Objective:To analyze the prevalence and clinical features of respiratory syncytial virus (RSV) in Chengde city.Methods:From August 2022 to June 2023, throat swabs and clinical data of 478 hospitalized children with respiratory tract infection in the Chengde Central Hospital were collected. Real-time quantitative PCR was used to detect the molecular epidemiology of RSV-A and RSV-B subtypes and analyze the clinical features of patients with RSV infection.Results:Among the hospitalized children, 67.57% (323/478) tested positive for RSV. The outbreak of RSV infection was caused by RSV-A subtype. The peaks of RSV-A infection occurred from November to December, 2022 and May to June, 2023. There were 86.07% (278/323) of the RSV-A-positive cases had mixed infection with other pathogens, primarily bacterial pathogens with Streptococcus pneumoniae being the most common, followed by Klebsiella pneumoniae. Influenza virus A was the most common viral pathogens causing mixed infection. The level of lactate dehydrogenase was higher in the patients with single RSV-A infection than in those with mixed infection ( Z=2.396, P=0.017), and higher than the normal upper limit. Compared with the single infection group, the mixed infection group had higher white blood cell count ( Z=2.417, P=0.016), neutrophil ratio ( Z=3.218, P=0.001), C-reactive protein level ( Z=1.998, P=0.046) and creatinine level ( Z=2.107, P=0.035), and lower lymphocyte ratio ( Z=3.205, P=0.001), but they were all within the normal range. There were no significant differences in the clinical features between RSV-A-positive patients co-infected with bacteria or other viruses (all P>0.05). Conclusions:RSV-A is the leading cause of respiratory tract infection in children in Chengde from 2022 to 2023, and often co-detected with bacteria. The mixed infection with other respiratory pathogens is related to the clinical features of patients with RSV-A infection.

Objective To investigate the association between metabolic associated fatty liver disease(MAFLD)and carotid atherosclerotic plaque.Methods A total of 1 107 patients who were hospitalized in The Second Affiliated Hospital of Kunming Medical University from July,2014 to December,2022 were enrolled,and all patients underwent abdominal ultrasound and CT angiography of the head and neck arteries.Baseline data and clinical diagnosis were collected,and the patients were divided into MAFLD group with 499 patients and non-MAFLD group with 608 patients based on medical history,clinical tests,and imaging findings.According to the CT value,carotid plaques were classified into calcified plaques,non-calcified plaques,and mixed plaques.According to the NASCET criteria,carotid stenosis was categorized as normal vessel,slight stenosis,mild stenosis,moderate stenosis,and severe stenosis/occlusion.The independent-samples t test was used for comparison of normally distributed continuous data between two groups,and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data between two groups;the chi-square test was used for comparison of categorical data between two groups.Univariate and multivariate Logistic regression analyses were used to investigate the influencing factors for carotid atherosclerosis.Results Compared with the non-MAFLD group,the MAFLD group had a significantly higher proportion of patients with calcified plaques(74.3%vs 63.3%,P<0.05),non-calcified plaques(27.1%vs 17.1%,P<0.05),or mixed plaques(27.3%vs 20.7%,P<0.05),as well as a significantly higher proportion of patients with mild stenosis(50.9%vs 44.9%,P<0.05),moderate stenosis(14.6%vs 8.4%,P<0.05),or severe stenosis/occlusion(6.6%vs 3.5%,P<0.05).The univariate logistic regression analysis showed that MAFLD was a risk factor for calcified carotid plaques,non-calcified plaques,and mixed plaques,and it was also a risk factor for mild stenosis,moderate stenosis,and severe stenosis/occlusion of the carotid artery(all P<0.05).After adjustment for confounding factors,the multivariate Logistic regression analysis showed that MAFLD was an independent risk factor for calcified plaque,non-calcified plaque,mixed plaque,and moderate stenosis of the carotid arteries(all P<0.05).Conclusion MAFLD is an independent risk factor for moderate stenosis,calcified plaques,non-calcified plaques,and mixed plaques of the carotid arteries.

Objective:To investigate the mediating effect of social interaction on the relationship between internet use and the health status of the elderly.The findings of this research can potentially contribute new insights into improving the health status of the elderly population.Methods:Based on the data from the Chinese General Social Survey(CGSS)in 2021, a total of 2 675 elderly individuals aged 60 and above were included in the study.The researchers analyzed the linear regression relationship between Internet use, social interaction, and the health status of the elderly using a hierarchical regression model.Additionally, a mediation effect test was conducted using the Bootstrap test.Furthermore, heterogeneity analysis of the mediation effect was performed considering factors such as gender, place of residence, working conditions, and physical exercise among the elderly participants.Results:The total effect value was 0.170.The direct effect of Internet use on the health status of older adults was 0.160(95% CI: 0.078-0.240), accounting for 93.66% of the effect value.The indirect effect of social interaction was 0.011(95% CI: 0.003-0.021), accounting for 6.34% of the effect value.The mediating effect of social interaction on the health status of older adults varied among different groups.Among female elderly, rural elderly, elderly who are currently unemployed, and those who are physically active, the mediating effects were 5.16%, 7.86%, 10.18%, and 9.91%, respectively. Conclusions:The impact of internet use and social interaction on the health status of the elderly is notably positive.Additionally, social interaction partially mediates the relationship between internet use and the health status of the elderly.

Objective:Taking the main international collaborative innovation network management modes for clinical research as a reference, to promote the construction of a collaborative innovation network for clinical research in China.Methods:Using literature research, case study, comparative analysis, and interview methods, this study systematically studied the collaborative innovation network management modes of clinical research in the United States, the United Kingdom, Japan, South Korea, and China from the perspective of integrating and sharing clinical research resources.Results:Based on the comparative results and drawing on foreign experience, suggestions were proposed to optimize the management mode of the clinical research collaborative innovation network in China and promote the construction of clinical research collaborative innovation network.Conclusions:It is recommended to establish national medical research institutions, increase investment in clinical research funds, build a platform for integrating volunteer resources based on clinical research collaborative innovation networks, and establish a clinical sample resource service platform to further supplement and improve the structure and layout of China’s clinical research collaborative innovation network.

Objective:To screen the main characteristic variables affecting the incidence of cardiovascular and cerebrovascular diseases,and to construct the Bayesian network model of cardiovascular and cerebrovascular disease incidence risk based on the top 10 characteristic variables,and to provide the reference for predicting the risk of cardiovascular and cerebrovascular disease incidence.Methods:From the UK Biobank Database,315 896 participants and related variables were included.The feature selection was performed by categorical boosting(CatBoost)algorithm,and the participants were randomly divided into training set and test set in the ratio of 7∶3.A Bayesian network model was constructed based on the max-min hill-climbing(MMHC)algorithm.Results:The prevalence of cardiovascular and cerebrovascular diseases in this study was 28.8％.The top 10 variables selected by the CatBoost algorithm were age,body mass index(BMI),low-density lipoprotein cholesterol(LDL-C),total cholesterol(TC),the triglyceride-glucose(TyG)index,family history,apolipoprotein A/B ratio,high-density lipoprotein cholesterol(HDL-C),smoking status,and gender.The area under the receiver operating characteristic(ROC)curve(AUC)for the CatBoost training set model was 0.770,and the model accuracy was 0.764;the AUC of validation set model was 0.759 and the model accuracy was 0.763.The clinical efficacy analysis results showed that the threshold range for the training set was 0.06-0.85 and the threshold range for the validation set was 0.09-0.81.The Bayesian network model analysis results indicated that age,gender,smoking status,family history,BMI,and apolipoprotein A/B ratio were directly related to the incidence of cardiovascular and cerebrovascular diseases and they were the significant risk factors.TyG index,HDL-C,LDL-C,and TC indirectly affect the risk of cardiovascular and cerebrovascular diseases through their impact on BMI and apolipoprotein A/B ratio.Conclusion:Controlling BMI,apolipoprotein A/B ratio,and smoking behavior can reduce the incidence risk of cardiovascular and cerebrovascular diseases.The Bayesian network model can be used to predict the risk of cardiovascular and cerebrovascular disease incidence.

Objective To screen differentially expressed genes in lung adenocarcinoma by constructing Lasso-Cox model to provide potential gene targets for the research of lung adenocarcinoma and new directions for clinical diagnosis,treatment and prognosis by calculating patient risk score and constructing prediction model of lung adenocarcinoma.Methods The gene expression and clinical data of lung adenocarcinoma were downloaded from the Cancer Genome Atlas(TCGA)and Gene Expression Omnibus database(GEO).The TCGA database was used to train model,and the two databases were combined for model validation.The screened differentially expressed genes(DEGs)of lung adenocarcinoma were analyzed by univariate Cox and multivariate Lasso-Cox to construct a risk score prognosis model.Risk score from the final Cox prediction model and clinical data were combined to determine independent prognostic factors.GO enrichment analysis,KEGG pathway analysis and CIBERSORTx immunoassay were used to evaluate the biological interpretation of differentially expressed genes in the risk model.Results The analysis using univarate Cox and Lasso-Cox regreesion identified 9 differentially expressed genes associated with the prognosis of lung adenocarcinoma.Multivariate Cox regression analysis,incorporating clinical data,revealed that a history of malignant tumors,N stage,T stage,and the risk score were independent prognostic factors.Conclusion The prognositic model of lung adenocarcinoma can effectively predict the prognosis risk and provide a theoretical basis for clinical decision-making and personalized treatment.

Objectives To explore the causal relationship between hypothyroidism and nonalcoholic fatty liver disease(NAFLD)by two-sample Mendelian randomization.Methods Genome-wide association study(GWAS)data was used to identify genetic variation data associated with hypothyroidism as instrumental variables(IVs).MR-Egger regression,inverse variance weighting method(IVW)and weighted median method(WME)were used to examine the causal effects between hypothyroidism and NAFLD.The causal association was evaluated with OR.Results A total of 334 SNP loci were included as Ⅳ,and there was no heterogeneity and pleiotropy in the Ivs.Based on IVW estimation mryhod,the OR with 95%CI of two different sets were 21.30(95%CI:93.61～125.50)]and 5.79(95%CI:1.03～32.47),which both indicated a significant causal association between the hypothyroidism and NAFLD(P＜0.05).What′s more,the results of Mendelian randomization were robust.Conclusion Hypothyroidism is potentially associated with NAFLD.

Objective To explore the prognostic factors of colorectal cancer patients in gene data using random survival forest model.Method The differentially expressed genes were screened using the gene expression data of colorectal cancer in TCGA database,and combined with clinical and survival information.The RSF model is constructed and compared with the traditional Lasso-Cox regression model.Results The RSF model obtained 13 important factors affecting the prognosis of colorectal cancer patients,including HAND1(VIMP=0.090)and PCOLCE2(VIMP=0.075)genes,and analyzed the interaction between pathological N,PCOLCE2 gene and IGSF9 gene variables.Compared with Lasso-Cox model,the RSF model has better model calibration(IBS:training set:0.205 vs.0.214;test set:0.210 vs.0.221)although its prediction error rate is slightly higher(1-C-index:training set:0.296 vs.0.213;test set:0.369 vs.0.332).Conclusion RSF model has a good performance in processing the analysis of right censored survival data,can find important influencing factors and the interaction between variables,and provide scientific basis for the improvement of prognosis and quality of life of colorectal cancer patients.

Steatotic liver diseases (SLD) are the principal worldwide cause of cirrhosis and end-stage liver cancer, affecting nearly a quarter of the global population. SLD includes metabolic dysfunction-associated alcoholic liver disease (MetALD) and metabolic dysfunction-associated steatotic liver disease (MASLD), resulting in asymptomatic liver steatosis, fibrosis, cirrhosis and associated complications. The immune processes include gut dysbiosis, adiposeliver organ crosstalk, hepatocyte death and immune cell-mediated inflammatory processes. Notably, various immune cells such as B cells, plasma cells, dendritic cells, conventional CD4+ and CD8+ T cells, innate-like T cells, platelets, neutrophils and macrophages play vital roles in the development of MetALD and MASLD. Immunological modulations targeting hepatocyte death, inflammatory reactions and gut microbiome include N-acetylcysteine, selonsertib, F-652, prednisone, pentoxifylline, anakinra, JKB-121, HA35, obeticholic acid, probiotics, prebiotics, antibiotics and fecal microbiota transplantation. Understanding the immunological mechanisms underlying SLD is crucial for advancing clinical therapeutic strategies.

Objective To retrieve relevant literature on risk prediction model for methicillin-resistant Staphylococcus aureus (MRSA) infection among hospitalized patients from databases and evaluate the predictive model. Methods The literature on risk prediction models for MRSA infection among hospitalized patients was retrieved from PubMed, Embase, Scopus, Cochrane library, China National Knowledge Infrastructure (CNKI), WanFang data, and VIP database, with a time range from the inception of the database to January 1, 2024. Two researchers independently screened the literature, extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was applied to evaluate the risk of bias and applicability of the prediction model in the literature, and descriptive analysis was conducted. Results A total of 12 articles (15 prediction models) were included in this study, with significant differences in the total sample size, the number of MRSA infection events, sample size of modeling, and sample size of validation among the studies. Common predictors in the prediction models were admission to the intensive care unit, antibiotic use, history of residence in nursing facilities, age, chronic kidney disease, and previous hospitalization history. Nine articles conducted internal validation, and three articles conducted both internal and external validation. Nine articles reported the area under the receiver operating characteristic curve, and only three articles reported the calibration of the model based on the Hosmer-Lemeshow test. PROBAST analysis showed that 10 articles were assessed as high risk bias, mainly stemming from statistical analysis. Conclusion Most of the MRSA infection risk prediction models in the current literature have good predictive efficacy for MRSA infection, but they all have higher overall risk of bias, and only a few models have undergone external validation. Researchers should follow PROBAST standards to construct and externally validate models in the future so as to develop models suitable for clinical practice.