Objective Using the weighted gene co-expression network analysis(WGCNA)to explore the key genes of aging associated with Alzheimer's disease(AD).Methods GSE132903 was selected from GEO database as the analysis dataset.The differential expressed genes(DEGs)of AD were screened,and visualized with volcano and heat map.Aging and senescence-associated genes(ASAGs)were downloaded from MsigDB,Aging Altas and CellAge databases.WGCNA screened the gene modules with the highest correlation with AD,and genes of key modules subsequently performed with gene ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis.AD age-related differential expressed genes(ARDEGs)were obtained by taking intersection genes of DEGs,key module genes of WGCNA and ASAGs.Protein-protein interaction(PPI)network analysis was performed using the STRING database to find key node genes.The co-expression networks and associated functions of key genes were analyzed using the GeneMANIA database.The key genes were validated in Alzdata database.Results 226 DEGs,606 ASAGs and 8 ARDEGs were obtained.The top 5 key genes selected by PPI were SYP,STXBP1,VAMP2,CPLX1 and STX1A.Alzdata database verified that the expressions of 5 key genes in other brain regions of AD were down-regulated,except for no significant changes of VAMP2 in hippocampus and STXBP1 in frontal cortex,as well as no expression of CPLX1 in frontal cortex.The differential expression of VAMP2,STXBP1 and STX1A appeared in the early stage of AD,and CPLX1 was related to the pathological process of Tau.SYP and STXBP1 were related to the pathological processes of amyloid β-protein and Tau.Conclusion SYP,STXBP1,VAMP2,CPLX1 and STX1A are ARDEGs,which are expected to be potential diagnostic and therapeutic targets for AD.

Objective To design a pH-sensitive nintedanib liposomes(Nb-Lips)which targeted the acidic microenvironment of pulmonary fibrosis.The entrapment efficiency(EE%)was optimized by the formulation process.Methods Nintedanib liposomes were prepared by membrane hydration method,and the formulation of nintedanib liposomes were optimized by single factor experiments and response surface method(RSM).The particle size,polymer dispersity index(PDI),Zeta potential and encapsulation rate was investigated by dynamic light scattering technique and microcolumn centrifugation method.The release behavior of nintedanib liposomes in artificial lung fluid with pH 7.4 and artificial lung fluid with pH 5.3 was investigated by dialysis method.Nintedanib liposomes were atomized with a compressed air atomizer and its atomization stability and aerodynamic particle size were investigated.Results The particle size of nintedanib liposomes was(100.651±7.315)nm,the PDI was(0.328±0.026),the zeta potential was(21.633±2.004)mV,and the encapsulation rate was higher than 80%.Compared with nintedanib solution at pH 7.4,the total release of nintedanib liposomes in pH 5.3 artificial lung solution was 60.78%higher,and the release of nintedanib liposomes in pH 5.3 artificial lung solution was 48h delayed,which was much higher than that of nintedanib solution.The data reveals no significant differences in particle size,potential and PDI before and after atomization of nintedanib liposomes,and the encapsulation rate decreased 4.25%.The fine particle fraction of the atomized droplets was 37.49%.Conclusion The response surface method can effectively improve the encapsulation rate of nintedanib liposomes,and successfully prepare nintedanib liposomes that are sensitive to acidic environment,and can be inhaled and released slowly.

This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.
Algorithms ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted