Abstract Exotoxin A is the most lethal virulence factor produced by Pseudomonas aeruginosa. It inhibits elongation factor-2 by ADP-ribosylation of EF2. This causes stop of the elongation of polypeptides. In recent study, the effect of low concentration of exotoxin A on some important internal organs of mice was studied. Four groups white mice were injected intraperitonialy with pure exotoxin A in the following manner 0.1, 0.2, 0.3, 0.4 ng\ animals. One mouse receives normal saline inrtaperitonialy as a control. After 72 hours the mice were killed and four organs were taken, liver, spleen, lung and heart from each killed mice. Histological sections were made from each organ and stained with hematoxylin and eosin stain then examined under microscope. The resultant pathological changes were included, congestion of blood vessels, degenerative and necrotic changes, and infiltration of inflammatory cells of these organs. These pathological changes increased with increase the amount of exotoxin A injection as compared with control. No mouse was dying due to injection of exotoxin A. As a conclusion, exotoxin A at low concentrations can affect important internal organs namely, liver, spleen, lung and heart, this effect increase with increasing the concentration of exotoxin A. Lethal dose50 is much higher than that of the highest concentration that causes pathological changes.
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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
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A compact microstrip six-port reflectometer (SPR) with extended bandwidth is proposed in this paper. The design is based on using 16-dB multi-section coupled line directional couplers and a multi-section 3-dB Wilkinson power divider operating from 1 to 6 GHz. The proposed SPR employs only two calibration standards: a matched load and an open load. As compared to other dielectric substrates, fabricating the proposed SPR involves using a low-cost (FR4) substrate. A novel algorithm is also proposed to estimate the complex reflection coefficient over the frequency ranges at which the standard performance of the circuit components is not fully satisfied. The new algorithm is based on the circles’ intersection points, which have been de
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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
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Rheumatoid arthritis is a chronic, progressive, inflammatory autoimmune disease of unidentified etiology, associated with articular, extra-articular and systemic manifestation that require long-standing treatment. Taking patient’s beliefs about the prescribed medication in consideration had been shown to be an essential factor that affects adherence of the patient in whom having positive beliefs is an essential for better adherence. The purpose of the current study was to measure beliefs about medicines among a sample of Iraqi patients with Rheumatoid arthritis and to determine possible association between this belief and some patient-certain factors. This study is a cross-sectional study carried out on 250 already diagnosed rheumatoid
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Objective Thalassemic patients present with multiple immune abnormalities that may predispose them to oral Candida, however this has not been investigated. The aim of this study was to assess oral candidal colonization in a group of patients with β-thalassemia major both qualitatively and quantitatively. Study design The oral mycologic flora of 50 β-thalassemia major patients and 50 age- and sex-matched control subjects was assessed using the concentrated oral rinse technique. Candida species were identified using the germ tube test and the Vitek yeast identification system. Results Oral Candida was isolated from 37 patients (74%) and 28 healthy subjects (56%; P = .04). The mean candidal count was significantly higher in thalassemic patie
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