Hydatidosis is a sickness that affects human and farm animals. This disease is deemed as a public health problem in different regions of the world until nowadays. Surgical overlaps is the best way to treat the disease, while the risk of surgery lies in the possibility of cyst rupture and leakage of protoscolices and the recurrence of infection again, this prompted researchers to use scolicidal agents before surgery such as ethanol, plant extracts, to reduce parasite spread and recurrence of infection, recently researchers have been using nanoparticles as a scolicidal agent, like gold nanoparticles, silver nanoparticles, selenium nanoparticles, and others. This research aims to evaluate the fatal effect of zirconium oxide (ZrO2) nanoparticles to protoscolices of hydatid cysts. The Protoscolices were collected from sheep livers infected with hydatid cyst disease. The protoscolices were treated with different concentrations (250, 500, 1000, 2000, and 4000 ?g/ ml) of ZrO2 NPs. The viability of protoscolices was determined by using an eosin staining method after 15, 30, and 60 min. The results showed that the concentrations of 1000, 2000, and 4000 µg/ml were significantly effective in the killing of protoscolices after 60 min., where the fatality rate of protoscolices was 49.6%, 52.7%, and 53.1% respectively when compared with the control group 38.5% (p<0.05).
Biomedical alloy 316L stainless steel enhancing to replace biological tissue or to help stabilize a biological structure, such as bone tissue, enhancing were coated with deposition a thin layer of silver nanoparticles as anti-bacterial materials by using DC- magnetron sputtering device. The morphology surface of The growth nanostructure under the influence of different working pressure were studied by atomic force microscope. The average grain size decrease but roughness of the silver thin layer was increased with‖ ―increasing the working pressure. The thickness of silver thin layer was increased from 107 nm at 0.08 mbar to 126 nm at 1.1 mbar. Antimicrobial activity of silver thin layers at different working pressure were studied. Th
... Show MoreMetal (III) and (II) coordination compounds of o- phenylenediamine, oxalic acid dihydrate and 8-hydroxyquinoline were synthesized for mixed ligand complexes and characterized using FT-IR, UV-Vis and mass spectra, atomic absorption, elemental analysis, electric conductance and magnetic susceptibility measurements. In addition, thermal behavior (TGA) of the metal complexes (1-6) showed good agreement with the formula suggested from the analytical data. The stoichiometric reaction between the metal (III) and (II) ions with three various ligands in molar ratio at aqueous ethyl alchol for (1:1:1:1) (M: O-PDA: OA: 8-HQ) [where M = Cr+3, Mn+2, Co+2, Ni+2. Cu+2 and Zn+2; O-PDA = O-Phenylenediamine; OA = Oxalic acid and 8-HQ = 8-Hydroxyquinoline]. R
... Show MoreThe present study was conducted to estimate the antimicrobial activity and the potential biological control of the killer toxin produced by
Metal (III) and (II) coordination compounds of o- phenylenediamine, oxalic acid dihydrate and 8-hydroxyquinoline were synthesized for mixed ligand complexes and characterized using FT-IR, UV-Vis and mass spectra, atomic absorption, elemental analysis, electric conductance and magnetic susceptibility measurements. In addition, thermal behavior (TGA) of the metal complexes (1-6) showed good agreement with the formula suggested from the analytical data. The stoichiometric reaction between the metal (III) and (II) ions with three various ligands in molar ratio at aqueous ethyl alchol for (1:1:1:1) (M: O-PDA: OA: 8-HQ) [where M = Cr+3, Mn+2, Co+2, Ni+2. Cu+2 and Zn+2; O-PDA = O-Phenylenediamine; OA = Oxal
This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANF
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