Antibiotic resistance increment is a major problem for the human society nowadays which encourages the efforts to look for new therapeutic alternatives from natural defenses. Synergistic antibacterial activity of epidermin and staphylolysin LasA A against Staphylococcus aureus (Staph aureus), Escherichia coli (E. coli) and Pseudomonas aeruginosa (Ps. aeruginosa) was evaluated. The antibacterial activities of epidermin from Staphylococcus epidermidis (Staph epidermidis) and Staphylolysin (LasA) from Ps. aeruginosa using the agar well diffusion assay were evaluated, and then using the micro dilution method to evaluate the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC). The checkerboard method and fractional inhibitory concentration (FIC) were used to evaluate the combination between epidermin and LasA toward targeted clinical isolates of Staph aureus, E. coli and Ps. aeruginosa. The results revealed a synergistic effect between epidermin and LasA on all clinical isolates growth. The highest MIC and MBC of epidermin were 36.04 µL/mL and 51.73 µL/mL against Staph aureus; meanwhile, the highest MIC and MBC of LasA were 44.38 µL/mL and 50 µL/mL against Staph aureus. The FICindex revealed synergistic interactions in combination of epidermin and LasA which recorded 0.286 for Staph aureus while for E. coli was 0.327 and for Ps. aeruginosa was 0.390 respectively showing a synergism effect. This study finds that combination of epidermin with LasA had inhibitory activity on the targeted clinical isolate growth, which can be useful for designing and developing alternative therapeutic strategies against pathogens causing wound and burn infections.
Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a
... Show MoreIn this paper, a new method of selection variables is presented to select some essential variables from large datasets. The new model is a modified version of the Elastic Net model. The modified Elastic Net variable selection model has been summarized in an algorithm. It is applied for Leukemia dataset that has 3051 variables (genes) and 72 samples. In reality, working with this kind of dataset is not accessible due to its large size. The modified model is compared to some standard variable selection methods. Perfect classification is achieved by applying the modified Elastic Net model because it has the best performance. All the calculations that have been done for this paper are in
Breast cancer is the most prevalent malignancy among women worldwide, in Iraq it ranks the first among the population and the leading cause of cancer related female mortality. This study is designed to investigate the correlations between serum and tissue markers in order to clarify their role in progression or regression breast cancer. Tumor Markers are groups of substances, mainly proteins, produced from cancer cell or from other cells in the body in response to tumor. The study was carried out from April 2018 to April 2019 with total number of 60 breast cancer women. The blood samples were collected from breast cancer women in postoperative and pretherapeutic who attended teaching oncology hospital of the medical city in Baghdad and
... Show MoreSoil pH is one of the main factors to consider before undertaking any agricultural operation. Methods for measuring soil pH vary, but all traditional methods require time, effort, and expertise. This study aimed to determine, predict, and map the spatial distribution of soil pH based on data taken from 50 sites using the Kriging geostatistical tool in ArcGIS as a first step. In the second step, the Support Vector Machines (SVM) machine learning algorithm was used to predict the soil pH based on the CIE-L*a*b values taken from the optical fiber sensor. The standard deviation of the soil pH values was 0.42, which indicates a more reliable measurement and the data distribution is normal.