OBJECTIVE:To determine the content of ceftizoxime for injection METHODS:A RP-HPLC method was established,the polarls C18 was used as analysis column The mobile phase consisted of 0 23% sodium hydrogen phosphate and 0 14% trisodium buffer-acetonitrile(85∶15),the flow rate was 1 0ml/min,the detection wavelength was 254nm RESULTS:The linear range of ceftizoxime was 4 1 462～37 3 156?g/ml,r=0 9 999 The relative standard deviation was 0 6% CONCL_USION:The method is rapid,accurate and suitable for determination of content of ceftizoxime for injection

OBJECTIVETo explore the molecular mechanism in the formation of unstable plaques.

METHODSThe cDNA microarray E-MTAB-2055 was downloaded from ArrayExpress database to screen the differentially expressed genes in 24 ruptured plaques against 24 stable plaques. Functional enrichment analysis was conducted to define the biological processes and pathways involved in disease progression. The protein-protein interaction network was constructed to identify the risk modules with close interactions. Five pairs of carotid specimens were used to validate 3 differentially expressed genes of the risk modules by real-time PCR.

RESULTSA total of 439 genes showed differential expression in our analysis, including 232 up-regulated and 207 down-regulated genes according to the data filter criteria. Immune-related biological processes and pathways were greatly enriched. The protein-protein interaction network and module analysis suggested that TYROBP, VCL and CXCR4 might play critical roles in the development of unstable plaques, and differential expressions of CXCR4 and TYROBP in carotid plaques were confirmed by real-time PCR.

CONCLUSIONOur study shows the differential gene expression profile, potential biological processes and signaling pathways involved in the process of plaque rupture. TYROBP may be a new candidate disease gene in the pathogenesis of unstable plaques.

Adaptor Proteins, Signal Transducing ; genetics ; Disease Progression ; Down-Regulation ; Gene Expression Profiling ; Humans ; Membrane Proteins ; genetics ; Oligonucleotide Array Sequence Analysis ; Plaque, Atherosclerotic ; genetics ; Protein Interaction Maps ; Real-Time Polymerase Chain Reaction ; Receptors, CXCR4 ; genetics ; Transcriptome ; Up-Regulation ; Vinculin ; genetics

Objective To explore the molecular mechanism in the formation of unstable plaques. Methods The cDNA microarray E-MTAB-2055 was downloaded from ArrayExpress database to screen the differentially expressed genes in 24 ruptured plaques against 24 stable plaques. Functional enrichment analysis was conducted to define the biological processes and pathways involved in disease progression. The protein-protein interaction network was constructed to identify the risk modules with close interactions. Five pairs of carotid specimens were used to validate 3 differentially expressed genes of the risk modules by real-time PCR. Results A total of 439 genes showed differential expression in our analysis, including 232 up-regulated and 207 down-regulated genes according to the data filter criteria. Immune-related biological processes and pathways were greatly enriched. The protein-protein interaction network and module analysis suggested that TYROBP, VCL and CXCR4 might play critical roles in the development of unstable plaques, and differential expressions of CXCR4 and TYROBP in carotid plaques were confirmed by real-time PCR. Conclusion Our study shows the differential gene expression profile, potential biological processes and signaling pathways involved in the process of plaque rupture. TYROBP may be a new candidate disease gene in the pathogenesis of unstable plaques.

Objective To explore the molecular mechanism in the formation of unstable plaques. Methods The cDNA microarray E-MTAB-2055 was downloaded from ArrayExpress database to screen the differentially expressed genes in 24 ruptured plaques against 24 stable plaques. Functional enrichment analysis was conducted to define the biological processes and pathways involved in disease progression. The protein-protein interaction network was constructed to identify the risk modules with close interactions. Five pairs of carotid specimens were used to validate 3 differentially expressed genes of the risk modules by real-time PCR. Results A total of 439 genes showed differential expression in our analysis, including 232 up-regulated and 207 down-regulated genes according to the data filter criteria. Immune-related biological processes and pathways were greatly enriched. The protein-protein interaction network and module analysis suggested that TYROBP, VCL and CXCR4 might play critical roles in the development of unstable plaques, and differential expressions of CXCR4 and TYROBP in carotid plaques were confirmed by real-time PCR. Conclusion Our study shows the differential gene expression profile, potential biological processes and signaling pathways involved in the process of plaque rupture. TYROBP may be a new candidate disease gene in the pathogenesis of unstable plaques.

Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern ( wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.
Brain-Computer Interfaces ; Imagination ; Signal Processing, Computer-Assisted ; Electroencephalography/methods* ; Algorithms ; Spectrum Analysis