The aim of this study is to formulate and evaluate ezetimibe nanoparticles using solvent antisolvent technology. Ezetimibe is a practically water-insoluble drug which acts as a lipid lowering drug that selectively inhibits the intestinal absorption of cholesterol and related phytosterols. Ezetimibe prepared as nano particles in order to improve its solubility and dissolution rate.

Thirty formulas were prepared and different stabilizing agents were used with different concentrations such as poly vinyl pyrrolidone (PVPK-30), poly vinyl alcohol (PVA), hydroxy propyl methyl cellulose E5 (HPMC), and poloxamer. The ratios of drug to stabilizers used to prepare the nanoparticles were 1: 2, 1:3 and 1:4.

The prepared nanoparticles

Background: Antibacterial action of root canal filling is an important factor for successful root canal treatment, so the aim of the study was to identify and to compare the antimicrobial effect of new sealer (GuttaFlow) to commonly used endodontic sealers (AH Plus, Apexit and EndoFill) against four endodontic microbes. Materials and methods: Twenty patients aged (30-40) years with infected root canals were selected. Four types of microorganisms were isolated from root canals (E faecalis, Staphylococcus aureus, E coli and Candida albicans) and cultured on Mueller Hinton agar Petri-dishes. After identification and isolation of bacterial species, agar diffusion method was used to assess the antibacterial action of four contemporary endodontic

A new application of a combined solvent extraction and two-phase biodegradation processes using two-liquid phase partitioning bioreactor (TLPPB) technique was proposed and developed to enhance the cleanup of high concentration of crude oil from aqueous phase using acclimated mixed culture in an anaerobic environment. Silicone oil was used as the organic extractive phase for being a water-immiscible, biocompatible and non-biodegradable. Acclimation, cell growth of mixed cultures, and biodegradation of crude oil in aqueous samples were experimentally studied at 30±2ºC. Anaerobic biodegradation of crude oil was examined at four different initial concentrations of crude oil including 500, 1000, 2000, and 5000 mg/L. Complete removal of crud

This study aims to show the  effectiveness of immobilization of Chlorella green algae biomass in the form of bead for the removal of lead ions from synthetic polluted water  at various operational parameters such as pH (2–6), biosorbent dosage (0.5–20 g/L) and initial concentration (10–100 mg/L). More than 90 % removal efficiency was achieved. FTIR and SEM-EDX analysis of the biosorbent before and after sorption show differences in the functional groups on the adsorbent surface. Langmuir and Freundlich equilibrium isotherm, pseudo-first-order and pseudo-second-order kinetic models were applied to the experimental and results and show good conformity with Langmuir isotherm model and pseudo-second-order kinetic model with c

Lignans are natural products widely distributed in the plant kingdom. They are composed of two β-β-linked phenylpropane (shikimate-derived biogenetic subunits). Although the backbone of lignans is composed of phenylpropane units, there is enormous diversity in the structure of lignans leading to different classes of lignans, such as γ-butyrolactone derivatives, eg. Hymatairesinol, bicyclooctadiene derivatives, e.g. pinoresinol, tetrahydrofuran derivatives e.g.lariciresinol, di-arylbutandiol derivatives, e.g. secoisolariciresinol. Introduction of a further carbon –carbon linkage leads to a class of lignans collectively known as cyclolignans such as tetrahydro-naphthalene derivatives, for example podophyllotoxin. Lignans ha

In this work, a deep computational study has been conducted to assign several qualities for the graph ⁠. Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes