Abstract

The aim of this study was to prepare rebamipide ocular inserts in order to extend its release on the ocular surface for dry eye treatment. Solubility study was applied to the drug with or without l-arginine using different solvents. Solvent casting technique was used to prepare the inserts; l-arginine was used to solubilize the drug, hydroxypropyl methylcellulose grades (E5 and K15M) and poly ethylene glycol 200 were used as excipients. The inserts were evaluated for their physical and mechanical properties, moisture loss% and absorption %, surface pH, and in-vitro drug release. The use l-arginine exhibited an enhancement of rebamipide solubility in both deionized water and phosphate buffer (pH 7.4) by a

A simple, rapid spectrophotometric method has been established for the determination of chlorpromazine hydrochloride (CPZ) in its pure form and in a tablet formulations. The  suggested  method  is  based  on  the  oxidative coupling  reaction  with4-nitroainlline using KIO₃ in acidic solution to produce a violet colored product with maximum absorption at λ=526 nm.The  analytical data  obtained  throughout  this  study  could  be  summarid  as  follows:  1ml of 1M HCl (pH=2.2), 1 ml  of  4-nitroanilline (1x10^-2M), and 1.5ml  of (1x10^-2)KIO₃ per 25 ml reaction medium. The order of a

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of t

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample