Objective To explore the potential molecular mechanism of Sangma Xingbei Tang （SMXBT） in the treatment of acute exacerbation of chronic obstructive pulmonary disease （AECOPD）. Methods TCMSP and TCMID databases were searched for the active ingredients of SMXBT, the targets of the active ingredients were predicted by SwissTargetPrediction database, and the AECOPD-related targets were searched by GeneCards and OMIM databases; the drug-active ingredient-target network and protein interaction network were constructed, and the GO enrichment and KEGG pathway enrichment were analyzed by the DAVID database. The drug-active ingredient-target network and protein interaction network were constructed, and the GO enrichment and KEGG pathway enrichment were analyzed by DAVID database, and molecular docking was performed by AutoDock Tools software. Animal experiments were conducted for validation. Results 192 active ingredients were obtained and 1066 targets were predicted in SMXBT. Network analysis showed that the top 3 active ingredients in SMXBT were quercetin, kaempferol, and glycyrrhizin, and the top 3 therapeutic targets were TNF, ALB, and IL-1β, etc. The GO analysis and KEGG showed that the main pathways were the cancer pathway, the MAPK signaling pathway, and the PI3K-Akt signaling pathway. Molecular docking showed that the binding energies of glycyrrhizin and AKT1, IL-1β, GFR, SRC, and quercetin and kaempferol and IL-1β were all <−5 kcal·mol. Animal experiments showed that Sangma Xingbei Decoction could treat AECOPD by anti-inflammatory effects and reduce changes in lung tissue structure. Additionally, it could lower the serum inflammatory levels of TNF-α, IL-6, and IL-8. Conclusion SMXBT may act on the targets of AKT1, IL-1β, EGFR, SRC, etc., reduce the serum inflammation levels of TNF-α, IL-6, IL-8, and improve the lung tissue structure and inflammatory response to achieve the therapeutic effect on AECOPD through the modulation of the PI3K/Akt signaling pathway, the MAPK signaling pathway, the cancer pathway, and other pathways of action.

Digital biopsy for liver diseases is characterized by the deep integration of artificial intelligence （AI） technologies and large-scale liver disease data， through which intelligent analytics are applied to support clinical decision-making and full-cycle management. This article reviews the AI technical framework based on standardized data governance and centered on multimodal large medical models， covering the application of natural language processing， knowledge map， generative AI， and large language models in the establishment of databases for specialty diseases， diagnosis， prognosis prediction， treatment， and automated medical documentation. This article also discusses the application prospects of this framework in medical education， scientific research， and healthcare management. Although this technique shows broad application potential， it still faces challenges in areas such as multi-center data integration， model interpretability， ethics， and data security. In the future， a smart ecosystem with closed-loop optimization and human-AI collaboration should be established to promote the comprehensive implementation of digital biopsy in the whole process of medicine， education， research， and management， thereby providing help for the precise prevention and control and holistic health management of liver diseases.

Objective:To analyze the association between inflammation-related dietary patterns and the risk for cognitive impairment in older adults aged ≥65 years in longevity areas in China by using reduced rank regression (RRR) analysis.Methods:This study used cross-sectional data from the 2021 Healthy Aging and Biomarkers Cohort Study, including the information about study participants' demographic characteristics, lifestyles, daily life activities, and disease histories. Dietary intake was obtained by using a simplified food frequency questionnaire. Cognitive impairment was evaluated based on the Mini-Mental State Examination Scale combined with years of education. Fasting venous blood samples were collected to detect inflammatory markers, especially high-sensitivity C-reactive protein (hs-CRP) and the platelet-to-lymphocyte ratio (PLR). RRR analysis was used to obtain inflammation-related dietary patterns using hs-CRP and PLR as response variables. Multivariate logistic regression model was used to analyze the association between dietary pattern score and the risk for cognitive impairment. Restricted cubic spline was used to explore the dose response relationship, and mediation analysis was used to quantify the mediating effects of hs-CRP and PLR.Results:Two dietary patterns were identified with RRR. The primary pattern was characterized by higher intakes of flour, red meat, and dairy products, and lower intake of fresh vegetables, explaining 6.84% of the variance in food intake and 0.50% of the variance in inflammatory markers. Compared with the T1 group, the T3 group had significantly higher risk for cognitive impairment ( OR=1.242, 95% CI: 1.034-1.491). Each one standard deviation increase in the dietary pattern score was associated with an 8.7% increase in the risk for cognitive impairment ( OR=1.087, 95% CI: 1.008-1.172), with a significant linear trend (overall-model P<0.001, non-linear P=0.295). Mediation analysis indicated that hs-CRP mediated 6.2% of the association between the dietary pattern and the risk for cognitive impairment. Conclusion:The inflammation- related dietary pattern characterized by higher consumption of flour, red meat, and dairy products and lower consumption of fresh vegetables is associated with an increased risk for cognitive impairment in older adults, and hs-CRP partially mediates this association.

Objective:To investigate the relationship between dietary behavior and sarcopenia in older adults aged ≥65 years in longevity areas of China based on latent class analysis.Methods:A total of 4 358 older adults aged ≥65 years were selected from the 2021 Healthy Aging and Biomarkers Cohort Study. The information about their demographic characteristics, lifestyles, and chronic disease histories were collected. A simplified food frequency questionnaire was used to collect information about their dietary intake in the last month. The food intake frequency and food category score were calculated, and the higher the food category score, the richer the dietary intake. Latent class analysis was used to identify the latent classes of the dietary behavior. Sarcopenia was diagnosed using the SARC-CalF. Multivariate logistic regression model was used to analyze the association of food category scores and different latent classes of the dietary behavior with the risk for sarcopenia.Results:In 4 358 older adults, 1 841 (42.24%) had sarcopenia. The frequencies of intakes of cereals and potatoes, vegetable and fruit, meat and bean products were lower in the sarcopenia group than in the non-sarcopenia group. The risk for sarcopenia decreased with the increase of food category score in older adults ( OR=0.850, 95% CI: 0.796-0.907). Latent class analysis identified 4 latent classes of the dietary behavior. Compared with those with class 1 (frequency of intake of all 5 food species was higher probability in T3 group), those with class 2 (frequency of intake of vegetables and fruits and energy-only foods were less likely to be in the T3 group) and class 3 (frequency of intake of all 5 food species was lower probability in T3 group) had significantly increased risk for sarcopenia ( OR=1.377, 95% CI: 1.131-1.676) and ( OR=1.354, 95% CI: 1.091-1.680), 37.7% and 35.4% increased risk for sarcopenia, respectively. Conclusion:Increasing dietary intake category and sufficient intake of various foods for a balanced dietary pattern can reduce the risk of sarcopenia in older adults.

Objective:To analyze the association between inflammation-related dietary patterns and the risk for cognitive impairment in older adults aged ≥65 years in longevity areas in China by using reduced rank regression (RRR) analysis.Methods:This study used cross-sectional data from the 2021 Healthy Aging and Biomarkers Cohort Study, including the information about study participants' demographic characteristics, lifestyles, daily life activities, and disease histories. Dietary intake was obtained by using a simplified food frequency questionnaire. Cognitive impairment was evaluated based on the Mini-Mental State Examination Scale combined with years of education. Fasting venous blood samples were collected to detect inflammatory markers, especially high-sensitivity C-reactive protein (hs-CRP) and the platelet-to-lymphocyte ratio (PLR). RRR analysis was used to obtain inflammation-related dietary patterns using hs-CRP and PLR as response variables. Multivariate logistic regression model was used to analyze the association between dietary pattern score and the risk for cognitive impairment. Restricted cubic spline was used to explore the dose response relationship, and mediation analysis was used to quantify the mediating effects of hs-CRP and PLR.Results:Two dietary patterns were identified with RRR. The primary pattern was characterized by higher intakes of flour, red meat, and dairy products, and lower intake of fresh vegetables, explaining 6.84% of the variance in food intake and 0.50% of the variance in inflammatory markers. Compared with the T1 group, the T3 group had significantly higher risk for cognitive impairment ( OR=1.242, 95% CI: 1.034-1.491). Each one standard deviation increase in the dietary pattern score was associated with an 8.7% increase in the risk for cognitive impairment ( OR=1.087, 95% CI: 1.008-1.172), with a significant linear trend (overall-model P<0.001, non-linear P=0.295). Mediation analysis indicated that hs-CRP mediated 6.2% of the association between the dietary pattern and the risk for cognitive impairment. Conclusion:The inflammation- related dietary pattern characterized by higher consumption of flour, red meat, and dairy products and lower consumption of fresh vegetables is associated with an increased risk for cognitive impairment in older adults, and hs-CRP partially mediates this association.

Objective:To investigate the relationship between dietary behavior and sarcopenia in older adults aged ≥65 years in longevity areas of China based on latent class analysis.Methods:A total of 4 358 older adults aged ≥65 years were selected from the 2021 Healthy Aging and Biomarkers Cohort Study. The information about their demographic characteristics, lifestyles, and chronic disease histories were collected. A simplified food frequency questionnaire was used to collect information about their dietary intake in the last month. The food intake frequency and food category score were calculated, and the higher the food category score, the richer the dietary intake. Latent class analysis was used to identify the latent classes of the dietary behavior. Sarcopenia was diagnosed using the SARC-CalF. Multivariate logistic regression model was used to analyze the association of food category scores and different latent classes of the dietary behavior with the risk for sarcopenia.Results:In 4 358 older adults, 1 841 (42.24%) had sarcopenia. The frequencies of intakes of cereals and potatoes, vegetable and fruit, meat and bean products were lower in the sarcopenia group than in the non-sarcopenia group. The risk for sarcopenia decreased with the increase of food category score in older adults ( OR=0.850, 95% CI: 0.796-0.907). Latent class analysis identified 4 latent classes of the dietary behavior. Compared with those with class 1 (frequency of intake of all 5 food species was higher probability in T3 group), those with class 2 (frequency of intake of vegetables and fruits and energy-only foods were less likely to be in the T3 group) and class 3 (frequency of intake of all 5 food species was lower probability in T3 group) had significantly increased risk for sarcopenia ( OR=1.377, 95% CI: 1.131-1.676) and ( OR=1.354, 95% CI: 1.091-1.680), 37.7% and 35.4% increased risk for sarcopenia, respectively. Conclusion:Increasing dietary intake category and sufficient intake of various foods for a balanced dietary pattern can reduce the risk of sarcopenia in older adults.

Medical imaging large-scale models demonstrate broad application prospects in the field of tumor diagnosis and treatment.Their powerful high-dimensional feature extraction and data analysis capabilities have brought revolutionary breakthroughs to precision oncology,driving the transformation of diagnostic and therapeutic paradigms.However,current research in this field still faces numerous challenges and technical bot-tlenecks.Based on the research background of artificial intelligence(AI)large-scale models,this article sys-tematically reviews the current research status of medical imaging large-scale models from three key dimensions:the construction of large-scale medical imaging datasets,optimization of large-scale model algorithms,and com-putational resource requirements.Furthermore,it elaborates on the application scenarios of these models in pre-cision oncology and provides a forward-looking perspective on their future development.The aim is to offer prac-tical guidance for advancing precision diagnosis and treatment of tumors.

Background and Aims:Single-incision laparoscopic cholecystectomy(LC)has become increasingly popular due to its minimal invasiveness and cosmetic advantages,but challenges remain in biliary identification and limited operative space.Indocyanine green(ICG)fluorescence navigation enables real-time visualization of the biliary tract and may enhance surgical safety and efficiency.This study aimed to evaluate the clinical application value of ICG fluorescence navigation in single-incision LC.Methods:A retrospective analysis was conducted on 59 patients with benign gallbladder diseases who underwent elective single-incision LC at Yixing Hospital affiliated to Jiangsu University from January 2023 to December 2024.Patients were divided into a fluorescence group(n=27)and a white-light group(n=32)according to whether ICG fluorescence navigation was applied.The two groups were compared in terms of Calot's triangle dissection time,operative time,intraoperative blood loss,surgeon satisfaction,hospital stay,and postoperative complications.Results:No significant differences were observed in baseline clinical characteristics between the two groups(all P＞0.05).In the fluorescence group,the cystic duct,common hepatic duct,and common bile duct were all successfully visualized.Compared with the white-light group,the fluorescence group had significantly shorter Calot's triangle dissection time[(25.56±3.49)min vs.(38.81±5.59)min],shorter operative time[(44.67±3.06)min vs.(61.31±4.96)min],and less intraoperative blood loss[(13.44±1.70)mL vs.(14.50±2.11)mL](all P＜0.05),with significantly higher surgeon satisfaction(P＜0.05).No intraoperative bile duct injury,conversion to three-port surgery,or postoperative complications occurred in either group.Conclusion:ICG fluorescence navigation can significantly improve biliary identification efficiency in single-incision LC,shorten operative time,reduce blood loss,and enhance surgeon satisfaction,demonstrating promising clinical application prospects.

Objective:To analyze the clinical phenotypes and genetic characteristics of pediatric patients with neurofibromatosis type 1 (NF1).Methods:A cross-sectional study was adopted. Clinical and imaging data of 25 pediatric patients diagnosed as having NF1 in Department of Pediatric Neurology, Linyi People's Hospital Affiliated to Shandong Second Medical University from January 2024 to July 2025 were collected. Whole exome sequencing and Sanger sequencing were used to conduct genetic testing on the pediatric patients and his/her parents. Protein 3D modeling of the domestic and foreign unreported variations was conducted using SWISS-MODEL software.Results:Among the 25 pediatric patients with NF1, 14 were male (56%) and 11 were female (44%), with age ranging from 8 months to 18 years. All pediatric patients exhibited café-au-lait macules, and 7 (28%) presented with plexiform neurofibromas. Genetic test identified 4 types of NF1 variants: nonsense variant ( n=11, 44%), frameshift variant ( n=9, 36%), missense variant ( n=3, 12%), and splice-site variant ( n=2, 8%). Importantly, 5 novel NF1 variants were discovered, including c.3455T>A, c.3709dupG, c.2665_2684del, c.7092_7095delinsTA, and c.3260del. Three pediatric patients inherited NF1 variant from their parents, while the remaining 22 harbored de novo mutation. Conclusion:NF1 exhibits a broad clinical spectrum, primarily affecting the skin and nervous system; this study identifies 5 previously unreported variants, expanding the genetic profile of NF1.

Medical imaging large-scale models demonstrate broad application prospects in the field of tumor diagnosis and treatment.Their powerful high-dimensional feature extraction and data analysis capabilities have brought revolutionary breakthroughs to precision oncology,driving the transformation of diagnostic and therapeutic paradigms.However,current research in this field still faces numerous challenges and technical bot-tlenecks.Based on the research background of artificial intelligence(AI)large-scale models,this article sys-tematically reviews the current research status of medical imaging large-scale models from three key dimensions:the construction of large-scale medical imaging datasets,optimization of large-scale model algorithms,and com-putational resource requirements.Furthermore,it elaborates on the application scenarios of these models in pre-cision oncology and provides a forward-looking perspective on their future development.The aim is to offer prac-tical guidance for advancing precision diagnosis and treatment of tumors.