Aim: The importance of changes about platelet emphasized in most chronically diseases in recent years. Mean platelet volume (MPV) and platelet count can be used as a prognostic biomarker. In this study, clinical importance of the changes of MPV during active and remission phases in children with nephrotic syndrome was investigated. Patients and methods: Fifty-five children with nephrotic syndrome (30 females, 25 males) and 29 healthy children (18 females, 11 males) were included to the study. Patients were divided in two groups (steroid sensitive nephrotic syndrome and focal segmental glomerulosclerosis). Demographic characteristics of the patients, type of nephrotic syndrome were recorded and laboratory parameters in active and remission phases were evaluated. Results: Mean platelet count in the patient group was significantly higher than control group. Mean platelet count of FSGS group was significantly higher than SSNS group. Mean MPV value was significantly lower in active period of nephrotic syndrome when compared with control group. A significant negative relation between mean MPV value and mean platelet count was found. Significant positive correlations between mean platelet count and mean total cholesterol and mean triglyceride levels were demonstrated. Conclusion: MPV in nephrotic syndrome patients can be an easy, cheap and simple method for determine the prognosis of the disease and steroid resistance.

Abstract:In this study, a hybrid security system is proposed. The proposed system is composed of two subsystems namely iris recognition system (IRS) and speaker recognition system (SRS). Pre-processing, feature extraction and feature matching are the main steps of these systems. In IRS subsystem, Gaussian filter, Canny edge detector, Hough transform, and histogram equalization is performed for pre-processing, respectively. After that, by applying 4-level Discrete Wavelet Transform (DWT) to pure iris image, the iris image is decomposed into four sub-bands (LL4, LH4, HL4 and HH4). In order to extract the feature vector from iris pattern, the LH4, HL4 and HH4 sub-bands (matrices) are merged into one matrix. Finally the matrix is transformed in vector to obtain the feature vector of iris image. For SRS subsystem, the pre-processing step includes spectral arrangement, silence part removing and band limitation operations. After pre-processing, frame blocking and windowing are applied to the long-term speech samples and then Fast Fourier Transform (FFT) is performed for the each short-term speech segments (frames). Finally, the Mel Frequency Cepstral Coefficients (MFCC) technique is performed in order to obtain feature vector of the speech. The feature matching step of both IRS and SRS is implemented with Dynamic Time Warping (DTW) which is an efficient algorithm to measure the distance between two vectors. According to the DTW results, the false acceptance rate (FAR) is zero and false rejecting rate (FRR) is about 4 % for the proposed hybrid system.

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