One of the most popular causes for implant infection is dental plaque bacteria. Previous studies have shown the bactericidal effect of CO2 laser irradiation on bacteria associated with soft tissue surrounding the implant materials. No published studies have examined the effect of irradiation by CO2 laser on Streptococcus oralis and Staphylococcus aureus.The aim of this study was to evaluate the bactericidal effect of CO2 laser on bacteria that are causing dental implant infections. This study was carried out on two isolates of bacterial species out of 25 samples, isolated from patients having soft tissue infections around the dental implant. These two pure isolates including Streptococcus oralis and Staphylococcus aureus were identified

Analysis the economic and financial phenomena and other requires to build the appropriate model, which represents the causal relations between factors. The operation building of the model depends on Imaging conditions and factors surrounding an in mathematical formula and the Researchers target to build that formula appropriately. Classical linear regression models are an important statistical tool, but used in a limited way, where is assumed that the relationship between the variables illustrations and response variables identifiable. To expand the representation of relationships between variables that represent the phenomenon under discussion we used Varying Coefficient Models

Some parameters for advancement of Leishmania tropica infection were examined in three groups of golden hamsters, Group (1) inoculated with autoclaved killed Leishmania tropica , Group (2) inoculated with BCG vaccine alone while Group (3) Inoculated with mixed vaccine (autoclaved killed Leishmania with BCG). The follow up of experimentally infected animals with virulent isolation of Leishmania tropica was done for 90 days, the animals inoculated with mixed vaccine (autoclaved killed Leishmania with BCG) showed the minimum average in each of foot pad thickness (2.3 ± 0.05) mm after (60) days of infection, spleen enlargement (1.13±0.38) after (45) days of infection, spleen length (23.9±0.08) mm after (30) days of infection, liver weight(3.

Background: Coronavirus disease 2019 (COVID-19) is an emerging zoonotic disease caused by the new respiratory virus SARS-CoV2. It has a tropism in the lung tissues where excess target receptors exist. Periostin plays a role in subepithelial fibrosis associated with bronchial asthma. Since the Coronavirus's target is the human respiratory system, Periostin has been recently described as a valuable new biomarker in the diagnosis and evaluation of disease in patients with COVID-19 lung involvement. Objectives: To assess the level of Periostin in the serum of COVID-19 patients and to correlate its role in disease severity and prognosis. Subjects and Methods: Periostin serum levels were measured for 63 patients attending three main COVID

Phase change materials are known to be good in use in latent heat thermal energy storage (LHTES) systems, but one of their drawbacks is the slow melting and solidification processes. So that, in this work, enhancing heat transfer of phase change material is studied experimentally for in charging and discharging processes by the addition of high thermal conductive material such as copper in the form of brushes, which were added in both PCM and air sides. The additions of brushes have been carried out with different void fractions (97%, 94% and 90%) and the effect of four different air velocities was tested. The results indicate that the minimum brush void fraction gave the maximum heat transfer in PCM and reduced the time

 Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat