Background: Alterations in the microhardness and roughness are commonly used to analyze the possible negative effects of bleaching products on restorative materials. This in vitro study evaluated the effect of in-office bleaching (SDI pola office +) on the surface roughness and micro-hardness of four newly developed composite materials (Z350XT –nano-filled, Z250XT-nano-hybrid, Z250-mico-hybrid and Silorane-silorane based). Materials and methods: Eighty circular samples with A3 shading were prepared by using Teflon mold 2mm thickness and 10mm in diameter. 20 samples for each material, 10 samples for base line measurement (surface roughness by using portable profillometer, and micro-hardness by usingDigital Micro Vickers Hardness Tester), and 10 samples for after bleaching measurement. The appropriate bleaching procedure was performed on the top surface of test groups for 90 minutes total bleaching period. Then surface roughness and hardness were tested at the end of the duration. Statistical analysis was carried out using ANOVA, LSD and t-test. Results: There was a highly significant increase in surface roughness of all tested groups after bleaching. There is a highly significant increase in micro-hardness for Z250, there is decrease in Micro-hardness for siloraneand Z250xt and there is a non-significant increase in micro-hardness of Z350xt. Conclusion: bleaching has a negative effect on surface roughness of all the tested materials, as surface roughness increased after bleaching. Micro-hardness is a material dependent, there is different reaction to bleaching depending on the resin, load and size of the fillers used in the materials. Nano-filled composite is the material that has better performance than the other tested materials, as it is the material that has the least affection by bleaching.
Objective(s): The study aims to assess the early detection of early detection of first degree relatives to type-II
diabetes mellitus throughout the diagnostic tests of Glycated Hemoglobin A1C. (HgbA1C), Oral Glucose Tolerance
Test (OGTT) and to find out the relationship between demographic data and early detection of first degree
relatives to type-II diabetes mellitus.
Methodology: A purposive "non-probability" sample of (200) subjects first degree relatives to type-II diabetes
mellitus was selected from National Center for Diabetes Mellitus/Al-Mustansria University and Specialist Center
for Diabetes Mellitus and Endocrine Diseases/Al-kindy. These related persons have presented the age of (40-70)
years old. A questio
In the present work advanced oxidation process, photo-Fenton (UV/H2O2/Fe+2) system, for the treatment of wastewater contaminated with oil was investigated. The reaction was influenced by the input concentration of hydrogen peroxide H2O2, the initial amount of the iron catalyst Fe+2, pH, temperature and the concentration of oil in the wastewater. The removal efficiency for the system UV/ H2O2/Fe+2 at the optimal conditions and dosage (H2O2 = 400mg/L, Fe+2 = 40mg/L, pH=3, temperature =30o C) for 1000mg/L load was found to be 72%.
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Solubility problem of many of effective pharmaceutical molecules are still one of the major obstacle in theformulation of such molecules. Candesartan cilexetil (CC) is angiotensin II receptor antagonist with very low water solubility and this result in low and variable bioavailability. Self- emulsifying drug delivery system (SEDDS) showed promising result in overcoming solubility problem of many drug molecules. CC was prepared as SEDDS by using novel combination of two surfactants (tween 80 and cremophore EL) and tetraglycol as cosurfactant, in addition to the use of triacetin as oil. Different tests were performed in order to confirm the stability of the final product which includes thermodynamic study, determination of self-emulsificat
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A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.
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Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration
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This case series aims to evaluate patients affected with post COVID‐19 mucormycosis from clinical presentation to surgical and pharmacological treatment to improve the disease prognosis.
This case series was conducted at a specialized surgery hospital in Baghdad Medical City for over 10 months. Fifteen cases who had mild to severe COVID‐19 infections followed by symptoms similar to aggressive periodontitis, such as mobility and bone resorption around the multiple maxillary teeth, were included in this case series.
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