV effects through direct pathways， such as blocking viral entry， as well as indirect pathways， including the regulation of programmed cell death. Studies have confirmed that the integration of traditional Chinese medicine （TCM） and Western medicine in treating HBV infection and its related complications offers complementary advantages， particularly in enhancing HBV clearance rates， improving liver function， preventing various complications， and delaying the progression from hepatic fibrosis to hepatocellular carcinoma. This review focuses on advances in anti-HBV research involving TCM， Western medicine， and their integrated application， aiming to provide a basis for integrated HBV therapy and new drug development.

BACKGROUND:The Guilu Erxian Glue consists of Testudinis Plastrum,Cornu Cervi,Lycii Fructus,and Ginseng Radix.In earlier clinical observations,it is discovered that using Guilu Erxian Glue to treat patients with liver-kidney deficiency type knee osteoarthritis effectively alleviated knee pain,increased the range of motion,and improved walking ability.However,the exact mechanism by which oral administration of Guilu Erxian Glue can produce local therapeutic effects on the knee joint is still unclear.OBJECTIVE:To investigate the effects of Guilu Erxian Glue on gut microbiota in rats with knee osteoarthritis and to evaluate its mechanism using 16S rDNA sequencing and machine learning analysis.METHODS:Totally 18 female SD rats were randomly divided into three groups:blank group,model group,and Guilu Erxian Glue group,with 6 rats in each group.A knee osteoarthritis model was prepared using the destabilization of the medial meniscus surgical method.After successful modeling,the Guilu Erxian Glue group was given a decoction of Guilu Erxian Glue by gavage,while the blank and model groups were given an equal amount of distilled water.After 28 days of continuous intervention,high performance liquid chromatography was used to detect the active ingredients of Guilu Erxian Glue.MRI imaging was used to observe the condition of rat knee articular cartilage.Fecal samples were collected;DNA was extracted using a kit,amplified and purified by PCR,and an Illumina sequencing library was constructed.The Illumina MiSeq platform was used for high-throughput sequencing to generate raw sequence data.After obtaining the raw data,QIIME2 software was used to process the data.Linear Discriminant Analysis Effect Size analysis and random forest algorithm were used to screen for differential species in microbial data.KEGG and MetaCyc functional pathway analyses were used to explore the association between key microbial communities and experimental groups.Linear discriminant analysis effect values and random forest algorithm were used to screen for differential species.Association networks were used to analyze the interactions between microbial communities,and machine learning methods were used to analyze the composition and changes of gut microbiota.RESULTS AND CONCLUSION:(1)LC-MS component identification was conducted on the traditional Chinese medicine formula of Guilu Erxian Glue,and a total of 7 effective ingredients were identified.(2)MRI imaging showed that synovitis scope of high-density shadows in rats of the Guilu Erxian Glue group was reduced,and the degeneration of medial femoral condyle cartilage was less than that in the model group.(3)16S rDNA sequencing showed that the model group rats exhibited significant microbial imbalance,with a significant decrease in the abundance of Firmicutes and Bacteroidetes at the phylum level,while the proportion of Proteobacteria increased significantly(P＜0.05).The gut microbiota structure of rats in the Guilu Erxian Glue group was significantly improved,and the proportion of Firmicutes and Bacteroidetes increased,restoring a more diverse microbiota composition,approaching that of the blank group(P＜0.05).(4)KEGG and MetaCyc functional pathway analysis showed that the Guilu Erxian Glue group significantly activated multiple metabolic pathways,including amino acid metabolism,lipid metabolism,and biotin synthesis pathways(P＜0.05).(5)The results indicate that Guilu Erxian Glue contains seven active ingredients,and the changes in gut microbiota of knee osteoarthritis rats were analyzed using 16S rDNA sequencing.Guilu Erxian Glue can significantly improve the imbalance of gut microbiota,restore the abundance of beneficial bacteria,and have a significant impact on the composition of gut microbiota,providing scientific basis for the efficacy and mechanism of Guilu Erxian Glue.

BACKGROUND:The Guilu Erxian Glue consists of Testudinis Plastrum,Cornu Cervi,Lycii Fructus,and Ginseng Radix.In earlier clinical observations,it is discovered that using Guilu Erxian Glue to treat patients with liver-kidney deficiency type knee osteoarthritis effectively alleviated knee pain,increased the range of motion,and improved walking ability.However,the exact mechanism by which oral administration of Guilu Erxian Glue can produce local therapeutic effects on the knee joint is still unclear.OBJECTIVE:To investigate the effects of Guilu Erxian Glue on gut microbiota in rats with knee osteoarthritis and to evaluate its mechanism using 16S rDNA sequencing and machine learning analysis.METHODS:Totally 18 female SD rats were randomly divided into three groups:blank group,model group,and Guilu Erxian Glue group,with 6 rats in each group.A knee osteoarthritis model was prepared using the destabilization of the medial meniscus surgical method.After successful modeling,the Guilu Erxian Glue group was given a decoction of Guilu Erxian Glue by gavage,while the blank and model groups were given an equal amount of distilled water.After 28 days of continuous intervention,high performance liquid chromatography was used to detect the active ingredients of Guilu Erxian Glue.MRI imaging was used to observe the condition of rat knee articular cartilage.Fecal samples were collected;DNA was extracted using a kit,amplified and purified by PCR,and an Illumina sequencing library was constructed.The Illumina MiSeq platform was used for high-throughput sequencing to generate raw sequence data.After obtaining the raw data,QIIME2 software was used to process the data.Linear Discriminant Analysis Effect Size analysis and random forest algorithm were used to screen for differential species in microbial data.KEGG and MetaCyc functional pathway analyses were used to explore the association between key microbial communities and experimental groups.Linear discriminant analysis effect values and random forest algorithm were used to screen for differential species.Association networks were used to analyze the interactions between microbial communities,and machine learning methods were used to analyze the composition and changes of gut microbiota.RESULTS AND CONCLUSION:(1)LC-MS component identification was conducted on the traditional Chinese medicine formula of Guilu Erxian Glue,and a total of 7 effective ingredients were identified.(2)MRI imaging showed that synovitis scope of high-density shadows in rats of the Guilu Erxian Glue group was reduced,and the degeneration of medial femoral condyle cartilage was less than that in the model group.(3)16S rDNA sequencing showed that the model group rats exhibited significant microbial imbalance,with a significant decrease in the abundance of Firmicutes and Bacteroidetes at the phylum level,while the proportion of Proteobacteria increased significantly(P＜0.05).The gut microbiota structure of rats in the Guilu Erxian Glue group was significantly improved,and the proportion of Firmicutes and Bacteroidetes increased,restoring a more diverse microbiota composition,approaching that of the blank group(P＜0.05).(4)KEGG and MetaCyc functional pathway analysis showed that the Guilu Erxian Glue group significantly activated multiple metabolic pathways,including amino acid metabolism,lipid metabolism,and biotin synthesis pathways(P＜0.05).(5)The results indicate that Guilu Erxian Glue contains seven active ingredients,and the changes in gut microbiota of knee osteoarthritis rats were analyzed using 16S rDNA sequencing.Guilu Erxian Glue can significantly improve the imbalance of gut microbiota,restore the abundance of beneficial bacteria,and have a significant impact on the composition of gut microbiota,providing scientific basis for the efficacy and mechanism of Guilu Erxian Glue.

The left transradial access is less widely used in neurointervention. This article reports a case of an internal carotid dissection aneurysm with a aberrant right subclavian artery (ARSA) type aortic arch. Preoperative assessment showed that a risk of selective vascular catheterization through the right radial artery approach. After switching to the left transradial access, angiography and subsequent intervention surgery were successfully completed. This has accumulated valuable clinical experience for neurointerventional treatment through the left transradial access.

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.

Objective: To investigate the association between brain and muscle Arnt like protein 1( BMAL 1) gene and ambulatory blood pressure (ABP) parameters in college students, and to explore the influence of the interaction between the gene and weight status on the ABP levels, so as to provide reference for early prevention of chronic cardiovascular diseases related to hypertension. Methods: During September 2022 to June 2024, a total of 510 college students were recruited from a university in Changsha, China for on site questionnaire investigation, physical examination, ABP monitoring and genotyping testing. Multiple linear regression method was used to explore the association between the BMAL 1 gene and ABP indicators. An interaction term was included in the general linear model of multiple linear regression to analyze the interactive effects of BMAL 1 gene polymorphisms and weight status on ABP levels. Results: The prevalence of abnormal 24 hour blood pressure, abnormal daytime blood pressure, and abnormal nighttime blood pressure were 3.3%, 3.1%, and 3.9%, respectively. Multiple linear regression analysis showed that the BMAL 1/rs7950226 polymorphism was positively correlated with the 24 hour diastolic blood pressure(DBP) and the daytime DBP level after adjusting for gender, age, and body mass index( β =0.87,0.99, both P <0.05). Also, interaction between BMAL 1/ rs 11022775 and weight status on the nighttime DBP level ( P interaction =0.03) was found. The CC genotype carriers had significantly higher nighttime DBP level ( β =3.52, P <0.05) among overweight/obesity college students, but no differences in nocturnal DBP levels were observed between CC genotype carriers and TT+CT genotype carriers among college students with normal body weight( P > 0.05). Conclusions The rs 7950226 polymorphism is associated with 24 hours DBP and daytime DBP levels. Significant interaction between rs 11022775 with weight status on the nighttime DBP level is found among college students.

Objective: To analyze the demographic evolution of China over the forthcoming decade (2026-2035) and to systematically evaluate its potential implications for voluntary blood donation practices and the equilibrium of clinical blood supply and demand. Methods: Leveraging comprehensive blood collection and utilization data from 2021 to 2025 at the national, provincial (Shandong), and municipal (Zibo) levels, we identified and examined critical determinants influencing blood management systems. The DeepSeek-V3.2 large language model was deployed to project future population age structures, and to generate quantitative estimates for hospitalization volumes, clinical blood requirements, available blood supply, and donation frequencies for the target years 2030 and 2035, thereby furnishing an evidence base for strategic planning and priority-setting. Results: DeepSeek-derived projections indicate that the proportion of China's population aged 60 years and above will surpass 27% (370 million/1 380 million) by 2030 and exceed 30% (400 million/1 350 million) by 2035, signaling a marked acceleration in population aging. This demographic shift is projected to reduce the proportion of the national population eligible to donate blood from 59.2% (835 million out of 1.41 billion) to 52% (710 million out of 1.36 billion), while the demand for blood is expected to increase by 87.8% relative to 2024, rising from 26.927 million units to 50.56 million units. Sustaining blood supply-demand balance will necessitate progressive annual increases in donation rates. Under prevailing policy frameworks, the required blood donation rate among the eligible population in Shandong Province would need to rise by 122.57% by 2035 [from 20.07‰ (1.1641 million person-times/58 million people) to 44.67‰ (2.189 million person-times/49 million people)] to potentially satisfy projected clinical requirements. Conclusion: By integrating DeepSeek-V3.2, AI predictive models, and blood donation incentive measures, it is possible to monitor population demographics, forecast clinical blood demand, manage blood supplies, and ensure the long-term sustainability of clinical blood availability. It must be acknowledged, however, that the present analysis is constrained by a limited data foundation (comprising only five annual observation points); accordingly, the projections should be interpreted as directional trend indicators or methodological illustrations rather than definitive quantitative forecasts.

Background Diarrhea disease is a common intestinal infectious disease, and its incidence is affected by meteorological conditions. A better understanding of its epidemiological patterns and influencing factors, together with the construction of reliable prediction models, is of great significance for precise public health prevention and control. Objective To clarify the epidemic characteristics of adult diarrhea disease in Shanghai, analyze the associations of meteorological factors and a web search index with adult diarrhea disease, and develop and compare forecasting models to support precise regional prevention and control. Methods Weekly surveillance data of adult diarrhea disease cases from the Shanghai Comprehensive Surveillance Information System for Diarrhea Diseases, together with concurrent meteorological observation data and web search index (Baidu index) data from 2014 to 2019, were collected. A distributed lag non-linear model (DLNM) was adopted to analyze the associations of multiple meteorological factors and the web search index with the number of diarrhea disease cases. By integrating meteorological factors and web search index data, three types of forecasting models were developed, including autoregressive integrated moving average (ARIMA), Random Forest, and extreme gradient boosting (Xgboost), and their predictive performances were evaluated. Results Adult diarrhea disease in Shanghai exhibited seasonal variation, with an major incidence peak in summer and winter peaks in some years. The number of cases declined annually after 2015. Mean temperature was significantly associated with the risk of diarrhea disease, and both low and high temperature exposures were associated with increased risks. The highest risk was observed at 32.8°C (RR=2.04, 95%CI: 1.62, 2.55), while the strongest effect of low temperature was observed at 0.9 °C (RR=1.53, 95%CI: 1.25, 1.88). When relative humidity exceeded 69%, the risk of diarrhea disease increased with relative humidity, reaching a peak at 81% (RR=1.20, 95%CI: 1.07, 1.35). When weekly cumulative precipitation exceeded 16 mm, the risk also increased with increasing precipitation, reaching a maximum at 105 mm (RR=1.23, 95%CI: 1.07, 1.42). The web search index was positively associated with the risk of diarrhea disease. Model prediction indicated that both the Random Forest model and the Xgboost model adequately captured the overall trend in diarrhea disease cases, with R² values generally exceeding 0.7. Notably, the Xgboost model demonstrated greater accuracy in capturing peak intensities. Conclusion Meteorological factors are associated with adult diarrhea disease in Shanghai. The web search index may serve as an auxiliary indicator for diarrhea forecasting. Machine learning models, with advantages in integrating multisource data, may provide effective predictive tools for the prevention and control of diarrhea disease.

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.