Background: The bond strength of root canal sealers to dentin and gutta-percha seems to be an important property for maintaining the stability of root canal filling, which potentially influences both leakage and root strength. The objective of this, in vitro, study was to evaluate the shear bond strength of three different endodontic sealers (Gutta-Flow, AH Plus, Apexit Plus) to dentin, in the presence and absence of the smear layer and gutta percha. Material and Methods: After slicing off the occlusal 2mm of 60 extracted human maxillary premolar teeth, the exposed dentin served as the tested surfaces; the teeth were fixed with cold cure acrylic, and were divided into two groups according to the smear layer presence, group A without smear layer, when dentin surfaces were irrigated with EDTA 17% followed by distilled water then subdivided into 3 subgroups according to the type of sealer used; group B when dentin surfaces were washed with distilled water only, then subdivided into 3 subgroups. Thirty samples of gutta-percha were prepared and named as group C which was subdivided into 3 subgroups. Five mm long section of polyethylene tubes were placed on the dentin or gutta percha surfaces and filled with freshly mixed sealer. After one week, all the samples were tested for shear bond strength by the Instron Universal Testing Machine at a cross head speed of 0.5 mm/min. The data was calculated in MPa and was statistically analyzed Result: There was a highly significant difference in the shear bond strength (P < 0.05) in comparison among the tested groups, GuttaFlow showed non-significant difference in bond strength to dentin with and without smear layer, while AH Plus and Apexit Plus showed a high significant difference. Conclusions: AHPlus showed the highest shear bond strength in all the tested samples, while GuttaFlow was the least. Additionally, AH Plus and Apexit Plus shear bond strengths were affected by the smear layer removal, while GuttaFlow was not.
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Predicting peterophysical parameters and doing accurate geological modeling which are an active research area in petroleum industry cannot be done accurately unless the reservoir formations are classified into sub-groups. Also, getting core samples from all wells and characterize them by geologists are very expensive way; therefore, we used the Electro-Facies characterization which is a simple and cost-effective approach to classify one of Iraqi heterogeneous carbonate reservoirs using commonly available well logs.
The main goal of this work is to identify the optimum E-Facies units based on principal components analysis (PCA) and model based cluster analysis(MC
... Show MoreMachine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed
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DBN Rashid, JOURNAL OF XI'AN UNIVERSITY OF ARCHITECTURE & TECHNOLOGY, 2020
Objectives: This study aimed to evaluate the therapeutic potential effects of ascorbic acid or and pyridoxine on diabetic renal microalbumiuria. Methods: This was a cross-sectional study on patients with diabetes mellitus at Al-Yarmouk teaching hospital from January to December 2012, Iraq-Baghdad. Twenty one patients with diabetes mellitus (D.M), 8 IDDM and 13 IDDM were selected from, the duration of disease were ranged from 2-12 years for both type (10 females and 11males) and all enrolled patients ages were ranged from 28-65years. The concentration of total protein in urine was calculated by a biuret colorimetric assay and the urine creatinine level was measured by a modified Jaffe test. Statistical analysis: results are expressed as mean
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The consequences of ionizing radiation-induced oxidative stress on radiographers in X-ray and CT-scan departments utilizing several biochemical were analyzed. The study found highly considerable discrepancies in the interplay between radiation levels and gender in terms of mean Malondialdehyde (MAD), Vitamin D3 (Vit.D3), Triiodothyronine (T3), Thyroxine (T4), and High-Density Lipoprotein (HDL), but not Thyroid Stimulating Hormone (TSH), cholesterol, triglyceride (TG) and Low-Density Lipoprotein (LDL). The findings indicated that malondialdehyde is a useful biomarker for assessing oxidative stress in radiographers with exposure to ionizing radiation.
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