The target of this study was to synthesize several new Ciprofloxacin drug analogs by providing a nucleophilic substitution procedure that provides new functionality at the carboxylic group location. The analogs were synthesized, designed, and characterized by 1HNMR, and FTIR. The synthetic path began from the reaction of ciprofloxacin drug with morpholine to give compound[B], ciprofloxacin derivative was linked with a variety of primary and secondary amines to give compounds[B1-B9]. The above-mentioned prepared compounds [B3 and B5] were applied to liver enzymes, and the increase in the activity of these enzymes was observed. In addition, a theoretical study was conducted to study the energies and properties of the prepared compounds.

The target of this study was to synthesize several new Ciprofloxacin drug analogs by providing a nucleophilic substitution procedure that provides new functionality at the carboxylic group location. The analogs were synthesized, designed, and characterized by ¹HNMR, and FTIR. The synthetic path began from the reaction of ciprofloxacin drug with morpholine to give compound[B], ciprofloxacin derivative was linked with a variety of primary and secondary amines to give compounds[B1-B9]. The above-mentioned prepared compounds [B3 and B5] were applied to liver enzymes, and the increase in the activity of these enzymes was observed. In addition, a theoretical study was conducted to study the energies and properties of the prepared co

An increasing interest is emerging in identifying natural products to overcome drug resistance in cancer patients. In this context, the present study was conducted to investigate the cytotoxic effects of neem plant (Azadirachta indica) oil in three different biological models (breast cancer cell lines, Allium cepa root tip, and mice vital organs). The cytotoxic potential of the neem oil was evaluated with two human cell lines (MCF7 and MDA-MB231) and an Allum cepa root tip bioassay. Histopathological analysis was conducted on the neem oil-treated and untreated control mice. The results revealed an anti-proliferative effect for neem oil on both estrogen receptor-positive (MCF7) and estrogen receptor-negative (MDA-MB231) breast cancer cell li

The High Modulus Asphalt Concrete Mixture (HMACM) or (EME) (Enrobes a Module Eleve) developed in France, since, 1980 by Laboratories Central des Ponts et Chaussees (LCPC). Due to the increasing in traffic intensity and axle loading this type of mixing were suitable for pavement subjected to heavy duty. Experiments showed that EME mixtures have an excellent moisture damage resistance permanent deformation, fatigue cracking and reducing costs of maintenance and a significant reduction in thickness of pavement. Because of the high stiffness of EME mixes, the stresses transformed to the bottom laid layer by repeated traffic wheel loads were reduced effectively. This study intend to focus the light into the possibility of producing asphalt mixtu

    Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it