HPLC method development for the simultaneous analysis of amlodipine and valsartan in combined dosage forms and in vitro dissolution studies

A simple, rapid and reproducible HPLC method was developed for the simultaneous determination of amlodipine and valsartan in their combined dosage forms, and for drug dissolution studies. A C18 column (ODS 2, 10 μm, 200 x 4.6 mm) and a mobile phase of phosphate buffer (pH 3.6 , 0.01 mol L-1):acetonitrile: methanol (46:44:10 v/v/v) mixture were used for separation and quantification. Analyses were run at a flow-rate of 1 mL min-1 and at ambient temperature. The injection volume was 20 μL and the ultraviolet detector was set at 240 nm. Under these conditions, amlodipine and valsartan were eluted at 7.1 min and 3.4 min, respectively. Total run time was shorter than 9 min. The developed method was validated according to the literature and found to be linear within the range 0.1 - 50 μg mL-1 for amlodipine, and 0.05 - 50 μg mL-1 for valsartan. The developed method was applied successfully for quality control assay of amlodipine and valsartan in their combination drug product and in vitro dissolution studies.Desenvolveu-se método de HPLC rápido e reprodutível para a determinação simultânea de anlodipino e valsartana em suas formas de associação e para os estudos de dissolução dos fármacos. Utilizaram-se coluna C18 (ODS 2, 10 μm, 200 x 4,6 mm) e fase móvel tampão fosfato (pH 3,6, 0,01 mol L-1):acetonitrila: metanol para a separação e a quantificação. As análises foram efetuadas com velocidade de fluxo de 1 mL min-1 e à temparatura ambiente O volume de injeção foi de 20 μL e utilizou-se detector de ultravioleta a 240 nm. Sob essas condições, anlodipino e valsartana foram eluídas a 7,1 min e 3,4 min, respectivamente. O tempo total de corrida foi menor que 9 min. O método desenvolvido foi validado de acordo com a literatura e se mostrou linear na faixa de 0,1-50 μg mL-1 para anlodipino e de 0,05-50 μg mL-1 para valsartana. O método desenvolvido foi aplicado com sucesso para ensaios de controle de qualidade de associações de anlodipino e valsartana e nos estudos de dissolução in vitro

Multistage classifier-based approach for Alzheimer's disease prediction and retrieval

The most prevalent and common type of dementia is Alzheimer's disease (AD). However, it is notable that very few people who are suffering from AD are diagnosed correctly and in a timely manner. The definite cause and cure of the disease are still unavailable. The symptoms might be more manageable and its treatment can be more effective, when the impairment is still at an earlier stage or at MCI (mild cognitive impairment). AD can be clinically diagnosed by physical and neurological examination, so there is an need for developing better and efficient diagnostic tools for AD. In recent years, content-based image retrieval (CBIR) systems have been widely researched and applied in many medical applications. Combining an automated image classification system and the radiologist's professional knowledge, to increase the accuracy of prediction and diagnosis, were the main motives. In this paper, a multistage classifier using machine learning, including Naive Bayes classifier, support vector machine (SVM), and K-nearest neighbor (KNN), was used to classify Alzheimer's disease more acceptably and efficiently. For this, MRI (Magnetic resonance imaging) scans were processed by FreeSurfer, a powerful software tool suitable for processing and normalizing brain MRI images. We also applied a feature selection technique - PSO (particle swarm optimization) to many feature vectors in order to obtain the best features that represent the salient characteristics of AD. The results of the proposed method outperform individual techniques in a benchmark database provided by the Alzheimer's Disease Neuroimaging Institute (ADNI). Keywords: Alzheimer's disease, Machine learning, Content-based image retrieval, Multistage classifier, PSO, Structural MRI, SVM, K-N

Cluster pre-existence probability

Pre-existence probability of the fragments for the complete binary spectrum of different systems such as 56Ni, 116Ba, 226Ra and 256Fm are calculated, from the overlapping part of the interaction potential using the WKB approximation. The role of reduced mass as well as the classical hydrodynamical mass in the WKB method is analysed. Within WKB, even for negative Q -value systems, the pre-existence probability is calculated. The calculations reveal rich structural information. The calculated results are compared with the values of preformed cluster model of Gupta and collaborators. The mass asymmetry motion is shown here for the first time as a part of relative separation motion

RECOGNITION OF MULTI-VIEW HUMAN FACES BASED ON MACHINE INTELLIGENCE USING KLT ALGORITHM

Nowadays Image Processing has become a proficient domain due to the prolific techniques like face detection and face recognition. They play an important role in our society due to their use in wide range of applications such as surveillance, security, banking, and multimedia. One of major challenges faced in this technique of face recognition is difficulty in handling arbitrary pose variations in three dimensional representations. In video retrieval system, many approaches have been developed for recognition across pose variations and to assume the face poses to be known. These constraints made it semi-automatic. In this paper we propose a fully automatic method for multi-view face recognition of improving the accuracy or efficiency using local binary patterns. It uses tree-based data structure to create sub-grids. In this system we use KLT algorithm to detect and extract features automatically by using Eigen vectors and estimation of hessian value.</jats:p