Search

The co-infection of HCV/CMV may accelerate the progression of liver diseases and worsen responsiveness to IFN treatment. The Direct-acting antiviral agents (DAAs), currently approved therapy for HCV, may cause a transient change in immune status, favoring the reactivation of other viruses. The current study aims to evaluate the impact of DAAs treatment on the reactivation of latent CMV in HCV patients.The serological IgG, IgM Abs against CMV were detected by ELISA on192 HCV patients. The seronegative CMV IgM patients received (sofosbuvir/daclatasvir) regimen, then the CMV reactivation was examined by measuring the CMV IgM by ELISA and CMV DNA by real-time PCR.The serological data revealed that all patients were positive for CMV IgG (100%) while (64%) patients were positive for CMV IgM. The seronegative CMV IgM (36%) received the DAAs protocol. The sustained virological response was monitored by measuring the HCV RNA viremia in the patient sera. The serological data revealed that 28.6% of patients had a reactivation of CMV, while 18.5% of patients had detectable CMV DNA viremia. Moreover, there was a significant improvement in liver function as well as a decrease in FIB-4 and APRI scores at EOT. SVR was reached 97.4% among the total studied patients (N= 192).CMV co-infection has no impact on the response rate to DAAs. However, the CMV reactivation might have occurred after the complete eradication of HCV by DAAs.

A non-Newtonian Williamson fluid flow due to a stretching sheet with radiation, magnetic field, and viscous dissipation effects is described using variable conductivity and variable diffusivity. The Cattaneo-Christov model is used to correctly compute the physical properties of a heat and mass flux model. Both the chemical reaction phenomenon and the slip velocity have an impact on the heat and mass mechanism. The physical problem is represented mathematically as a nonlinear coupled differential system. After that, the shooting method is used to solve the mathematical model numerically. To gain a better understanding of the behavior of governing emergent factors on dimensionless velocity, concentration, and temperature profiles, physical interpretations are created and discussed utilizing graphical and tabular representations. The results show that the Sherwood number and the Nusselt number are both decreased by the magnetic, viscosity, and slip velocity parameters. Also, according to the findings it has been observed that the concentration outlines enhances for the magnetic number, the viscosity parameter, and the slip velocity parameter, but they dwindle for expanding reaction rate values. Finally, after confirmation of our numerical results, the theoretical results show good agreement with previously published work.

Human health may depend on the environment and its compartments, which may include climatic factors. These climatic factors and their changes might impact on human health particularly the outbreak of pandemics like COVID-19. The combined stress resulted from climate changes and COVID-19 could be noticed in several countries especially in the developing countries. Malnutrition is considered one of the most important problems in the developing countries in particular under the droughts, flooding, and other climatic events. Malnutrition was aggravated under COVID-19 outbreak in these countries due to the closure of border between countries, the crisis of global trade, and the global food insecurity. The biofortification process is the sustainable solution to overcome malnutrition, which included very recently using nano-nutrients as called nano-biofortification. The approach of nano-biofortification is a promising tool in producing biofortified edible plants, otherwise this tool still needs more studies to answer the open questions like which nano-nutrients can be used in nano-biofortification? Which recommended doses and crops are considered suitable candidates?

Introduction: COVID-19 severity and mortality predictors could determine admission criteria and reduce mortality. We aimed to evaluate the clinical-laboratory features of hospitalized patients with COVID-19 to develop a novel score of severity and mortality. Methodology: This retrospective cohort study was conducted using data from patients with COVID-19 who were admitted to five Egyptian university hospitals. Demographics, comorbidities, clinical manifestations, laboratory parameters, the duration of hospitalization, and disease outcome were analyzed, and a score to predict severity and mortality was developed. Results: A total of 1308 patients with COVID-19, with 996 (76.1%) being moderate and 312 (23.9%) being severe cases, were included. The mean age was 46.5 ± 17.1 years, and 61.6% were males. The overall mortality was 12.6%. Regression analysis determined significant predictors, and a ROC curve defined cut-off values. The COVEG severity score was defined by age ≥ 54, D-dimer ≥ 0.795, serum ferritin ≥ 406, C-reactive protein ≥ 30.1, and neutrophil: lymphocyte ratio ≥ 2.88. The COVEG mortality score was based on COVEG severity and the presence of cardiac diseases. Both COVEG scores had high predictive values (area under the curve 0.882 and 0.883, respectively). Conclusions: COVEG score predicts the severity and mortality of patients with COVID-19 accurately.

Given a reliable tree network containing vertices where each edge has an independent operational probability, this paper presents finding a reliable subtree with at most leaves and with a diameter of at most which maximizes the expected number of nodes that are reachable from the selected subtree by operational paths. An efficient algorithm is presented for finding a reliable tree core of. Numerical example is explained to clarify the efficient algorithm.

This research is designed to cover literature gaps in the intention to adopt mobile commerce in Jordan as development country. In one hand we explored and identified the non-technological factors that affect the intention to adopt mobile commerce. In the other hand we introduced a revised and extended mobile commerce technology adaption model based on the available literature and based on the Technology Acceptance Model (TAM). Our result shows that our proposed model is valid. Our model validity was confirmed using Loading Factor and Kaiser-Mayer- Olkin (KMO). The result of this research shows that Perceived Usefulness (PU), Perceived Ease of Use (PEOU), privacy, compatibility, government policy, legal protection, risk, cost and social-culture values factors have a direct significant effect in the intention to adopt mobile commerce. This research also finds that those factors are different in their effect in the mobile commerce adoption decision where legal protection factor has the highest impact in mobile commerce adoption decision while perceived usefulness factor has the lowest impact in making such decision. The result also shows that there is a positive relationship between all study factors and the intention to adapt mobile commerce except for risk and cost factors.

There have been a little number of attempts to develop an Arabic sign recognition system that can be used as a communication means between hearing-impaired and other people. This study introduces the first automatic isolated-word Arabic Sign Language (ArSL) recognition system based on Time Delay Neural Networks (TDNN). The proposed vision-based recognition system that the user wears two simple but different colors gloves when performing the signs in the data sets within this study. The two colored regions are recognized and highlighted within each frame in the video to help in recognizing the signs. This research uses the multivariate Gaussian Mixture Model (GMM) based on the characteristics of the well known Hue Saturation Lightness Model (HIS) in determining the colors within the video frames. In this research the mean and covariance of the three colored region within the frames are determined and used to help us in segmenting each frame (picture) into two colored regions and outlier region. Finally we propose, create and use the following four features as an input to the TDNN; the centroid position for each hand using the center of the upper area for each frame as references, the change in horizontal velocity of both hands across the frames, the change in vertical velocity of both hands across the frames and the area change for each hand across the frames. A large set of samples has been used to recognize 40 isolated words coded by 10 different signers from the Standard Arabic sign language signs. Our proposed system obtains a word recognition rate of 70.0% in testing set.

The identification of human beings based on their biometric body parts, such as face, fingerprint, gait, iris, and voice, plays an important role in electronic applications and has become a popular area of research in image processing. It is also one of the most successful applications of computer–human interaction and understanding. Out of all the abovementioned body parts,the face is one of most popular traits because of its unique features.In fact, individuals can process a face in a variety of ways to classify it by its identity, along with a number of other characteristics, such as gender, ethnicity, and age. Specifically, recognizing human gender is important because people respond differently according to gender. In this paper, we present a robust method that uses global geometry-based features to classify gender and identify age and human beings from video sequences. The features are extracted based on face detection using skin color segmentation and the computed geometric features of the face ellipse region. These geometric features are then used to form the face vector trajectories, which are inputted to a time delay neural network and are trained using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) function. Results show that using the suggested method with our own dataset under an unconstrained condition achieves a 100% classification rate in the training set for all application, as well as 91.2% for gender classification, 88% for age identification, and 83% for human identification in the testing set. In addition, the proposed method establishes the real-time system to be used in three applications with a simple computation for feature extraction.