Determination of methyl gallate in Bauhinia retusa extract by high-performance liquid and thin-layer chromatography

Two stability-indicating chromatographic methods are reported for the determination of methyl gallate in crude extracts of Bauhinia retusa. Separation by high performance thin layer chromatography was conducted on silica gel aluminum sheets using 9.5:0.5:0.2 (v/v/v) chloroform:methanol:acetic acid at 280 nm. The results from the 2–40 µg/band were used to prepare a linear calibration graph. The limits of detection and quantitation were 0.5 and 1.5 µg/band, respectively. The reverse phase high performance liquid chromatographic isolation of methyl gallate was performed at ambient temperature with an injection volume of 10 μL. The mobile phase consisted of 40:60 (v/v) methanol:0.1% ortho-phosphoric acid. The separation was performed at 1 mL/min using a detection wavelength of 280 nm. The calibration graph for methyl gallate was rectilinear from 0.02–40 µg/mL with limits of detection and quantitation of 0.004 and 0.010 µg/mL, respectively. For both methods, intra-day and inter-day precision were evaluated and the relative standard deviation was less than 2%, indicating good precision. The robustness was evaluated by making small and deliberate changes to appropriate parameters and the calculated relative standard deviation was less than 2%.The chromatographic methods were employed to determine methyl gallate in crude Bauhinia retusa extracts.     

A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest were used to create the classifier models that will help to predict breast cancer. Two different experiments were conducted using three datasets: Gene expression (GE), deoxyribonucleic acid (DNA) methylation, and a combination of the two. Six measures were used to evaluate the performance of the proposed algorithm, which include the following: Accuracy, precision, recall, specificity, area under the curve (AUC), and execution time. The effectiveness of the proposed classifiers was evaluated through comprehensive experiments. The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time. High accuracy values of 99.77%, 99.45%, and 99.45% have been achieved by SA-SVM for GE, DNA methylation, and the combined datasets, respectively. The execution time of the proposed approach was significantly reduced, in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM, which was 0.02, 0.03, and 0.02 on GE, DNA methylation, and the combined datasets respectively. In regard to precision and specificity, SA-RF obtained the best result of 100 on GE dataset. While SA-SVM attained the best recall result of 100 on GE dataset.     

DETERMINATION OF CINACALCET HYDROCHLORIDE BY CAPILLARY ELECTROPHORESIS WITH PHOTODIODE ARRAY DETECTION

This article reports a validated stability-indicating capillary electrophoresis method using a photodiode array detector at 220 nm for the determination of cinacalcet hydrochloride. The best electrophoretic separation between the analyte and internal standard (lamotrigine) was achieved within 5 min in a deactivated fused silica capillary (55 cm effective length × 75 µm internal diameter) maintained at 24°C using a background electrolyte solution consisted of phosphate buffer (50 mM, pH 6.4):methanol (95:5, v/v) at a separation voltage of 30 kV. The linear range of the method was 0.5–30 µg/mL (r = 0.9999) with limits of detection and quantitation of 0.1 and 0.5 µg/mL, respectively. The assay precision and accuracy were favorable as the relative standard deviations did not exceed 1.09%, and the recovery values were 98.99–100.33 ± 0.19–1.09%. The induced degradation products, when any, did not interfere with the detection of analyte. The proposed method was successfully applied for the determination of cinacalcet hydrochloride in bulk and pharmaceutical formulations; the percentage recovery values were 98.16–102.00 ± 0.24–1.08%. The results demonstrated the value of the method.     

IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

Medical data classification (MDC) refers to the application of classification methods on medical datasets. This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis. To gain experts’ trust, the prediction and the reasoning behind it are equally important. Accordingly, we confine our research to learn rule-based models because they are transparent and comprehensible. One approach to MDC involves the use of metaheuristic (MH) algorithms. Here we report on the development and testing of a novel MH algorithm: IWD-Miner. This algorithm can be viewed as a fusion of Intelligent Water Drops (IWDs) and AntMiner+. It was subjected to a four-stage sensitivity analysis to optimize its performance. For this purpose, 21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine. Interestingly, there were only limited differences in performance between IWD-Miner variants which is suggestive of its robustness. Finally, using the same 21 datasets, we compared the performance of the optimized IWD-Miner against two extant algorithms, AntMiner+ and J48. The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner, as confirmed by the Wilcoxon nonparametric statistical test. Results suggest that IWD-Miner is more efficient than AntMiner+ as measured by the average number of fitness evaluations to a solution (1,386,621.30 vs. 2,827,283.88 fitness evaluations, respectively). J48 exhibited higher accuracy on average than IWD-Miner (79.58 vs. 73.65, respectively) but produced larger models (32.82 leaves vs. 8.38 terms, respectively).     

Effect of chlorination on the assessment of waste engine oil modified asphalt binders

The main objective of this study is to develop a new asphalt binder using waste engine oil (WEO) and improve its properties especially the resistance to rutting, reduces thermal susceptibility as well as the solubility in kerosene. The properties of WEO binders were modified further by chlorine. All blends were evaluated using Fourier transform infrared (FT–IR) spectroscopy, Thermal gravimetric analysis (TGA) differential scanning calorimetry (DSC) and dynamic shear rheometer (DSR). Asphalt binder specimens containing different concentrations (0, 2, 4, 6, and 8) % of waste engine oil with and without chlorine gas were fabricated.