Invasomes are newly developed types of nanovesicles. A vesicular drug delivery system is considered one of the approaches for transdermal delivery to enhance permeation and improve drug bioavailability. Ondansetron is a serotonin receptor antagonist used for treating vomiting associated with different clinical cases. The study aimed to prepare invasomal dispersions for improving permeation of ondansetron across the skin with a controlled release pattern. Twenty-seven formulas of ondansetron-loaded invasomes were prepared by a modified mechanical dispersion method. These formulas were optimized by studying the effect of variables on entrapment efficiency. Vesicle size, polydispersity, zeta potential, in-vitro release and ex-vivo perm
... Show MoreThe study aimed to estimate the content of lead and determine the quality of the internal coating of metal cans through electrical conductivity as well as to determine the accuracy of the information card for some types of canned food that available in local markets. The information card test showed that all of these samples contained the name of the food, trade mark, country origin, weight, and components, as was indicated by the company producing in all of them except for the C12 sample which was otherwise, and the batch number was mentioned in all samples except for the C3 and C17 which was not clear and not mentioned in the C21, and the validity period was observed (produce and fini
... Show MoreThis study was conducted in the poultry field of the Department of Animal Production/ College of Agricultural Engineering Sciences / University of Baghdad for the period from 42 days. Aiming to know the effect of using shrimp waste powder (Metapenaeus Affinis) and enzyme in broilers diet on physiological and microbial performance and indicators of fat oxidation in meat. 250 one-day-old ROSS308 chicks were used. The chicks were fed on diets containing shrimp waste treated with enzyme and not treated with protease enzyme by 0,4,6 %. The experiment included five treatments, with 5 replicates for each treatment, and each replicate contained 10 birds. The results showed a significant decrease (P≤0.05) in the concentration of ALT and AS
... Show MoreBackground: Adipose derived-mesenchymal stem cells have been used as an alternative to bone marrow cells in this study. Objective: We investigated the in vitro isolation, identification, and differentiation of stem cells into neuron cells, in order to produce neuron cells via cell culture, which would be useful in nerve injury treatment. Method: Mouse adipose mesenchymal stem cells were dissected from the abdominal subcutaneous region. Neural differentiation was induced using β-mercaptoethanol. This study included two different neural stage markers, i.e. nestin and neurofilament light-chain, to detect immature and mature neurons, respectively. Results: The immunocytochemistry results showed that the use of β-mercaptoethanol resulted in
... Show MoreThis work is a trial to ensure the absolute security in any quantum cryptography (QC) protocol via building an effective hardware for satisfying the single-photon must requirement by controlling the value of mean photon number. This was approximately achieved by building a driving circuit that provide very short pulses (≈ 10 ns) for laser diode -LD- with output power of (0.7-0.99mW) using the available electronic components in local markets. These short pulses enable getting faint laser pulses that were further attenuated to reach mean photon number equal to 0.08 or less.
Semantic segmentation realization and understanding is a stringent task not just for computer vision but also in the researches of the sciences of earth, semantic segmentation decompose compound architectures in one elements, the most mutual object in a civil outside or inside senses must classified then reinforced with information meaning of all object, it’s a method for labeling and clustering point cloud automatically. Three dimensions natural scenes classification need a point cloud dataset to representation data format as input, many challenge appeared with working of 3d data like: little number, resolution and accurate of three Dimensional dataset . Deep learning now is the po
Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essentia
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