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.

Purpose To study the relationshiPbetween CD88 expression and clinicopathologic features and epithelial-mesenchymal transition(EMT)in esophageal squamous cell carcinoma.Methods TCGA and TIMER database were used to analyze the expression level of CD88 in esophageal squamous cell carcinoma and adjaecnt esophageal squamous cell epithelium and its relationship with epithelial-mesenchymal transition.Par-affin specimens were collected from 199 patients with clinically diagnosed esophageal squamous cell carcinoma.Immunohisto-chemical EnVision method was used to detect the expression of CD88 and EMT-related proteins in esophageal squamous cell carcinoma and adjacent tissues,the relationship between CD88 expression and clinicopathological features,prognosis and EMT in ESCC tumors was analyzed.Results There were 86 cases with high CD88 expression and 113 cases with low CD88 expres-sion.The expression level of CD88 in esophageal squamous cell carcinoma was significantly higher than that of paracancerous tis-sue(P＜0.001).The group with high CD88 expression had lower ESCC differentiation level(P＜0.001)and higher T stage(P=0.03).The 5-year survival of patients with high CD88 ex-pression was significantly lower than that of patients with low CD88 expression(P=0.002).Cox univariate and multivariate analysis showed that CD88 expression was an independent prog-nostic factor for overall survival of patients with esophageal squa-mous cell carcinoma(P=0.013).The high expression of CD88 was negatively correlated with E-cadherin(r=-0.146,P=0.039),and positively correlated with vimentin(r=0.387,P=1.61e-08)and N-cadherin(r=0.304,P=1.3e-05).Con-clusion CD88 is highly expressed in esophageal squamous cell carcinoma.CD88 may affect the occurrence,development,in-vasion and metastasis of esophageal squamous cell carcinoma through EMT,and it might be used as a prognostic marker for e-sophageal squamous cell carcinoma patients.

Despite decades of laboratory and clinical trials,breast cancer remains the main cause of cancer-related disease burden in women.Considering the metabolism destruction effect of metformin(Met)and cancer cell starvation induced by glucose oxidase(GOx),after their efficient delivery to tumor sites,GOx and Met may consume a large amount of glucose and produce sufficient hydrogen peroxide in situ.Herein,a pH-responsive epigallocatechin gallate(EGCG)-conjugated low-molecular-weight chitosan(LC-EGCG,LE)nanoparticle(Met-GOx/Fe@LE NPs)was constructed.The coordination between iron ions(Fe3+)and EGCG in this nanoplatform can enhance the efficacy of chemodynamic therapy via the Fenton reaction.Met-GOx/Fe@LE NPs allow GOx to retain its enzymatic activity while simultaneously improving its stability.Moreover,this pH-responsive nanoplatform presents controllable drug release behavior.An in vivo biodistribution study showed that the intracranial accumulation of GOx delivered by this nano-platform was 3.6-fold higher than that of the free drug.The in vivo anticancer results indicated that this metabolism destruction/starvation/chemodynamic triple-combination therapy could induce increased apoptosis/death of tumor cells and reduce their proliferation.This triple-combination therapy approach is promising for efficient and targeted cancer treatment.