The new of compounds synthesized by sequence reactions starting from a reaction of 3-phenylenediamine or 4-phenylenediamine with chloroacetyl chloride to produce the compounds [I]a,b, then the compounds[I]a,b reacted with sodium azide to yield compounds[II]a,b that reacted 1,3-dipolarcycloaddition reaction with acrylic acid to give compounds [III]a,b these compounds reacted with methanol led to ester compounds[IV]a,b then reacted with hydrazine to give acid hydrazide [V]a,b . Finally compounds [V]a,b reacted with aromatic aldehydes to product shiff bases derivatives. The compounds characterized by mp. , IR, 1HNMR in addition to mass spectroscopy for some of them the liquid crystals properties were studied by using polarized optical microsco

This research include synthesized and characterization the compound [I] by reaction terephthaldehyde , mercaptoacetic acid and thiosemicarbazide with concentrated sulfuric acid then this compound reaction with ethyl chloroacetate and sodium acetate to product ester compound [II],the latter compound reaction with hydrazine hydrate to synthesized acid hydrazide [III] after that reaction with 4-alkoxy benzaldehyde[IV]n to synthesized Schiff bases compounds [V]n, the compound [VI] synthesized via reaction compound [I] with chloroacetic acid and sodium acetate then the compound[VI] reaction with 2-phenylenediamine in 4 N hydrochloric acid to product benzimidazole compound[VII]. The compounds characterized by melting points, FTIR and 1HNMR spectr

A rapid high performance liquid chromatography method for the determination of sphinganine (Sa) and sphingosine (So) in urine samples by employing a silica-based monolithic column is described. The samples were first extracted using ethyl acetate and derivatized using ortho-phthaldialdehyde in the presence of 2-mercaptoethanol. C20 sphinganine was used as internal standard. Under the optimized conditions, separation was achieved using a mixture of methanol:water (93:7, v/v), column temperature at 30°C, flow rate of 1 mL min−1, and an injection volume of 10 μL. Good linearity was obtained for Sa and So over the concentration range 20–500 ng mL−1(correlation coefficients ≥0.9978). The detection limits were 0.45 ng mL−1 for Sa and

      After diagnostic tests on ten isolates of S. marcescence, were made an examination of sensitivity to various antibiotics and checking the ability to produce β-lactamase of all isolates.        The outer membrane protein quantity was determined in μg/cm3 for all isolates. The results showed that S. marcescence have antibiotic multiresistant and all isolates had the ability to produce β-lactamase and its resistance may arise from more than two mechanisms like overproduction of antibiotic inactivation enzymes and the decrease of permeability by outer membrane protein.

The effective surface area of drug particle is increased by a reduction in the particle size. Since dissolution takes place at the surface of the solute, the larger the surface area, the further rapid is the rate of drug dissolution. Ketoprofen     is class II type drug according to (Biopharmaceutics Classification System BCS) with low solubility and high permeability. The aim of this investigation was to increase the solubility and hence the dissolution rate by the preparation of ketoprofen     nanosuspension using solvent evaporation method. Materials like PVP K30, poloxamer 188, HPMC E5, HPMC E15, HPMC E50, Tween 80 were used as stabilizers in perpetration of differ

This work was influenced the separation and preconcentration steps were carried out to determination of metformin (MET) in pharmaceutical preparations and human serum samples. Complex formation method and cloud-point extraction (CPE) coupling with UV-Visible spectrophotometry were used to investigated of study target.The results has showed the best optical characteristic for calibration curve and statistical data which were obtained under optimum conditions. The first method is based on the reaction of MET with nickel (II) in alkaline medium an absorption maximum ?)max) at 434nm. ''Beer's low'' is obeyed in the concentration range (10-100µg.ml-1) with molar absorptivity of 3.9x103 L.mol-1.cm-1.The limit of detection and quantitation valu

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature