Magnetic Field Assistedand Flow Injection μ-Solid Pahse Extraction Techniques

, M.Sc. Thesis Sharif University of Technology Khanipour Mehrin, Peyman (Author) ; Bagheri, Habib (Supervisor)

Abstract

The main goal of this thesis was to develop μ-solid extraction techniques using electrspun nanofibers under the influence of magnetic field along with flow injection system. The thesis is divided into two sections:The first chapter deals with the use of magnetic field for improving μ-solid phase extraction efficiencies. After preparing the magnetic-based electrospun nanofibers (PBt/MNPs), sufficient numbers of disc from the prepared electrospun composite mat were cut and used as packing materials in a cartridge. The prepared system was coupled on-line to liquid chromatography for microextraction of selected drugs of furosemide, naproxene, diclofenac and clobetasol propionate. The homogeneity...

Loading of Drug and Nanostructured Coating on Dental Implant

, M.Sc. Thesis Sharif University of Technology Abbaspour Ghomi, Somayya (Author) ; Sadrnezhaad, Khatiboleslam (Supervisor)

Abstract

The aim of this project is to load analgesic drug; Paracetamol on dental implant. The implant is titanium alloy (Ti-6Al-4V). There are two kinds of samples of anodized and HA coated onto anodized. They are in the shape of the sheets in this study. The electrodeposition and anodization carried out in order to treat the two samples. Nanotubes were formed during anodic oxidation of the samples in the 1M Ammonium sulfate (NH₄)₂SO4 and 5wt% Ammonium fluoride NH4F electrolyte. They are expected to play role of carrier for the model drug; paracetamol. The results showed that HA anodized Ti-6-4 has the ability to hold higher amounts of drug and also can keep the drug for a longer time than the...

Drug-Target Binding Affinity Prediction Based on Machine Learning Algorithms and Features Extraction using Neural Networks

, M.Sc. Thesis Sharif University of Technology Belal Yengi Kand, Alireza (Author) ; Koohi, Somayyeh (Supervisor)

Abstract

One of the fundamental steps in drug discovery and development is predicting the binding affinity of the drug to target proteins. Due to the high costs, slow pace, and lack of structural data in experimental approaches, several machine learning and deep learning-based methods have been proposed to date. Machine learning-based methods, which have shown high prediction capabilities, require precise engineering and selection of a wide range of physicochemical, biological, and structural information about proteins and drugs. On the other hand, deep neural network-based methods require the design of numerous complex networks for automatic feature extraction. Therefore, a method that can utilize...