Sharif Digital Repository

The cellular microenvironment can be engineered through the utilization of various nano-patterns and matrix-loaded bioactive molecules. In this study, a multilayer system of electrospun scaffold containing chitosan nanoparticles was introduced to overcome the common problems of instability and burst release of proteins from nanofibrous scaffolds. Bovine serum albumin (BSA)-loaded chitosan nanoparticles was fabricated based on ionic gelation interaction between chitosan and sodium tripolyphosphate. Suspension electrospinning was employed to fabricate poly-e{open}-caprolacton (PCL) containing protein-loaded chitosan nanoparticles with a core-shell structure. To obtain the desired scaffold... 

In this paper fabrication of a 0-3 ceramic/ceramic composite lead zirconate titanate, Pb(Zr0.52Ti0.48)O3 thin film has been presented and then a pressure sensor based on multilayer thin-film PZT diaphragm contain of Lead Zirconate Titanate nanocrystalline powders was designed, modeled and optimized. This multilayer diaphragm in general acts as sensor or actuator. ANSYS was used for simulation of diaphragm. Dynamics characteristics of this multilayer diaphragm have been investigated. By this simulation the effective parameters of the multilayer PZT diaphragm for improving the performance of a pressure sensor in different ranges of pressure are optimized. The optimized thickness ratio of PZT... 

Purpose: Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in radiation therapy, MRI-guided radiation treatment planning is limited by the fact that MRI does not directly provide the electron density map required for absorbed dose calculation. In this work, a new deep convolutional neural network model with efficient learning capability, suitable for applications where the number of training subjects is limited, is proposed to generate accurate synthetic computed tomography (sCT) images from MRI. Methods: This efficient convolutional neural network (eCNN) is built upon a combination of the SegNet architecture (a 13-layer encoder-decoder structure similar to the... 

In this study, a novel artificial intelligence-based approach is presented to directly estimate the surface topography. To this aim, performance of different artificial intelligence-based techniques, including the multi-layer perceptron neural, radial basis function neural, and adaptive neural fuzzy inference system networks, in estimation of the sample topography is investigated. The results demonstrate that among the designed observers, the multi-layer perceptron method can estimate surface characteristics with higher accuracy than the other methods. In the classical imaging techniques, the scanning speed of atomic force microscope is restricted due to the time required by the oscillating... 

Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients’ features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis... 

Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for...