During pregnancy, high blood pressure disorder is the most common medical complication in pregnancy. It is the foremost cause of maternal mortality and perinatal diseases. Vascular endothelial growth factor (VEGF) affects the growth of vascular endothelial cells, existence, and multiplying, which are known to be expressed in the human placenta. This study aimed to identify the expression VEGF in the placenta of hypertension and normotensive women. In this study, a cross-sectional study from november 2019 to February 2020. A total of 100 placentae involved 50 hypertensive cases and 50 normotensive groups were assessed. VEGF-A expression in two placentas groups was evaluated by immunohistochemistry techniques. Strong and moderate VEGF expression was seen in syncytiotrophoblasts, stromal and endothelial cells of hypertensive cases, while not seen in hypertensive cases. There were statistically significant differences in VEGF-A expression between hypertensive cases and normotensive group. In conclusion, VEGF-A expression was significantly increased in each of syncytiotrophoblasts, stroma and endothelial cells in the placenta of hypertensive cases, and it could be used to predict the development of hypertension.
This research aims to harmonize contemporary and traditional clothes, also expenses and savings. It is done by recycling evening clothes into Clothes with traditional features. The study followed descriptive explanatory approach. The sample consisted of seven dresses, as well as 208 female participants from Makkah Al-Mukarramah province, the age range was between 21 and 65 years old. An electronic questionnaire was distributed and the stability and reliability of the internal consistency were measured.
The research resulted in the ability to recycle evening dresses into modern clothes with traditional characteristics. It also confirmed that the reason of wearing traditional clothes is spirituality of the month of Ramadan. Addi
Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E
... Show MoreConsiderable amounts of domestic and industrial wastewater that should be treated before reuse are discharged into the environment annually. Electrocoagulation is an electrochemical technology in which electrical current is conducted through electrodes, it is mainly used to remove several types of wastewater pollutants, such as dyes, toxic materials, oil content, chemical oxygen demand, and salinity, individually or in combination with other processes. Electrocoagulation technology used in hybrid systems along with other technologies for wastewater treatment are reviewed in this work, and the articles reviewed herein were published from 2018 to 2021. Electrocoagulation is widely employed in integrated systems with other electrochemical tech
... Show MoreAs a result of the significance of image compression in reducing the volume of data, the requirement for this compression permanently necessary; therefore, will be transferred more quickly using the communication channels and kept in less space in memory. In this study, an efficient compression system is suggested; it depends on using transform coding (Discrete Cosine Transform or bi-orthogonal (tap-9/7) wavelet transform) and LZW compression technique. The suggested scheme was applied to color and gray models then the transform coding is applied to decompose each color and gray sub-band individually. The quantization process is performed followed by LZW coding to compress the images. The suggested system was applied on a set of seven stand
... Show MoreMaulticollinearity is a problem that always occurs when two or more predictor variables are correlated with each other. consist of the breach of one basic assumptions of the ordinary least squares method with biased estimates results, There are several methods which are proposed to handle this problem including the method To address a problem and method To address a problem , In this research a comparisons are employed between the biased method and unbiased method with Bayesian using Gamma distribution method addition to Ordinary Least Square metho
... Show MoreHartree-Fock (HF) method relies in the calculations of nonlinear optical properties (NLO) for benzoic acid molecule. Also, another theoretical study is conducted by using the TD-DFT Density Functional Theory through B3LYP/High Base Set 6-311++G (2d,2p) on Gaussian program09. Moreover, an experimental study has been done to obtain the electrons spectrum for benzoic acid with and without ethanol. While the experimental study is done by using UV/VIS. spectrophotometer. Energy gap values of electronic transition between HOMO and LUMO is obtained from theoretical and experimental results. Consequently, the theoretical result for determining the energy gap calculated from EHOMO-LUMO wasvery close to the results of UV / VIS. spectrum. A theoretica
... Show MoreThe issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of
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