Background: Colonization of soft denture liners by Candida albicans and other microorganisms continued to be a serious problem. The aim of this study was to evaluate the effect of incorporating silver nanoparticles into heat cured acrylic-based soft denture liner on the antifungal activity, and on water sorption, solubility, shear bond strength and color change of the soft lining material. Furthermore, evaluating the amount of silver released. Materials and methods: Silver nanoparticles were incorporated into soft denture liner in different percentages (0.05%, 0.1% and 0.2% by weight). Four hundred and twenty specimens were prepared and divided into five groups according to the test to be performed. The antifungal activity of the soft liner/AgNPs composite was evaluated in three different periods by using (viable count of C. albicans and disk-diffusion test). The amount of silver released in artificial saliva was measured by atomic absorption spectroscopy. The water sorptions, solubility, shear bond strength and color change was measured and the results were statistically analyzed. Results:All experimental groups showed a highly significant decrease in colony forming units of C. albicans in comparison to control group. There was no inhibition zone around any test specimen of any test group. There was no silver detected to be released. The addition of AgNPs resulted in a highly significant decrease in water sorption, while only 0.2% group showed highly significant decrease in solubility. Non significant differences in shear bond strength were found. A highly significant increase in light absorption percentage was observed in all experimental groups. Conclusion: The addition of AgNPs helps to produce soft denture liner with antifungal properties. Silver was not detected to be released. This addition resulted in decrease in water sorption, and did not affect the shear bond strength and it increased the opacity of the material.
The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of
... Show More
(5)
(3)
S Khalifa E, N Adil A, AS Mazin M…, 2008
(43)
(83)
(84)
(2)
(1)
Carbon dioxide geo-sequestration (CGS) into sediments in the form of (gas) hydrates is one proposed method for reducing anthropogenic carbon dioxide emissions to the atmosphere and, thus reducing global warming and climate change. However, there is a serious lack of understanding of how such CO2 hydrate forms and exists in sediments. We thus imaged CO2 hydrate distribution in sandstone, and investigated the hydrate morphology and cluster characteristics via x-ray micro-computed tomography in 3D in-situ. A substantial amount of gas hydrate (∼17% saturation) was observed, and the stochastically distributed hydrate clusters followed power-law relations with respect to their size distributions and surface area-volume relationships. The layer-
... Show More
(62)
(61)
(4)
(4)
Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health
... Show More
(9)
(9)
(64)
(58)
(6)
(3)