Asthma is a chronic disease characterized by inflammatory events in the airways, that makes breathing a hard process in these patients. Angiotensin II (AngII) is important regulator for aldosterone production. Also, it participates in inducing reactive oxygen species (ROS) and inflammatory events. Angiotensin-converting enzyme (ACE) is responsible for the production of AngII. We hypothesised that ACE and AngII participate in the progression of asthma by increasing ROS production and inflammatory processes. Thus, we evaluated the level of AngII, the activity of ACE, and the level of malondialdehyde (MDA) as indicators for oxidative stress in 60 asthmatic male patients and 30 non-asthmatic male control. An asthma control test (ACT) was estima

This paper numerically and theoretically investigates the optical and thermal performance of a parabolic trough collector PTC system. Many numerical simulations and theoretical analyses are conducted to demonstrate the influence of the receiver geometry and shifting from the focal position on the optical performance. The examined receiver geometries are circular, square, triangular, elliptical, and the new circular–square combined geometry is named as channel receiver. The thermal performance of PTC is examined for different volume flow rates theoretically in the range of (0.36 to 2.4 lpm). The results show that the best optical design is the channel receiver with an intercept factor of 84%, while the worst is the elliptical receiver with

Purpose: Despite the high clinical accuracy of dynamic navigation, inherent sources of error exist. The purpose of this study was to improve the accuracy of dynamic navigated surgical procedures in the edentulous maxilla by identifying the optimal configuration of intra-oral points that results in the lowest possible registration error for direct clinical implementation. Materials and methods: Six different 4-area configurations were tested by 3 operators against positive and negative controls (8-areas and 3-areas, respectively) using a skull model. The two dynamic navigation systems (X-Guide® and NaviDent®) and the two registration methods (bone surface tracing and fiducial markers) produced four registration groups. The accuracy of the

Nowadays, there is increased interest in the biosynthesis of microbial melanin related to their numerous biological functions and applications in many fields, especially in medical fields, including immune-modulating, antimicrobial antibiotic, antiviral antivenin, anticancer, antitumor activity, and anti-biofilm activity. Pyomelanin is a hydrophobic macromolecule that is typically dark brown or black in color, formed by the oxidative polymerization of phenolic or indolic compounds. Pyomelanin is reported to be safe for consumption, thus providing a crucial strategy for biocontrol of biofilm. Furthermore, natural pyomelanin is known as a potent antioxidant, photoprotective, and free radical scavenging. Objective: This study focuses on the

Roller Compacted Concrete (RCC) is a mass concrete with zero slump consisting of well-graded aggregate, cementitious materials and water. RCC has many advantages over other pavement types, particularly cost and speed of construction. However, it also presents difficulties with high-speed applications related to surface texture and surface evenness. These difficulties have so far restricted the use of RCC to the lower layers of normal roads. This study investigates the possibility of expanding the application of RCC for normal roads and highways by using a two-layer system of roller compacted concrete. The suitability of RCC for such an application depends fundamentally on bond strength between the layers. The bond strength measured by shear

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class