Sharif Digital Repository

Injectable hydrogels are a promising treatment option for nervous system injuries due to the difficulty to replace lost cells and nervous factors but research on injectable conductive hydrogels is limited and these scaffolds have poor electromechanical properties. This study developed a chitosan/beta-glycerophosphate/salt hydrogel and added conductive aligned nanofibers (polycaprolactone/gelatin/single-wall carbon nanotube (SWCNT)) for the first time and inspired by natural nerve tissue to improve their biochemical and biophysical properties. The results showed that the degradation rate of hydrogels is proportional to the regrowth of axons and these hydrogels’ mechanical (hydrogels without... 

Since the beginning of the COVID-19 pandemic, nearly most confirmed cases develop respiratory syndromes. Using targeted drug delivery by microcarriers is one of the most important noteworthy methods for delivering drugs to the involved bronchi. This study aims to investigate the performance of a drug delivery that applies microcarriers to each branch of the lung under the influence of a magnetic field. The results show that by changing the inlet velocity from constant to pulsatile, the drug delivery performance to the lungs increases by ∼31%. For transferring the microcarriers to the right side branches (LUL and LLL), placing the magnet at zero height and ∼30° angle yields the best outcome.... 

Cancer is one of the most significant causes of death in the world. It has been shown that the role of circulating tumor cells (CTCs) in the early detection of cancer is crucial. Since the number of these cancerous cells in blood is very rare, the inertial microfluidic devices are one of the best candidates for the isolation of CTCs because they result in a high throughput process. Consequently, they can process a large volume of blood in a short time. Despite extensive computational and experimental studies on inertial microfluidic platforms, the impact of the curvature has not been thoroughly investigated during separation. In this paper, the feasibility of isolation of CTCs for... 

Background: In nuclear medicine application often it is required to use computational methods for evaluation of organ absorbed dose. Monte Carlo simulation and phantoms have been used in many works before. The shape, size and volume in organs are varied, and this variation will produce error in dose calculation if no correction is applied. Materials and Methods: A computational framework for constructing individual phantom for dosimetry was performed on five liver CT scan data sets of Japanese normal individuals. The Zubal phantom was used as an original phantom to be adjusted by each individual data set. This registration was done by Spherical Harmonics (SH) and Thin-Plate Spline methods.... 

A computational framework is presented, based on statistical shape modelling, for construction of race-specific organ models for internal radionuclide dosimetry and other nuclear-medicine applications. This approach was applied to the construction of a Japanese liver phantom, using the liver of the digital Zubal phantom as the template and 35 liver computed tomography (CT) scans of male Japanese individuals as a training set. The first step was the automated object-space registration (to align all the liver surfaces in one orientation), using a coherent-point-drift maximum-likelihood alignment algorithm, of each CT scan-derived manually contoured liver surface and the template Zubal liver... 

Liver-shape analysis and quantification is still an open research subject. Quantitative assessment of the liver is of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Liver-shape classification is of clinical importance for corresponding intra-subject and inter-subject studies. In this research, we propose a novel technique for the liver-shape classification based on Spherical Harmonics (SH) coefficients. The proposed liver-shape classification algorithm consists of the following steps: (a) Preprocessing, including mesh generation and simplification, point-set matching, and surface to template alignment; (b) Liver-shape parameterization,... 

Several methods have been proposed to construct Statistical Shape Model (SSM) to aim image analysis using computer in field Computer Aided Diagnosis (CAD), Computer Assisted Surgery (CAS), and other medical applications by providing a prior knowledge. The major challenge for liver shape model is a high variation in geometry such as size, shape and volume between livers. In this paper, we have presented a new technique for the automatic Multi-Scale Statistical Shape Model (MS-SSM) of three-dimensional (3-D) liver from volumetric segmented images data. The procedure included both building of Spherical Harmonics shape description and the Wavelet transform. Principal Component Analysis (PCA) was... 

A computational framework is presented for 3-D liver shape approximation and characterization in order to determine the accuracy of shape reconstruction via Spherical Harmonics expansion. Spherical Harmonics is a powerful mathematical tool for expanding the shape. But in medical domain, livers have very variation geometry, in shape, size, and volume. In this regards, we evaluated and optimized the Spherical Harmonics to create 3-D parametric surface of the liver from Computed Tomography (CT) imaging system which may useful for Shape modeling, surface representation, physical measurement of objects and mathematical model. We select randomly 5 livers from more 100 dataset. Mean Hausdorff... 

This study proposes a mathematical model to evaluate the impact of oxygen carriers and scaffold geometry on oxygen distribution and cell growth in a 3D cardiac construct using computational fluid dynamics (CFD). Flow equations, oxygen balance equation and cell balance equation were solved using special initial and boundary conditions. The modeling results revealed that 55% increase in cardiac cell density occurred by using 6.4% perfluorocarbon oxygen carrier (PFC) compared to pure culture medium without PFC supplementation. Moreover, the effects of the scaffold geometry on cell density were examined by changing the channel numbers and the construct length. A 30% increase in the average cells... 

Harnessing neural stem cells to repair neuronal damage is a promising potential treatment for neuronal diseases. To enable future therapeutic efficacy, the survival, proliferation, migration and differentiation of neural stem/progenitor cells (NPCs) should be accurately studied and optimized in in vitro platforms before transplanting these cells into the body for treatment purposes. Such studies can determine the appropriate quantities of the biochemical and biomechanical factors needed to control and optimize NPC behavior in vivo. In this study, NPCs were cultured within a microfluidic device while being encapsulated within the collagen matrix. The migration and differentiation of NPCs were... 

Nanostructured copper-doped uorapatite (Cux.Ca(10X).(PO4)6.F2) having crystallite sizes of 19, 29, and 34 nm at x = 0:9, 0.4, and 0.0, respectively, was synthesized by planetary ball milling of CaO, P2O5, CaF2, and CuO powders. Specifications of the products were determined by Fourier-transform infrared spectroscopy, eld emission scanning electron microscopy, transmission electron microscopy, and X-ray di raction analyses. In-vitro studies and Mossman's Tetrazole Test (MTT) assays were also conducted by incubating Cux.Ca(10X).(PO6).F2 powder into Kokubo's Simulated Body Fluid (SBF) and against BT-20 cell, respectively, to determine bioactivity and biocompatibility of the materials.... 

We determined the mechanism of mechanochemical synthesis of fluorapatite from CaO, CaF2 and P2O5 by characterisation of the intermediate compounds. We used atomic absorption spectroscopy, X-ray diffraction, field emission scanning electron microscopy, FTIR spectroscopy and transmission electron microscopy to find the transitional compounds. Investigation of the binary and ternary powder mixtures revealed the appearance of H3PO4, Ca(OH)2, Ca2P2O7 and CaCO3 as the intermediate compounds. At early stages of the milling, conversions of P2O5 to H3PO4 and CaO to Ca(OH)2 occurred in the wet atmosphere. Later, a combination of Ca(OH)2 and H3PO4 formed Ca2P2O7 while the unreacted CaO was converted to... 

von Willebrand factor (VWF) is a naturally collapsed protein that participates in primary haemostasis and coagulation events. The clotting process is triggered by the adsorption and conformational changes of the plasma VWFs localized to the collagen fibres found near the site of injury. We develop coarse-grained models to simulate the adsorption dynamics of VWF flowing near the adhesive collagen fibres at different shear rates and investigate the effect of factors such as interaction and cooperativity of VWFs on the success of adsorption events. The adsorption probability of a flowing VWF confined to the receptor field is enhanced when it encounters an adhered VWF in proximity to the... 

Interest in the design and manufacture of RNA and DNA aptamers as apta-biosensors for the early diagnosis of blood infections and other inflammatory conditions has increased considerably in recent years. The practical utility of these aptamers depends on the detailed knowledge about the putative interactions with their target proteins. Therefore, understanding the aptamer-protein interactions at the atomic scale can offer significant insights into the optimal apta-biosensor design. In this study, we consider one RNA and one DNA aptamer that were previously used as apta-biosensors for detecting the infection biomarker protein TNF-α, as an example of a novel computational workflow for... 

Pretrained representations have recently gained attention in various machine learning applications. Nonetheless, the high computational costs associated with training these models have motivated alternative approaches for representation learning. Herein we introduce TripletProt, a new approach for protein representation learning based on the Siamese neural networks. Representation learning of biological entities which capture essential features can alleviate many of the challenges associated with supervised learning in bioinformatics. The most important distinction of our proposed method is relying on the protein-protein interaction (PPI) network. The computational cost of the generated...