In recent decades, drug modification is no longer unusual in the pharmaceutical world as living things are evolving in response to environmental changes. A non-steroidal anti-inflammatory drug (NSAID) such as aspirin is a common over-the-counter drug that can be purchased without medical prescription. Aspirin can inhibit the synthesis of prostaglandin by blocking the cyclooxygenase (COX) which contributes to its properties such as anti-inflammatory, antipyretic, antiplatelet and etc. It is also being considered as a chemopreventive agent due to its antithrombotic actions through the COX’s inhibition. However, the prolonged use of aspirin can cause heartburn, ulceration, and gastro-toxicity in children and adults. This review article highlights the recent derivatives of aspirin, either to reduce the risk of side effects or to obtain better physicochemical properties. Aspirin derivatives can be synthesized in various pathways and have been reported to give better biological activities such as anticancer, anti-inflammatory, antibacterial, antioxidant, etc., compared to the parent drug. The presence of significant moieties such as nitric oxide (NO), NOSH, thiourea, azo, amide, and chalcone on the modified aspirin play important roles in achieving desired biological activities. The addition of the halogen in the modification has also become a preference among researchers as it also affects the actions due to its ability to hinder bacterial activity. This review is also sharing about the bulkiness effect of certain aspirin modifications that may cause steric hindrance of the compounds and influence their penetration into the enzyme’s active site. Overall, these aspirin modifications are safe to be considered as potential pharmaceutical agents.
Psoriasis is a dermatological, chronic, immune-mediated condition. Psoriasis symptoms are not associated with physical burden only, but it may also have psychosocial effects on patients, diminished cognitive control, poor body image and impairments in everyday life. The value of quality of life is important since improving it is the principal goal for non-curative disease. The aim of the current study was to evaluate quality of life in a sample of Iraqi patients with psoriasis. This study is a cross-sectional study that involved 300 already diagnosed psoriasis patients who attended to the center of Dermatology and Venereology, Medical City/Baghdad. The mean age of patients was (35.156 ±10.549 years). The Arabic version of Dermatology Li
... Show MoreObjective 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
... Show MoreImage 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
... Show MoreImage 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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