The aim of the present study is to evaluate the change in the levels of glucagon, GLP-1 and GPCR in diabetic patient's and diabetic with dyslipidemia as metabolic syndrome. The study included 75 male aged ranged (30-50) years and with BMI (25-29) kg/m2 which divided into three groups as follows: group one (G1): consist of 25 subjects as healthy control group. Group two (G2): consist of 25 patient's with diabetes mellitus and group three (G3): consist of 25 patient's with diabetic and dyslipidemia as metabolic syndrome. Serum was used in determination of FBG, lipid profile, insulin, glucagon, GLP-1 and GPCR. Whole blood was determination of HbA1c. The results revealed significant elevation in FBG and HbA1c in G2 and G3 comparing to G1. While non-significant elevation was found in FBG and HbA1c in G3 comparing to G2. The results also, showed no significant elevation in each of TC, TG, LDL and VLDL in G2 comparing to G1. Whereas, significant elevation was noticed in these parameters when G3 comparing to G2 and G1. Also, the levels of HDL showed no significant reduction in G2 comparing to G1, while significant reduction was found in G3 comparing to G2 and G1. The results also, revealed no significant elevation in insulin levels in G2 comparing to G1. While significant elevation was found in G3 comparing to G2 and G1. Also, the results illustrated significant elevation in glucagon levels in G2 comparing to G1. While significant reduction was seen in G3 comparing to G2. Significant reduction in GLP-1 and GPCR levels was found in G2 comparing to G1. While significant elevation in these parameters noticed in G3 comparing to G2 and G1. The conclusion could be drawn from this study that dyslipidemia affecting GLP-1 and GPCR levels that may be these patient's at high risk for cardiovascular disease.
Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d
... Show MoreExperimental investigation for small horizontal portable wind turbine (SHPWT) of NACA-44, BP-44, and NACA-63, BP-63 profiles under laboratory conditions at different wind velocity range of (3.7-5.8 m/s) achieved in present work. Experimental data tabulated for 2, 3, 4, and 6- bladed rotor of both profiles within range of blade pitch angles . A mathematical model formulated and computer Code for MATLAB software developed. The least-squares regression is used to fit experimental data. As the majority of previous works have been presented for large scale wind turbines, the aims were to present the performance of (SHPWT) and also to make a comparisons between both profiles to conclude which is the best performance. The overall efficiency and el
... Show MoreThe main object of this article is to study and introduce a subclass of meromorphic univalent functions with fixed second positive defined by q-differed operator. Coefficient bounds, distortion and Growth theorems, and various are the obtained results.
During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
... Show MoreIn this study, an experimental investigation had conducted for six high strength laced reinforced concrete one-way slabs to discover the behavior of laced structural members after being exposed to fire flame (high temperature). Self-compacted concrete (SCC) had used to achieve easy casting and high strength concrete. All the adopted specimens were identical in their compressive strength of ( , geometric layout 2000 750 150 mm and reinforcement specifics except those of lacing steel content, three ratios of laced steel reinforcement of (0.0021, 0.0040 and 0.0060) were adopted. Three specimens were fired with a steady state temperature of for two hours duration and then after the specimens were cooled suddenly by spraying water. The
... Show MoreThe search included a comparison between two etchands for etch CR-39 nuclear track detector, by the calculation of bulk etch rate (Vb) which is one of the track etching parameters, by two measuring methods (thichness and change mass). The first type, is the solution prepared from solving NaOH in Ethanol (NaOH/Ethanol) by varied normalities under temperature(55˚C)and etching time (30 min) then comparated with the second type the solution prepared from solving NaOH in water (NaOH/Water) by varied normalities with (70˚C) and etching time (60 min) . All detectors were irradiated with (5.48 Mev) α-Particles from an 241Am source in during (10 min). The results that Vb would increase with the increase of
... Show MoreThis paper presents the first data for bremsstrahlung buildup factor (BBUF) produced by the complete absorption of Y-91 beta particles in different materials via the Monte Carlo simulation method. The bremsstrahlung buildup factors were computed for different thicknesses of water, concrete, aluminum, tin and lead. A single relation between the bremsstrahlung buildup factor BBUF with both the atomic number Z and thickness X of the shielding material has been suggested.