The cytotoxic effect of catechol was examined in two human cancer cell lines, Epidermoid larynx carcinoma (Hep- 2), Cerebral glioblastoma multiforme (AMGM-5) and Murine mammary adenocarcinomacell (AMN3) treated with half concentrations of catechol (1000, 500, 250, 125, 62.5 and 32.25 μM) for 72 hr. The get hold of results showed catechol have a toxic effect of the cell viability of three types of cell lines after 72h of exposure, the toxicity was dependent on catechol concentrations and/or autoxidation for quinines formation, there were a marked decreased of cell viability in a dose dependent manner in all cell line types. Inhibition concentration of catechol for 50% of cell viability (IC50) were calculated, they were at 581.5 μM, 478 μM and 833 μM of HEP-2, AMGM-5 and AMN3 cells, respectively. In addition the combination affects of three cell lines treated with catechol (previously treated at three concentrations close to IC50 were125, 250 and 500 μM for cell lines were incubated for 24 hr.) were assay with 100ul superoxide Dismutase (SOD) , 500 ul peroxidase (POD) and theirs combination (100ul SOD and 500 ul POD) and 125 or 250 μg/mL of catechin against the toxicity of catechol in cell lines to estimated the reduction in quinine formation in these combinations, most inhibition rate of quinine formation display at 100ul SOD alone and combination with 500 ul POD in three cell lines in comparison to other treatments dependant to quinione formation in cells treated with catechol only. Higher percentage in inhibition rate of quinine formation were record in combination treatment of SOD & POD with three concentrations of catechol in AMGM5 cells (54.2%, 59.2% and 65%), followed Hep-2 (44.2%, 42.6% and 52.7%) and AMN3 in 38.7%, 46.6% and 50.7%, respectively, furthermore the less efficient protect obtained in treatment all cell lines with two concentrations of catechins.
Graphite coated electrodes (GCE) based on molecularly imprinted polymers were fabricated for the selective potentiometric determination of Glibenclamide (Glb). The molecularly imprinted (MIP) and nonimprinted (NIP) polymers were synthesized by radical bulk polymerization using (Glb.) as a template, acrylic acid (AA) and acrylamide (AAm) as monomers, ethylene glycol dimethacrylate (EGDMA) as a cross-linker and benzoyl peroxide (BPO) as an initiator. The imprinted membranes and the non-imprinted membranes were prepared using dioctyl phthalate (DOP) and Dibutylphthalate (DBP) as plasticizers in PVC matrix. The membranes were coated on graphite electrodes. The MIP electrodes using (AA) and (AAm) showed a near nernstian response with slopes o
... Show MoreThe mineralogical study using X-ray diffraction (XRD) supported by scanning electron microscopic (SEM) examination and energy-dispersive spectroscopy (EDS) on the claystone of the Kolosh Formation from northern Iraq was conducted to Shows the provenance history of rocks. Chlorite, montmorillonite, illite, palygorskite, and kaolinite were recorded in different amounts in the study area. The association of montmorillonite and chlorite in the claystone of the Kolosh Formation (Paleocene) refers to the marine environment. Chlorite and montmorillonite are the common minerals in the Kolosh Formation with less common of illite, kaolinite and palygorskite. These clay minerals are of authigenic, detrital and diagenetically origin, which
... Show MoreWe are used Bayes estimators for unknown scale parameter when shape Parameter is known of Erlang distribution. Assuming different informative priors for unknown scale parameter. We derived The posterior density with posterior mean and posterior variance using different informative priors for unknown scale parameter which are the inverse exponential distribution, the inverse chi-square distribution, the inverse Gamma distribution, and the standard Levy distribution as prior. And we derived Bayes estimators based on the general entropy loss function (GELF) is used the Simulation method to obtain the results. we generated different cases for the parameters of the Erlang model, for different sample sizes. The estimates have been comp
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
... Show MoreThe aim of this paper is adopted to give an approximate solution for advection dispersion equation of time fractional order derivative by using the Chebyshev wavelets-Galerkin Method . The Chebyshev wavelet and Galerkin method properties are presented. This technique is used to convert the problem into the solution of linear algebraic equations. The fractional derivatives are described based on the Caputo sense. Illustrative examples are included to demonstrate the validity and applicability of the proposed technique.
Background: Cystinosis is a rare autosomal recessive lysosomal storage disease with high morbidity and mortality. It is caused by mutations in the CTNS gene that encodes the cystine transporter, cystinosin, which leads to lysosomal cystine accumulation. It is the major cause of inherited Fanconi syndrome, and should be suspected in young children with failure to thrive and signs of renal proximal tubular damage. The diagnosis can be missed in infants, because not all signs of renal Fanconi syndrome are present during the first months of life. Elevated white blood cell cystine content is the cornerstone of the diagnosis. Since chitotriosidase (CHIT1 or chitinase-1) is mainly produced by activated macrophages both in normal and inflammator
... Show MoreCodes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de
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