Objective:To investigate the survival outcomes and prognostic factors in patients undergoing radical resection for pancreatic body and tail cancer.Methods:A retrospective case series study was conducted on 992 patients who underwent radical resection for pancreatic body and tail cancer at the Pancreatic Center of the First Affiliated Hospital of Nanjing Medical University from January 2016 to June 2024. In this study, 577 (58.2%) were male and 415 (41.8%) were female,with an age of (65±9) years (range: 26 to 86 years). Follow-up continued until June 2024. Survival rates were estimated using the Kaplan-Meier method,and prognostic factors were identified using univariate and multivariate Cox proportional hazards models.Results:Among 992 patients,open surgery was the predominant approach (89.1%, 884/992), and radical antegrade modular pancreatosplenectomy (RAMPS) was performed in 317 patients (32.0%). Combined organ resection,venous resection,and arterial resection were performed in 23.5%, 9.3%,and 11.2% of patients,respectively. The rates of R0, R1-1 mm, and R1-direct resections were 49.8% (494/992),41.5% (412/992), and 8.7% (86/992),respectively. Stage ⅡB was the most common TNM stage (32.2%,319/992). A total of 801 patients (80.8%) received adjuvant chemotherapy. The median follow-up period was 32.0(8.8) months(range:3.2 to 105.3 months),during which 508 patients (51.2%) died. The overall median survival (OS) was 26.4 months,with 1-,3-, and 5-year survival rates of 79.0%,40.0%, and 29.0%, respectively. In the recent five years (from 2020 to 2024), the median OS improved significantly to 34.1 months compared to 20.0 months from 2016 to 2019 ( P<0.01). Histological subtype analysis showed that the median OS time was 26.7 months for pancreatic ductal adenocarcinoma (PDAC, n=855),58.9 months for invasive intraductal papillary mucinous carcinoma (IPMC, n=32),and 15.7 months for adenosquamous carcinoma of pancreas (ASCP, n=73) ( P=0.001). Among PDAC patients, adjuvant chemotherapy significantly improved survival (29.1 months vs. 14.4 months, P<0.01);in IPMC patients, adjuvant chemotherapy also extended survival (65.7 months vs. 58.9 months, P=0.047). Although ASCP patients receiving chemotherapy had a longer median OS time than those without (18.8 months vs. 8.9 months),the difference was not statistically significant ( P=0.151). Multivariate Cox regression analysis in PDAC patients indicated that adjuvant chemotherapy, R0 resection, T stage,N stage,and tumor differentiation were independent prognostic factors ( P<0.01). The median OS time by TNM stage was:not reached for stage ⅠA, 51.6 months for ⅠB, 25.5 months for ⅡA, 23.7 months for ⅡB, 23.0 months for Ⅲ, and 14.4 months for Ⅳ. The median OS time for R0,R1-1 mm,and R1-direct resections was 34.1,24.7,and 15.7 months,respectively ( P<0.01). Conclusion:Adjuvant chemotherapy,R0 resection,tumor stage,and differentiation are independent prognostic factors for pancreatic body and tail cancer.

The introduction of immunotherapy has changed the traditional paradigms in cancer treatment and has demonstrated substantial clinical benefits across various malignancies. However, this progress is accompanied with emerging challenges, such as immune evasion and low response rates to immunotherapy. As an immunomodulatory molecule, zymogen granule protein 16 (ZG16) plays a crucial role in immunotherapy by inhibiting the binding of programmed death 1 (PD-1) to its ligand PD-L1, preventing immune evasion and restoring the immune system's ability to recognize and eliminate tumor cells. ZG16 is downregulated in multiple gastrointestinal tumors, and its regulatory mechanisms vary depending on the tumor type. ZG16 expression is closely associated with patient prognosis, and it has been identified as a favorable prognostic marker in colorectal cancer. This article provides a systematic overview of the structural characteristics, expression regulation mechanisms, and antitumor immune functions of ZG16.

ObjectiveTo investigate the mRNA expression profile and clinical significance of the RAB24 gene in hepatocellular carcinoma （HCC） based on multi-source database including The Cancer Genome Atlas （TCGA） and clinical cohort validation. MethodsData from TCGA （n=424） were utilized to evaluate the prognostic value of RAB24 expression via Kaplan-Meier analysis and Cox regression models. An independent clinical cohort comprising 90 HCC and 30 paracancerous tissues was established to validate protein expression and histopathological features through immunohistochemistry （IHC） and routine histological staining. The TIMER database was employed to analyze the association between RAB24 and the tumor microenvironment （TME）. Furthermore， integrated with LinkedOmics， GSEA and GSVA algorithms were used to quantitatively characterize the core molecular pathways regulated by RAB24. ResultsThe mRNA expression level of RAB24 in HCC was significantly higher than that in non-tumor liver tissues （P<0.05）. Kaplan-Meier analysis revealed that the overall survival （OS） of patients in the RAB24 low-expression group was significantly longer compared to the high-expression group （HR=1.570， Log-rank P=0.01）. Cox proportional hazards regression analysis demonstrated that RAB24 expression （HR=1.480， P=0.028）， T stage （HR=2.949， P<0.001）， M stage （HR=4.077， P=0.017）， and tumor residual status （HR=2.317， P<0.001） were independent prognostic factors for HCC. RAB24 expression was highly correlated with alpha-fetoprotein （AFP） levels and immune infiltration levels （P<0.01）， and showed a significant， low-level positive correlation with the TP53 gene （Rho=0.31， P<0.01）. Quantitative analysis of the RAB24 protein in clinical samples validated that RAB24 expression in HCC tissues was significantly elevated compared to non-tumor liver tissues （P<0.05）. TIMER analysis confirmed that RAB24 expression was highly associated with the infiltration of multiple immune cells （such as B cells and NK cells）， as well as stromal and tumor purity scores in tumor tissues （P<0.01）. GSEA/GSVA enrichment revealed that the P53， MYC， and mTORC1 signaling pathways were activated in the RAB24 high-expression group； correlation analysis confirmed that the RAB24 expression level exhibited a significant， low-level positive correlation with the ribosome pathway activity score （r=0.23， P<0.01）. ConclusionsThe RAB24 gene， as a poor prognostic factor for HCC patients， is a potential biomarker for HCC diagnosis， treatment and prognosis. The study is expected to lay a theoretical foundation for the precise diagnosis and treatment of clinical HCC.

Objective:To investigate the survival outcomes and prognostic factors in patients undergoing radical resection for pancreatic body and tail cancer.Methods:A retrospective case series study was conducted on 992 patients who underwent radical resection for pancreatic body and tail cancer at the Pancreatic Center of the First Affiliated Hospital of Nanjing Medical University from January 2016 to June 2024. In this study, 577 (58.2%) were male and 415 (41.8%) were female,with an age of (65±9) years (range: 26 to 86 years). Follow-up continued until June 2024. Survival rates were estimated using the Kaplan-Meier method,and prognostic factors were identified using univariate and multivariate Cox proportional hazards models.Results:Among 992 patients,open surgery was the predominant approach (89.1%, 884/992), and radical antegrade modular pancreatosplenectomy (RAMPS) was performed in 317 patients (32.0%). Combined organ resection,venous resection,and arterial resection were performed in 23.5%, 9.3%,and 11.2% of patients,respectively. The rates of R0, R1-1 mm, and R1-direct resections were 49.8% (494/992),41.5% (412/992), and 8.7% (86/992),respectively. Stage ⅡB was the most common TNM stage (32.2%,319/992). A total of 801 patients (80.8%) received adjuvant chemotherapy. The median follow-up period was 32.0(8.8) months(range:3.2 to 105.3 months),during which 508 patients (51.2%) died. The overall median survival (OS) was 26.4 months,with 1-,3-, and 5-year survival rates of 79.0%,40.0%, and 29.0%, respectively. In the recent five years (from 2020 to 2024), the median OS improved significantly to 34.1 months compared to 20.0 months from 2016 to 2019 ( P<0.01). Histological subtype analysis showed that the median OS time was 26.7 months for pancreatic ductal adenocarcinoma (PDAC, n=855),58.9 months for invasive intraductal papillary mucinous carcinoma (IPMC, n=32),and 15.7 months for adenosquamous carcinoma of pancreas (ASCP, n=73) ( P=0.001). Among PDAC patients, adjuvant chemotherapy significantly improved survival (29.1 months vs. 14.4 months, P<0.01);in IPMC patients, adjuvant chemotherapy also extended survival (65.7 months vs. 58.9 months, P=0.047). Although ASCP patients receiving chemotherapy had a longer median OS time than those without (18.8 months vs. 8.9 months),the difference was not statistically significant ( P=0.151). Multivariate Cox regression analysis in PDAC patients indicated that adjuvant chemotherapy, R0 resection, T stage,N stage,and tumor differentiation were independent prognostic factors ( P<0.01). The median OS time by TNM stage was:not reached for stage ⅠA, 51.6 months for ⅠB, 25.5 months for ⅡA, 23.7 months for ⅡB, 23.0 months for Ⅲ, and 14.4 months for Ⅳ. The median OS time for R0,R1-1 mm,and R1-direct resections was 34.1,24.7,and 15.7 months,respectively ( P<0.01). Conclusion:Adjuvant chemotherapy,R0 resection,tumor stage,and differentiation are independent prognostic factors for pancreatic body and tail cancer.

ObjectiveTo investigate the effects of Ditantang on metabolic dysfunction-associated fatty liver disease （MAFLD） in rats by focusing on the regulation of glycogen synthesis mediated by the uridine diphosphate glucose （UDPG）-site-1-protease （S1P）-sterol regulatory element-binding protein （SREBP） axis. MethodsMAFLD was modeled in SD rats by feeding a high-fat diet for 12 weeks. Rats were randomized into a control group， a model group， low-， medium-， and high-dose （2.68， 5.36， and 10.72 g·kg^-1·d^-1， respectively） Ditantang groups， and a positive control （Yishanfu， 0.18 g·kg^-1·d^-1） group. Starting from week 9 of modeling， each treatment group received corresponding drug interventions via gavage. After the experiment， the serum levels of total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C） were measured. The levels of TG， non-esterified fatty acids （NEFA）， glycogen， glucose-6-phosphate （G6P）， glucose-1-phosphate （G1P）， and UDPG in the liver tissue samples were measured by biochemical assay kits. Liver pathological morphology and lipid/glycogen deposition were observed via hematoxylin-eosin （HE） staining， oil red O staining， and Periodic acid-Schiff （PAS） staining. Immunofluorescence assay was conducted to detect the expression and localization of phosphoglucomutase-1 （Pgm1） and UDP-glucose pyrophosphorylase 2 （Ugp2） in the rat liver. Real-time PCR and Western blot were employed to quantify mRNA and protein levels， respectively， of key glycogen synthesis enzymes （Pgm1 and Ugp2）， key lipogenesis-related transcription factor SREBP and its target genes ［acetyl-CoA carboxylase （ACC）， fatty acid synthetase （FASN）， and stearoyl-CoA desaturase （SCD1）］， as well as S1P. ResultsCompared with the control group， the model group exhibited elevated levels of TC， TG， LDL-C in the serum and TG and NEFA in the liver， decreased serum HDL-C level （P<0.01）， reduced hepatic levels of G1P， G6P， UDPG， and glycogen （P<0.01）， appearance of fat vacuoles of varying sizes and abundant red lipid droplets， with prominent lipid deposition and reduced areas of deep glycogen staining in the liver tissue， upregulated mRNA and protein levels of SREBP1， S1P， ACC， FASN， and SCD1 （P<0.01）， and downregulated mRNA and protein levels of Pgm1 and Ugp2 （P<0.01）. Compared with the model group， all doses of Ditantang reduced the levels of TC and TG in the serum and TG and NEFA in the liver， while elevating the serum HDL-C level. Additionally， medium- and high-dose Ditantang reducted the LDL-C level （P<0.05）. Ditantang at all doses increased the hepatic levels of G1P， G6P， UDPG， and glycogen， alleviated hepatic steatosis and lipid deposition， enhanced glycogen storage， downregulated the mRNA and protein levels of SREBP1， S1P， ACC， FASN， and SCD1 （P<0.05）， while upregulating the expression of Pgm1 and Ugp2 （P<0.01）. Immunofluorescence analysis revealed decreased fluorescence intensity of S1P and enhanced fluorescence intensity of Ugp2 （P<0.01）. ConclusionDitantang can regulate glycogen synthesis through the UDPG-S1P-SREBP axis to prevent and treat MAFLD.

Objective To provide precise,effective,and intuitive nutritional and dietary recommendations for different population groups,a multi-modal nutritional knowledge graph was constructed,which includes entities such as food,nutrition,population,and diseases.Methods Data sets in the field of nutrition were obtained using web crawling and other technical means.The OneRel model was referenced to complete the joint extraction of Chinese entity relationships and construct a text library.The RoBERTa-ResNet model were used to learn the features of text and image data separately,to align images with text,and to construct a multi-modal knowledge graph.Results The F1 value of the joint entity relationship extraction model was 0.703.The constructed multi-modal knowledge graph contains 3312 textual entities,11 259 relationships,and 1000 image entities.Conclusion The algorithms used in this study to construct the multi-modal nutritional knowledge graph achieve good results.This knowledge graph not only systematically integrates multi-modal knowledge in the field of nutrition and enables good visual query capabilities,but also serves as the underlying support for downstream tasks such as intelligent question answering and nutritional recommendation systems.

Objective To analyze the distribution characteristics of 13 common respiratory pathogens in children in Zibo area from 2020 to 2022.Methods A total of 3091 hospitalized children with respiratory infections admitted to Zibo Maternal and Child Health Hospital from January 2020 to December 2022 were selected as the subjects.Throat swabs or bronchoalveolar lavage fluid samples were collected from the patients,and 13 common respiratory pathogens were tested to analyze the distribution differences among different genders,ages,and seasons.Results Among 3091 pediatric patients,1794 were found to be infected with pathogens.The top three pathogens were Mycoplasma pneumoniae,rhinovirus,and respiratory syncytial virus(RSV).The single infection rate was 47.75％,while the mixed infection rate was 10.28％,with the most common scenario being a mixed infection of two pathogens.There were statistically significant differences in the pathogen profiles across different age groups(P＜0.001):infants had the highest detection rate of RSV,young children were primarily infected with rhinovirus,preschool and school-age children were predominantly infected with Mycoplasma pneumoniae.Seasonal distribution showed that the highest positive rate was in autumn,while the lowest was in spring(P＜0.05).In spring,the main pathogens were rhinovirus and Mycoplasma pneumoniae;in summer,they were rhinovirus and parainfluenza virus;in autumn,they were Mycoplasma pneumoniae and RSV;and in winter,the detection rates of Mycoplasma pneumoniae and influenza B virus were higher.Conclusion From 2020 to 2022,Mycoplasma pneumoniae,rhinovirus and RSV were the main pathogens of children's respiratory tract infection in Zibo area,and there were significant differences in the distribution of pathogens among different ages and seasons.

Objective To identify the key factors affecting the risk of medical surges and their coupling relation5 ships,providing strategic support for medical institutions to optimize risk management and emergency governance.Methods 17 influencing factors were determined based on WSR theory,and an expert scoring method was employed to assess the impact strength among the factors.The DEMATEL method was applied to calculate the centrality,cau5 sality,influence,and being influenced degrees of the influencing factors.The ISM method was used to construct a hierarchical structure of the influencing factors related to medical surge risks,thereby revealing the connections and interaction mechanisms among these factors.Results Seven critical influencing factors were identified,including the crisis decision-making capacity and leadership effectiveness of emergency managers,the completeness of the emer5 gency system and dynamic execution capabilities,and the cross-departmental coordination mechanism and com5 mand collaboration efficiency.Deep driving factors and coupling pathways were also revealed.Conclusion The risk of medical surges exhibits multi-factorial coupling cascade effects;attention should be directed towards the construc5 tion of mid-to-deep level mechanisms such as information systems,institutional frameworks,and organizational management,to enhance targeted capabilities and systemic resilience in risk governance.

Objective Exploring the components of complex scenarios of healthcare surges triggered by major epidemics to provide a theorical basis for building resilience in healthcare organizations.Methods A hybrid analysis method is used to summarize macro-meso-micro multi-level and multi-source heterogeneous information,extract the elements of complex scenarios of medical surge and evaluate the rationality.Fault Tree Analysis method is used to clarify the logical relationship between various scenario elements and construct scenario reasoning paths.Results 10 scenario states,11 disaster-bearing,24 emergency management and 23 scenario results are summarized and extracted to form the key elements of complex surge scenarios.Among them,M4 expansion and coordinated scheduling of key positions,B2 conventional drug inventory emergency/insufficient core treatment drugs,B emergency medical material transportation breakage,S3 disease symptom spectrum shift to severe disease,R13 prevention and control awareness laxity,and M5 media information dissemination management are the key driving factors that promote a major turning point in the scenario.The most positive scenario result is the orderly operation of the medical service system,and the most negative scenario result is the paralysis of the medical service system.Conclusion Medical institutions need to improve emergency plans based on the complex evolution scenarios of medical surges and agile governance capabilities targeting key turning points,focus on dynamically expanding and scheduling personnel in key positions,strengthen material rotation and reserve mechanisms,maintain smooth emergency logistics channels,and improve efficient management of media and public opinion,so as to comprehensively improve overall resilience.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.