Osteoporosis (OP) is a systemic skeletal disorder that is characterized by reduced bone mass and micro-architectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture. The most frequent osteoporotic fractures are fractures of the hip, wrist, and spine. The exact causes of OP are still unknown; several factors contribute to the disorder. Subjects and Methods: This study consists of patient groups, this group (Group A) was composed of 80 postmenopausal women with OP and osteopenia and the patient group was subdivided into two group; First group (GroupA1) was composed of 50 postmenopausal women with OP and the second group (Group A2) composed of (30) Postmenopausal Women with osteopenia. In addition, to control group (20), 5 mL of venous blood sample were collected from each patient and healthy control in the population study, and the blood sample was transferred to a clean gel tube, left at room temperature for at least 30 minutes for clotting, then centrifuged for 5–10 minutes at 3000 rpm. Then, separated and divided into aliquots to obtain the serum, then its kept frozen at -20°C until analysis. The obtained serum was used to measure IL17A, FGF21, CXC12, calcium, and alkaline Phosphatase (ALP). Measurement of IL17A, FGF21 and CXC12 levels were performed by ELISA. The total calcium and serum ALK were measured by spectrophotometric-based method. Results: Serum levels of IL17A, FGF21 and CXC12 are significantly increased in Group A and subgroup (A1 and A2). Serum levels of total calcium and ALP are non-significant in Group A and sub group patients. Significant negative correlation between serum levels of IL17A and T score, FGF21 and T score, CXC12 and T score, IL 17A and Z score, IL17A and Calcium. Conclusions: Serum levels of IL17A, FGF21 and CXC12 is significantly increased in Group A and subgroup patients. Serum levels of total calcium and ALP are non-significant in Group A and sub group patients. Significant negative correlation exists between serum levels of IL17A and T score, FGF21 and T score, CXC12 and T score in Groups A and A1.
The 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 is concerned with finding solutions to free-boundary inverse coefficient problems. Mathematically, we handle a one-dimensional non-homogeneous heat equation subject to initial and boundary conditions as well as non-localized integral observations of zeroth and first-order heat momentum. The direct problem is solved for the temperature distribution and the non-localized integral measurements using the Crank–Nicolson finite difference method. The inverse problem is solved by simultaneously finding the temperature distribution, the time-dependent free-boundary function indicating the location of the moving interface, and the time-wise thermal diffusivity or advection velocities. We reformulate the inverse problem as a non-
... Show MoreThe degradation of Toluidine Blue dye in aqueous solution under UV irradiation is investigated by using photo-Fenton oxidation (UV/H2O2/Fe+). The effect of initial dye concentration, initial ferrous ion concentration, pH, initial hydrogen peroxide dosage, and irradiation time are studied. It is found put that the removal rate increases as the initial concentration of H2O2 and ferrous ion increase to optimum value ,where in we get more than 99% removal efficiency of dye at pH = 4 when the [H2O2] = 500mg / L, [Fe + 2 = 150mg / L]. Complete degradation was achieved in the relatively short time of 75 minutes. Faster decolonization is achieved at low pH, with the optimal value at pH 4 .The concentrations of degradation dye are detected by spectr
... Show MoreWireless Body Area Sensor Network (WBASN) is gaining significant attention due to its applications in smart health offering cost-effective, efficient, ubiquitous, and unobtrusive telemedicine. WBASNs face challenges including interference, Quality of Service, transmit power, and resource constraints. Recognizing these challenges, this paper presents an energy and Quality of Service-aware routing algorithm. The proposed algorithm is based on each node's Collaboratively Evaluated Value (CEV) to select the most suitable cluster head (CH). The Collaborative Value (CV) is derived from three factors, the node's residual energy, the distance vector between nodes and personal device, and the sensor's density in each CH. The CEV algorithm operates i
... Show MoreCr2O3 thin films have been prepared by spray pyrolysis on a glass substrate. Absorbance and transmittance spectra were recorded in the wavelength range (300-900) nm before and after annealing. The effects of annealing temperature on absorption coefficient, refractive index, extinction coefficient, real and imaginary parts of dielectric constant and optical conductivity were expected. It was found that all these parameters increase as the annealing temperature increased to 550°C.
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
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