Objective:To analyze the key β-amyloid protein (Aβ) deposition in brain regions affecting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).Methods:Based on the positron emission tomography data of Aβ in the Alzheimer's disease neuroimaging initiative database, the penalized generalized estimating equation (PGEE) and the mixed effects regression forest algorithm (MERF) were used to conduct dimensionality reduction analysis on 164 brain regions with Aβ deposition. Additionally, a multivariate longitudinal data joint model was used to screen the key Aβ deposition brain regions that influence the progression from MCI to AD.Results:Five key brain regions were commonly screened out by the PGEE and MERF models, they were the right prefrontal orbital cortex, the left superior temporal sulcus shore cortex, the right medial orbitofrontal cortex, the left putamen, and the right transverse temporal cortex, respectively. The results of the multivariate longitudinal data joint model based on these 5 Aβ deposition brain regions showed that, except the left superior temporal sulcus shore cortex, the longitudinal change trajectories of the other 4 Aβ deposition brain regions all affected the progression from MCI to AD ( P<0.05). Conclusion:The Aβ deposition in the right prefrontal orbital cortex, right medial orbitofrontal cortex, left putamen and right transverse temporal cortex affect the progression from MCI to AD.

Objective:To explore the causal association between insulin-like growth factor-1(IGF-1)and colorectal cancer(CRC)based on two sample Mendelian randomization(MR)analysis.Methods:A bidirectional two sample MR analysis was conducted based on publicly aggregated data from the IEU OpenGWAS project.The inverse variance weighted(IVW)method was used as the main analysis model to assess the causal relationship between IGF-1 and CRC.Additional analyses were performed using weighted median(WM),MR-Egger regression,weighted mode estimator(WME),and simple mode(SM)methods.Sensitivity analysis was performed to assess the robustness of the results.Results:A total of 386 single nucleotide polymorphisms(SNPs)were selected as instrumental variables(IVs)with IGF-1 as the exposure factor.The MR analysis results revealed a positive causal association between IGF-1 and the risk of CRC[odds ratio(OR)=1.178,95％confidence interval(CI):1.092-1.272)](P＜0.001),and the association remained significant after adjusting for height[OR(95％CI)=1.214(1.111,1.327)](P＜0.001).Cochran's Q-test showed heterogeneity among the IVs(P＜0.05),while the horizontal pleiotropy of IV was not detected by the MR-Egger regression(P＞0.05).The leave-one-out analysis showed that the MR results were robust.Reverse MR analysis indicated no reverse causal relationship between IGF-1 and CRC[OR(95％CI):1.017(0.997,1.037)](P=0.103).Conclusion:There is a causal relationship between IGF-1 level and CRC,and elevated IGF-1 level could be a risk factor for CRC.

Objective:To discuss the influencing factors of angina pectoris in the patients with diabetes mellitus(DM),to construct a Bayesian network model to explore the network relationships among the influencing factors,and to predict the risk of angina pectoris in the patients with DM.Methods:Based on the UK Biobank(UKB)database,the Logistic regression aralysis model was used to screen the influencing factors of angina pectoris in the patients with DM.The taboo search algorithm was used for structure learning,and the Bayesian parameter estimation method was used for parameter learning to construct the Bayesian network model.Results:A total of 22 712 DM patients were included.The influencing factors of angina pectoris in the patients with DM included 14 variables:gender,age,body mass index(BMI),triglycerides(TG),total cholesterol(TC),glycated hemoglobin(HbA1c),hypertension,maternal smoking around delivery,smoking status,alcohol consumption,regular exercise,insomnia,sleep duration,and childhood relative body size(P<0.05).A Bayesian network model was constructed with 15 nodes and 22 directed edges.Among them,age,HbA1c,hypertension,regular exercise,BMI,and sleep duration were directly associated with the occurrence of angina pectoris in the patients with DM,while gender,smoking status,alcohol consumption,TC,TG,insomnia,childhood relative body size,and maternal smoking around delivery were indirectly associated with the occurrence of angina pectoris in the patients with DM.Conclusion:Age,HbA1c,hypertension,regular exercise,BMI,and sleep duration are direct influencing factors of angina pectoris in the patients with DM.Controlling HbA1c,blood pressure,and BMI levels,engaging in regular exercise,and maintaining appropriate sleep duration are beneficial for reducing the risk of angina pectoris in the patients with DM.

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.

Objective:To analyze the key β-amyloid protein (Aβ) deposition in brain regions affecting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).Methods:Based on the positron emission tomography data of Aβ in the Alzheimer's disease neuroimaging initiative database, the penalized generalized estimating equation (PGEE) and the mixed effects regression forest algorithm (MERF) were used to conduct dimensionality reduction analysis on 164 brain regions with Aβ deposition. Additionally, a multivariate longitudinal data joint model was used to screen the key Aβ deposition brain regions that influence the progression from MCI to AD.Results:Five key brain regions were commonly screened out by the PGEE and MERF models, they were the right prefrontal orbital cortex, the left superior temporal sulcus shore cortex, the right medial orbitofrontal cortex, the left putamen, and the right transverse temporal cortex, respectively. The results of the multivariate longitudinal data joint model based on these 5 Aβ deposition brain regions showed that, except the left superior temporal sulcus shore cortex, the longitudinal change trajectories of the other 4 Aβ deposition brain regions all affected the progression from MCI to AD ( P<0.05). Conclusion:The Aβ deposition in the right prefrontal orbital cortex, right medial orbitofrontal cortex, left putamen and right transverse temporal cortex affect the progression from MCI to AD.

Objective To investigate the risk of chronic obstructive pulmonary disease(COPD)after asthma and explore factors influen-cing the onset and progression of asthma in patients with COPD.Methods A follow-up cohort was established based on the United Kingdom Biobank(UKB)database.The risk of asthma and COPD was predicted,and the influencing factors were analyzed using a mul-tistate model(MSM).Results Without considering the influence of covariates,the cumulative risk from COPD to mortality was the highest,followed by asthma to COPD,and asthma to mortality.Advanced age,male,diabetes mellitus(DM),high waist-to-hip ratio,hyper-tension,increased Townsend deprivation index,increased frequency of smoking,and family history were risk factors for developing COPD in the asthmatic population.Advanced age,male,DM,high waist-to-hip ratio,hypertension,increased Townsend deprivation index,and in creased frequency of smoking were risk factors for mortality in the asthmatic population.Advanced age,male,and DM and increased Townsend deprivation index were risk factors for mortality in the COPD population.Conclusion Advanced age,male,DM,high waist-to-hip ratio,hypertension,increased Townsend deprivation index,increased smoking frequency,and family history increased the risk of COPD in the asthmatic population.This MSM can be used to predict the influencing factors and degree of COPD after asthma,and reveal the change law of disease progression.

Objective To investigate the risk of chronic obstructive pulmonary disease(COPD)after asthma and explore factors influen-cing the onset and progression of asthma in patients with COPD.Methods A follow-up cohort was established based on the United Kingdom Biobank(UKB)database.The risk of asthma and COPD was predicted,and the influencing factors were analyzed using a mul-tistate model(MSM).Results Without considering the influence of covariates,the cumulative risk from COPD to mortality was the highest,followed by asthma to COPD,and asthma to mortality.Advanced age,male,diabetes mellitus(DM),high waist-to-hip ratio,hyper-tension,increased Townsend deprivation index,increased frequency of smoking,and family history were risk factors for developing COPD in the asthmatic population.Advanced age,male,DM,high waist-to-hip ratio,hypertension,increased Townsend deprivation index,and in creased frequency of smoking were risk factors for mortality in the asthmatic population.Advanced age,male,and DM and increased Townsend deprivation index were risk factors for mortality in the COPD population.Conclusion Advanced age,male,DM,high waist-to-hip ratio,hypertension,increased Townsend deprivation index,increased smoking frequency,and family history increased the risk of COPD in the asthmatic population.This MSM can be used to predict the influencing factors and degree of COPD after asthma,and reveal the change law of disease progression.

Objective To investigate the effect of lidocaine on the chemotherapy sensitivity of gastric cancer cells by regulating the Wnt/β-catenin axis. Methods Human gastric cancer cells SGC-7901 in logarithmic growth phase were inoculated into 96-well plates and treated with different concentrations of lidocaine (0, 10, 50, 100, 150, 200 μmol/L) for 24 h. The cell viability at different concentrations was compared. The SGC-7901 cells in logarithmic growth phase were divided into control group, cisplatin group, low concentration lidocaine group (Lido-L group), medium concentration lidocaine group (Lido-M group), high concentration lidocaine group (Lido-H group), high concentration lidocaine + Wnt/β-catenin signal pathway activator SKL2001 group (Lido-H+SKL2001 group). The cell proliferation, invasion, and migration abilities of each group were compared by 5-acetylidene-2'deoxyuracil nucleoside (EdU) cell proliferation detection, Transwell assay, and scratch healing experiment. The apoptosis of each group was detected by TUNEL assay. The expressions of apoptosis, epithelial-mesenchymal transition, and Wnt/β-catenin pathway-related proteins in each group were detected. Results Compared with 0 μmol/L lidocaine, the cell viability of SGC-7901 cells treated with 50, 100, 150, and 200 μmol/L lidocaine was reduced (P < 0.05). Compared with the control group, the EdU positive rate, the number of cell invasion, scratch healing rate, and expressions of vimentin, N-cadherin, B-cell lymphoma-associated protein-2 (Bcl-2), cyclin D1, scattered protein 2 (DVL2), Wnt family member 3a (Wnt3a), β-catenin (β-catenin) were reduced in the cisplatin group, while the cell apoptosis rate was increased, the expressions of E-cadherin, B-cell lymphoma-2-associated X protein (Bax), Cleaved-caspase-3 were increased, and the differences were statistically significant (P < 0.05). Compared with the cisplatin group, the EdU positive rate, the number of cell invasion, scratch healing rate, and expressions of vimentin, N-cadherin, Bcl-2, cyclin Dl, DVL2, Wnt3a, β-catenin were gradually reduced in the Lido-L group, Lido-M group, and Lido-H group, while the cell apoptosis rate, the expressions of E-cadherin, B-cell lymphoma-2-associated X protein (Bax), Cleaved-caspase-3 were increased (P < 0.05). Compared with the Lido-H group, the EdU positive rate, the number of cell invasion, scratch healing rate, and expressions of vimentin, N-cadherin, Bcl-2, cyclin Dl, DVL2, Wnt3a, β-catenin were increased in the Lido-H+SKL2001 group, while the cell apoptosis rate and expressions of E-cadherin, Bax, Cleaved-caspase-3 were reduced, the differences were statistically significant (P < 0.05). Conclusion Lidocaine may enhance the chemotherapy sensitivity of gastric cancer cells by inhibiting the activation of the Wnt/β-catenin axis.

Objective:Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease.However,we still don't fully understand how certain immune-related genes contribute to the disease's development and progression.This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis(WGCNA)and machine learning. Methods:This study downloaded the gene chip data from the Gene Expression Omnibus(GEO)database,and used WGCNA to screen out important gene modules related to Parkinson's disease.Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease.Gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)were used to analyze the the functions of immune-related genes and signaling pathways involved.Immune cell infiltration analysis was performed using the CIBERSORT package of R language.Using bioinformatics method and 3 machine learning methods[least absolute shrinkage and selection operator(LASSO)regression,random forest(RF),and support vector machine(SVM)],the immune-related genes of Parkinson's disease were further screened.A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes(hub)gene.The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn. Results:A total of 218 immune genes related to Parkinson's disease were identified,including 45 upregulated genes and 50 downregulated genes.Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways.The results of immune infiltration analysis showed that the infiltration percentages of CD4+ T cells,NK cells,CD8+ T cells,and B cells were higher in the samples of Parkinson's disease patients,while resting NK cells and resting CD4+ T cells were significantly infiltrated in the samples of Parkinson's disease patients.ANK1 was screened out as the hub gene.The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction,iron homeostasis regulation,and immune system activation. Conclusion:This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods,suggesting its potential as a candidate therapeutic target for Parkinson's disease.

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.