Background: The aim of this study was to measure the radiopacity (RO) of modified microhybrid composite resins by adding 2 types of nanofillers (Zinc Oxide and Calcium Carbonate) in two concentrations 3% and 5% and comparing them to unmodified microhybrid composite resins and to nanofilled composite resin. Materials and Methods: Two types of composite resin were used (Microhybrid composite MH Quadrent anterior shine and Nanofilled composite resin Filtek Z350 XT), for each tested group five disk-shaped specimens (1-mm-thick and 15 mm diameter) were fabricated. The material samples were radiographed together with the aluminum step wedge. The density of the specimens was determined with a transmission densitometer and was expressed in term of equivalent thickness of aluminum. Data analyzed by one-way ANOVA. Results: The radiopacity (RO) values of the tested group ranged between (0.9293- 2.6242 Eq. Al thickness) and there were significant differences among them. Nanofilled composite resin Filtek Z350 XT showed the highest value of RO while unmodified Microhybrid composite MH Quadrent anterior shine showed the lowest value of RO. Conclusion: The addition of 3% of both the ZnO and CaCO3 nanofillers fillers to microhybrid composite significantly increased the RO, while the addition of 5% of CaCO3 and ZnO nanofillers to microhybrid composite showed non-significant increase in the RO of the composite.
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FG Mohammed, HM Al-Dabbas, Science International, 2018 - Cited by 2
The data presented in this paper are related to the research article entitled “Novel dichloro(bis{2-[1-(4-methylphenyl)-1H-1,2,3-triazol-4-yl-κN3 ]pyridine-κN})metal(II) coordination compounds of seven transition metals (Mn, Fe, Co, Ni, Cu, Zn and Cd)” (Conradie et al., 2018) [1]. This paper presents characterization and structural data of the 2-(1-(4-methyl-phenyl)-1H-1,2,3-triazol-1-yl)pyridine ligand (L2 ) (Tawfiq et al., 2014) [2] as well as seven dichloro(bis{2- [1-(4-methylphenyl)-1H-1,2,3-triazol-4-yl-κN3 ]pyridine-κN})metal (II) coordination compounds, [M(L2 )2Cl2], all containing the same ligand but coordinated to different metal ions. The data illustrate the shift in IR, UV/VIS, and NMR (for diamagnetic complexes) peaks wh
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The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when
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