ABSTRACT
Glioblastoma Multiforme [GBM] or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. Several of the molecular alterations detected in gliomas may have diagnostic and/or prognostic implications. Proteomics has been widely applied in various areas of science, ranging from the deciphering of molecular pathogen nests of discuses. In this study proteins were extracted from the tumor and normal brain tissues and then the protein purity was evaluated by Bradford test and spectrophotometry. In this study, proteins were separated by 2-Dimensional Gel [2DG] electrophoresis method and the spots were then analyzed and compared using statistical data and specific software. Protein clustering analysis was performed on the list of proteins deemed significantly altered in glioblastoma tumors [t-test and one-way ANOVA; P< 0.05]. The 2D gel showed totally 876 spots. We reported, 172 spots were exhibited differently in expression level [fold > 2] for glioblastoma. On each analytical 2D gel, an average of 876 spots was observed. In this study, 188 spots exhibited up regulation of expression level, whereas the remaining 232 spots were decreased in glioblastoma tumor relative to normal tissue. Results demonstrate that functional clustering [up and down regulated] and Principal Component Analysis [PCA] has considerable merits in aiding the interpretation of proteomic data. 2D gel electrophoresis is the core of proteomics which permitted the separation of thousands of proteins. High resolution 2DE can resolve up to 5,000 proteins simultaneously. Using cluster analysis, we can also form groups of related variables, similar to what is practiced in factor analysis
ABSTRACT
Dihydropyrimidinase Related Proteins [DRPs] have known homologous to the Collapsing Response Mediator Proteins [CRMPs]. The DRP gene family has comprised four members, DRP 1, 2, 3, and 4, all out of which have considered to be involved in axonal outgrowth and path-finding. The protein has extracted from tumor, normal brain tissues, and then the protein purity has evaluated by Bradford test and spectrophotometric methods. In this study, proteins has separated by Two-Dimensional Gel [2DG] electrophoresis method and then spots have analyzed and compared using statistical data and specific software [Progenesis Same Spots].Spots have identified by pH isoelectric, molecular weights and data banks. The 2D gel has shown 800 spots totally. Two spots have reported for DRP2, and one spot has reported for DRP3 in the human brain proteome, that have differed in pH isoelectric, and Molecular Weights values. This protein family has involved in neuronal differentiation and axonal guidance, and abundantly influenced in the developing brain, but their expression persisted into adulthood. DRP2 has regulated by phosphorylation, Glycogen synthase kinase 3, regulate phosphorylation of DRP2 an inactive from, and induced neuronal polarity
ABSTRACT
Evaluation of depth of anesthesia is especially important in adequate and efficient management of patients. Clinical assessment of EEG in the operating room is one of the major difficulties in this field. This study aims to find the most valuable EEG parameters in prediction of the depth of anesthesia in different stages. EEG data of 30 patients with same anesthesia protocol [total intravenous anesthesia] were recorded in all anesthetic stages in Shohada-e-Tajrish Hospital. Quantitative EEG characteristics are classified into 4 categories of time, frequency, bispectral and entropy-based characteristics. Their sensitivity, specificity and accuracy in determination of depth of anesthesia were yielded by comparing them with the recorded reference signals in awake, light anesthesia, deep anesthesia and brain dead patients. Time parameters had low accuracy in prediction of the depth of anesthesia. The accuracy rate was 75% for burst suppression response. This value was higher for frequency- based characteristics and the best results were obtained in beta spectral power [accuracy: 88.9%]. The accuracy rate was 89.9% for synch fast slow bispectral characteristics. The best results were obtained from entropy-based characteristics with the accuracy of 99.8%. Analysis of the entropy-based characteristics had a great value in predicting the depth of anesthesia. Generally, due to the low accuracy of each single parameter in prediction of the depth of anesthesia, we recommend multiple characteristics analysis with greater focus on entropy-based characteristics