Psoriasis is a chronic, inflammatory condition that primarily affects the skin, hair, and joints and is associated with significant humanistic and economic consequences. Psoriasis was induced in mice in this work using an imiquimod 5% cream, an immune response modifier that can cause psoriasis-like skin inflammation when given orally. Paquinimod is prepared as a suspension and has been orally given to mice before imiquimod application. The current study found that paquinimod suspension reduced psoriasis area and severity index, spleen index, skin thickness ,TNF-α,IL-23,IL17 level and gene expression of TN

Psoriasis is a chronic, inflammatory condition that primarily affects the skin, hair, and joints and is associated with significant humanistic and economic consequences. This work induced psoriasis in mice using an imiquimod 5% cream, an immune response modifier that can cause psoriasis-like skin inflammation when given orally. Paquinimod is prepared as an ointment and has been topically given to mice before imiquimod application. In this study, albino mice were allocated into five groups and treated as follows: the control group received only a daily application of cream based on shaved back (62.5mg/2cm) with a daily topical dose of ointment for 14 consecutive days with the oral vehicle. The Imiquimod group received a daily topical

The recent escalation in Southeast Asia showed that the region is on the verge of political and economic crises through the United States and its allies imposing economic sanctions against China, and a military escalation that may begin with China’s invasion of Taiwan, especially since two-thirds of the American forces in the world are located in the Taiwan Strait and the South China Sea.

The United States claims that it is ready to defend the island of Taiwan in the event of the start of Chinese military operations, but the reality shows that the United States supports Taiwan militarily by equipping it with modern weapons to defend itself. Supporting Taiwan economically by engaging it in trade agreements with US allies in the r

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Tight reservoirs have attracted the interest of the oil industry in recent years according to its significant impact on the global oil product. Several challenges are present when producing from these reservoirs due to its low to extra low permeability and very narrow pore throat radius. Development strategy selection for these reservoirs such as horizontal well placement, hydraulic fracture design, well completion, and smart production program, wellbore stability all need accurate characterizations of geomechanical parameters for these reservoirs. Geomechanical properties, including uniaxial compressive strength (UCS), static Young’s modulus (Es), and Poisson’s ratio (υs), were measured experimentally using both static and dynamic met

     The stress(Y) – strength(X) model reliability Bayesian estimation which defines life of a component with strength X and stress Y (the component fails if and only if at any time the applied stress is greater than its strength) has been studied, then the reliability; R=P(Y<X), can be considered as a measure of the component performance. In this paper, a Bayesian analysis has been considered for R when the two variables X and Y are independent Weibull random variables with common parameter α in order to study the effect of each of the two different scale parameters β and λ; respectively, using three different [weighted, quadratic and entropy] loss functions under two different prior functions [Gamma and extension of Jeffery

In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.