Background: This study was conducted to assess the effect of sonic activation and bulk placement of resin composite in comparison to horizontal incremental placement on the fracture resistance of weakened premolar teeth. Materials and method: Sixty sound human single-rooted maxillary premolars extracted for orthodontic purposes were used in this study. Teeth were divided into six groups of ten teeth each: Group 1 (sound unprepared teeth as a control group), Group 2 (teeth prepared with MOD cavity and left unrestored), Group 3 (restored with SonicFill™ composite), Group 4 (restored with Quixfil™ composite), Group 5 (restored with Tertic EvoCeram® Bulk Fill composite) and Group 6 (restored with Universal Tetric EvoCeram® composite using horizontal incremental layering technique). Standardized class II MOD cavity was prepared in all teeth except (group 1).After finishing the restorative procedure of each group according to the manufacturer's instructions, all teeth were stored in deionized distilled water in an incubator at 37°C for seven days.All specimens were subjected to compressive axial loading until fracturein a universal testingmachine.Specimens were examined by a stereomicroscope at a magnification of (20X) to evaluate the mode of fracture . Results: The results of this study revealed that the control group exhibited the highest fracture resistance compared to all prepared teeth groups (restored or unrestored) and the differences were statistically highly significant (P<0.01), except with group 3 (which was restored with SonicFill™ composite) where the difference was statistically significant only (P < 0.05).Additionally the results of this study revealed that the prepared unrestored teeth (Group 2) exhibited the lowest fracture resistance compared to all restored groups and the differences were statistically highly significant (P<0.01). Meanwhile, among the restored teeth groups, teeth restored with SonicFill™ composite (group 3) exhibited the highest fracture resistance as compared with all other restored groups and the difference was statistically highly significant (P<0.01). On the other hand, no statistically significant differences in fracture resistance were found among groups 4, 5 and 6, which were restored with Quixfil™ composite, Tetric EvoCeram® Bulk Fill composite and Universal Tetric EvoCeram® composite, respectively (P > 0.05). Group 3and Group 5 showed mostly mixed mode of failure, while Group 4 showed mostly adhesive mode of failure. On the other hand Group 6 teeth showed different modes of failure. Conclusions: SonicFill™ composite can be considered as a viable treatment modality for the restoration of weakened maxillary premolar teeth. On the other hand, the time-consuming incremental layering technique can be substituted with bulk filling, using bulk fill materials (Quixfil™ and Tetric EvoCeram® Bulk Fill) for reinforcement ofweakened maxillary premolars.
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The aim of this paper is to identify Nano-particles that have been used in diagnosis and treatment of leishmaniasis in Iraq. All experiments conducted in this field were based on the following nanoparticles: gold nanoparticles, silver nanoparticles, zinc nanoparticles, and sodium chloride nanoparticles. Most of these experiments were reviewed in terms of differences in the concentrations of nanoparticles and the method that was used in the experiments whether it was in vivo or in vitro. These particles used in most experiments succeeded in inhibiting the growth of Leishmania parasites.
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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Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat
... 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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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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Pituitary adenomas are the anterior pituitary tumors. Patients with an Aryl Hydrocarbon Receptor-Interacting Protein (AIP) mutation (AIP- mut) tend to have more aggressive tumors occurring at a younger age. Single nucleotide polymorphisms (SNPs) in many studies have been related to metabolic comorbidities in the general population. Study aims investigated the role of AIP gene SNPs with susceptibility to acromegaly pituitary- adenoma, with levels of LH, FSH, TSH, Testosterone, IGF1,GH, FT4 , Prolactin hormones and blood sugar levels. The study was conducted on a group of acromegaly patients, including 50 patients) both Genders( with hyperplasia of the ends, and apparently healthy control group. Genotyping of
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