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

    An investigation was provided in this work for the host range of brown soft scale Coccus hesperidum Linnaeus in Baghdad Province.  Five plant species were found infected by this insect, three of these species, Citrusaurantium L. (Rutaceae); Nerium oleander L. (Apocynaceae); Ficuscarica L. (Moraceae) reported earlier, and the remaining two, Dahlia pinnata Cav. (Asteraceae) and Myrtuscommunis L. (Myrtaceae) are recordedhere for the first time as host plants for this pest.

Heterogeneous organic compounds play an important role in our daily life as they contribute in many medical and industrial fields and are in continuous development as a result of the preparation of new derivatives with different properties. From this premise, the goal of this work appears, which is preparation of (four, five, six, and seven) membered ring systems derived from furfural, by its reaction with different aromatic aldehydes, and record their antioxidant activity by using free radical scavenging method of DPPH radicals. The new ring systems are synthesized by reacting the prepared Schiff-bases with different ring closure agents (chloroacetyl chloride, mercaptoaceticacid, anthranilic acid, and phthalic anhydride), the prep

Atorvastatin calcium (ATR) is an antihyperlipidemic agent used for lowering blood cholesterol levels. However, it is very slightly soluble in water with poor oral bioavailability, which interferes with its therapeutic action. It is classified as a class II drug according to Biopharmaceutical Classification System (low solubility and high permeability).

ZSM-5 zeolite was synthesis under hydrothermal conditions at 175^oC. The synthesis parameters have been investigated to find optimum synthesis method. Firstly, the crystallization time has been investigated to find the optimum crystallization time. Also, the ageing time was studied. The morphology, structure, and composition of the synthesized ZSM-5 zeolite were characterized using scanning electron microscopy (SEM), pH meter, viscometer, and X-ray powder diffraction (XRD). The bulk Si/Al ratio of ZSM-5 zeolite was in the range of 9.5—11.7. The synthesized ZSM-5 zeolite with appropriate ageing time could adjust crystal size and degree of the crystallinity. The crystal size of ZSM-5 zeolite obtained at an agei

Urea formaldehyde resin was prepared by using basic media by yield 95%. The Remaining of ureaplasts resin were prepared in acetic acid media by high yield. Alkyde resins were prepared by condensation polymerization by react Succinic, Maleic, Phthalic anhydrides with Ethylene glycol or Glycerol. Select samples of the prepared alkyde resins were mixed with Azo dyes in special ratio. The mixtures were used as coatings for wood, and compaised with pure dyes. The Coating that some alkyde resins showed better adhesion from using dyes alone. Preparation of wood coating by mixing ureaplast resins and alkyde resins with Azo dyes in special ratios. The coating showed better adhesion, brighter colors and better resistance to heat from Preceding coat

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea