Swarming is one of the most important virulence factors used by bacteria to invade new sites. This study aimed to test the effects of gentamicin on swarming motility of Pseudomonas aeruginosa, both phenotypically and molecularly. The present results revealed that 11/25 isolates had gentamicin MIC of 1024 µg/ml.  However, gentamicin at sub-minimal inhibitory concentration significantly (P< 0.05) reduced the diameter of swarming in all P. aeruginosa isolates. Noticeably the mean and median swarming diameter before treatment with gentamicin 5.557 and 5.816 cm respectively had significantly (P < 0.001) reduced to 0.871 and 0.766 cm respectively. At the molecular level, amrZ (a global regulator

A simple, fast, and sensitive spectrophotometric method was suggested for the determination of Bromhexine Hydrochloride (BHH) in its pharmaceutical formulations. The method depends on the diazotization of BHH by sodium nitrite in acidic medium to produce the corresponding diazonium salt. The latter is coupled with phloroglucinol reagent in alkali medium to form a yellow water soluble azo-dye which has a maximum absorption at 405 nm with a molar absorptivity of 2.7×10⁴ l.mol^-1.cm^-1 and Sandellʼs sensitivity of 0.01517 µg.cm^-1. Beerʼs low is obeyed within a concentration range of 0.25-15 µg.mL^-1 of BHH. The LOD and LOQ values of the proposed method were 0.087 µg.mL

Diagenetic processes and types of pores that control the reservoir properties are studied for Mauddud Formation in selected wells of Badra oil field, central Iraq. The microscopic study of the thin sections shows the effects of micritization, cementation, neomorphism, dissolution, dolomitization, compaction, and fracturing on Mauddud Formation carbonate microfacies. The decrease of porosity is resulted from cementation, compaction, and neomorphism. Different types of calcite cement occlude pore spaces such as drusy cement, syntaxial rim cement, and granular (blocky) cement. The neomorphism of micritic matrix and skeletal grains reduces porosity as indicated by development of microspar or pseudospar. Evidence of decreasing porosity by com

This study includes using green or biosynthesis-friendly technology, which is effective in terms of low cost and low time and energy to prepare V₂O₅NPs nanoparticles from vanadium sulfate VSO₄.H₂O using aqueous extract of Punica Granatum at a concentration of 0.1M and with a basic medium PH= 8-12. The V₂O₅NPs nanoparticles were diagnosed using several techniques, such as FT-IR, UV-visible with energy gap Eg = 3.734eV, and the X-Ray diffraction XRD was calculated using the Debye Scherrer equation. It was discovered to be 34.39nm, Scanning Electron Microscope (SEM), Transmission Electron Microscopy TEM. The size, structure, and composition of synthetic V₂O₅

The study aimed to identify the degree of teaching competencies and job satisfaction of the teachers of physical education in the province of Baghdad, and then find the relationship between them. Use the descriptive approach in the style of associative relationships, the research community represented by teachers of physical education in the departments of education Karh (first, second, third) in the province of Baghdad. (522) teachers and (324) school principals distributed in (324) medium within the administrative configurations of these directorates for the academic year (2016 - 2017). The measure (teaching competencies) included (47) words distributed across seven areas. And the measure of (job satisfaction) included (15) words. After t

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c