The present study experimentally and numerically investigated the impact behavior of composite reinforced concrete (RC) beams with the pultruded I-GFRP and I-steel beams. Eight specimens of two groups were cast in different configurations. The first group consisted of four specimens and was tested under static load to provide reference results for the second group. The four specimens in the second group were tested first under impact loading and then static loading to determine the residual static strengths of the impacted specimens. The test variables considered the type of encased I-section (steel and GFRP), presence of shear connectors, and drop height during impact tests. A mass of 42.5 kg was dropped on the top surface at the m

BACKGROUND: Burkholderia cepacia adhesion and biofilm formation onto abiotic surfaces is an important feature of clinically relevant isolates. The in vitro biofilm formation of B. cepacia onto coated indwelling urinary catheters (IDCs) with moxifloxacin has not been previously investigated. OBJECTIVES: To examine the ability of B. cepacia to form biofilms on IDCs and the effect of coating IDCs with moxifloxacin on biofilm formation by B. cepacia in vitro. MATERIAL AND METHODS: The adhesion of B. cepacia to coated and uncoated IDCs with moxifloxacin was evaluated. Pieces of IDCs were coated with moxifloxacin (adsorption method). The spectrophotometric method was used to check moxifloxacin leaching into tubes. Coated and uncoated tubes were i

Clobetasol propionate (CP) is a super potent corticosteroid widely used to treat various skin disorders such as atopic dermatitis and psoriasis. However, its utility for topical application is hampered due to its common side effects, such as skin atrophy, steroidal acne, hypopigmentation, and allergic contact dermatitis. Microsponge is a unique three-dimensional microstructure particle with micro and nano-meters-wide cavities, which can encapsulate both hydrophilic and lipophilic drugs providing increased efficacy and safety. The aim of the current study is to prepare and optimize clobetasol-loaded microsponges. The emulsion solvent diffusion method is used for the preparation of ethylcellulose (EC)-based microsponges. The impact of

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Objective: To evaluate whether the SOX2 protein could be used as a predictor in patients with GC and to assessment the correlation between the IHC expression of the SOX2 protein and the various clinic pathological Parameters as age, sex, histopathological subtypes, grade and stage of the tumor by immune-histochemical Technique. This is a retrospective study conducted on 60 randomly selected patients (30) normal versus (30) GC, at the pathology department of the Gastroenterology and Hepatology Teaching Hospital & some private hospitals. It were collected and diagnosed during the period between from 2014 to 2018. From each block were stained with H&E and IHC stained for SOX2. The Statistical analysis was done using SPSS system, and the differ

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample