Background: Periodontal disease is a chronic bacterial infection that affects the gingiva and bone supporting the teeth. Smoking, which is an important risk factor for periodontitis, induce oxidative stress in the body and cause an imbalance between reactive oxygen species (ROS) and antioxidants, such as superoxide dismutase (SOD). This study aimed to evaluate the influence of smoking on periodontal health status by estimating the levels of salivary SOD level in non-smokers (controls) and light and heavy smokers and to test the correlation between the SOD enzyme level and the clinical periodontal parameters in each group. Materials and Methods: The study sample consisted of 75 male, with age ranged from 35 to 50 years. Clinically, the periodontal parameters used in this study were Plaque index (PLI). Gingival index (GI), probing pocket depth (PPD), Bleeding on probing (BOP) and clinical attachment level (CAL), unstimulated saliva sample were collected from all subjects and the levels of superoxide dismutase enzyme was analyzed for each group , and correlate the mean of salivary enzyme levels with the clinical periodontal parameters. Results: Highly significant differences in PLI between (non smokers/heavy smokers) and (light smokers/heavy smokers).On the other hand no significant difference in gingival index between groups. There were a high association between severity of smoking & probing pocket depth and there is association between severity of smoking and clinical attachment loss. There were a significant difference in the level of salivary superoxide dismutase enzyme between the (non smokers/light smokers) groups & between (heavy smokers/light smokers) &there were highly significant differences between (non smokers/heavy smokers) groups. There is no correlation between the activities of the salivary superoxide dismutase enzyme and the clinical periodontal parameters except in SOD with (BOP score 0 and PPD score 1&score 3) in heavy smokers group. Conclusions: Superoxide dismutase enzyme can be used as biomarker for estimating the level of oxidative stress on smoking habits.
A study carried out for study effect of furfural that extracted from corn cobs by using specialized reaction system laboratory on phytopathogenic fungi: Pythium aphanidermatum, Rhizoctonia solani, Macrophomina phaseolina and Fusarium solani in addition to biocontrol fungus Trichoderma viride were isolated from infected plants and from their rhizosphere . The preparation results of different concentrations from stock solution in concentration 1% of furflural showed that The concentration was 100 ppm of furfural was inhibited the growth of P. aphanidermatum46.7 % and the was in concentration 400 ppm. while the concentration 500 ppm caused inhibition 50% and 41.1% of R. solani and F. solani respectively. Whereas the concentration 500 pp
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Steel–concrete–steel (SCS) structural systems have economic and structural advantages over traditional reinforced concrete; thus, they have been widely used. The performance of concrete made from recycled rubber aggregate from scrap tires has been evaluated since the early 1990s. The use of rubberized concrete in structural construction remains necessary because of its high impact resistance, increases ductility, and produces a lightweight concrete; therefore, it adds such important properties to SCS members. In this research, the use of different concrete core materials in SCS was examined. Twelve SCS specimens were subjected to push-out monotonic loading for inspecting their mechanical performance. One specimen was constructed from co
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Obesity has been connected to a higher risk of acquiring a number of diseases, including cancer, type 2 diabetes mellitus (T2DM), hypertension, and cardiovascular disease. Periostin is a crucial regulator of the growth and maintenance of bones, teeth, and the heart.
The aim of the study was to estimate the level of (periostin, glycated hemoglobin [HbA1c], fasting serum [FBG], total cholesterol [TC], high-density lipoprotein [HDL], low-density lipoprotein [LDL], and triglycerides [TG]) in diabetic Ira
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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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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