Journal of Computational Medicine The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Context-Based Separation of Cell Clusters for the Automatic Biocompatibility Testing of Implant Materials Thu, 20 Mar 2014 12:19:20 +0000 This paper presents a new method to separate cells on microscopic surfaces joined together in cell clusters into individual cells. Important features of this method are that the remaining object geometry is preserved and few contour points are required for finding joints between neighboring cells. There are alternative methods such as morphological operations or the watershed transformation based on the inverse distance transformation but they have certain disadvantages compared to the method presented in this paper. The discussed method contains knowledge-based components in form of a decision function and exchangeable rules to avoid unwanted separations. S. Buhl, B. Neumann, and S. C. Schäfer Copyright © 2014 S. Buhl et al. All rights reserved. Modeling Requirements for Computer Simulation of Cerebral Aneurysm Sun, 16 Feb 2014 15:27:05 +0000 Background. In order to reduce the mortality risk of aneurysm rupture, a timely diagnosis and treatment are vital. There are different reasons for aneurysm, such as hypertension, arteriosclerosis, and heredity. An efficient and cost-effective method to study the generation, development, and rupture of aneurysm and also analysis of treatment methods can accelerate progress. The Computational Fluid Dynamics is a well-known tool to simulate various phenomena. A reliable virtual modeling in biology depends on our knowledge about variety of characteristics, that is, biological features, structural properties, and flow conditions. Objective. Because of the vast research about the related subjects, an organized review is required. The aim of current review article is classification of the required foundations for a reliable virtual modeling of cerebral aneurysm, especially in the Circle of Willis. S. R. Ghodsi, V. Esfahanian, and S. M. Ghodsi Copyright © 2014 S. R. Ghodsi et al. All rights reserved. Transmission Dynamics of Hepatitis C with Control Strategies Thu, 13 Feb 2014 12:24:04 +0000 We present a rigorous mathematical analysis of a deterministic model, for the transmission dynamics of hepatitis C, using a standard incidence function. The infected population is divided into three distinct compartments featuring two distinct infection stages (acute and chronic) along with an isolation compartment. It is shown that for basic reproduction number , the disease-free equilibrium is locally and globally asymptotically stable. The model also has an endemic equilibrium for . Uncertainty and sensitivity analyses are carried out to identify and study the impact of critical parameters on . In addition, we have presented the numerical simulations to investigate the influence of different important parameters on . Since we have a locally stable endemic equilibrium, optimal control is applied to the deterministic model to reduce the total infected population. Two different optimal control strategies (vaccination and isolation) are designed to control the disease and reduce the infected population. Pontryagin’s Maximum Principle is used to characterize the optimal controls in terms of an optimality system which is solved numerically. Numerical results for the optimal controls are compared against the constant controls and their effectiveness is discussed. Adnan Khan, Sultan Sial, and Mudassar Imran Copyright © 2014 Adnan Khan et al. All rights reserved. Validation of Shape Context Based Image Registration Method Using Digital Image Correlation Measurement on a Rat Stomach Mon, 06 Jan 2014 16:33:07 +0000 Recently we developed analysis for 3D visceral organ deformation by combining the shape context (SC) method with a full-field strain (strain distribution on a whole 3D surface) analysis for calculating distension-induced rat stomach deformation. The surface deformation detected by the SC method needs to be further verified by using a feature tracking measurement. Hence, the aim of this study was to verify the SC method-based calculation by using digital image correlation (DIC) measurement on a rat stomach. The rat stomach exposed to distension pressures 0.0, 0.2, 0.4, and 0.6 kPa were studied using both 3D DIC system and SC-based image registration calculation. Three different surface sample counts between the reference and the target surfaces were used to gauge the effect of the surface sample counts on the calculation. Each pair of the surface points between the DIC measured target surface and the SC calculated correspondence surface was compared. Compared with DIC measurement, the SC calculated surface had errors from 5% to 23% at pressures from 0.2 to 0.6 kPa with different surface sample counts between the reference surface and the target surface. This indicates good qualitative and quantitative agreement on the surfaces with small dissimilarity and small sample count difference between the reference surface and the target surface. In conclusion, this is the first study to validate the 3D SC-based image registration method by using unique tracking features measurement. The developed method can be used in the future for analysing scientific and clinical data of visceral organ geometry and biomechanical properties in health and disease. Donghua Liao, Peng Wang, Jingbo Zhao, and Hans Gregersen Copyright © 2014 Donghua Liao et al. All rights reserved. Use of SSA and MCSSA in the Analysis of Cardiac RR Time Series Tue, 24 Dec 2013 10:51:08 +0000 A new preprocessing procedure in the analysis of cardiac RR interval time series is described. It uses the singular spectrum analysis (SSA) and the Monte Carlo SSA (MCSSA) test. A novel feature of this preprocessing procedure is the ability to identify the noise component present in the series with a given probability and to separate the time series into a trend, signal, and noise. The MCSSA test involves testing whether the modes obtained from SSA can be generated by a noise process leading to separation of the noise modes from the signal. The procedure described here does not discard or modify any sample in the record but merely separates the time series into a trend, signal, and noise, allowing for further analysis of these components. The procedure is not limited to the length of the record and could be applied to nonstationary data. The basis functions used in SSA are data adaptive in that they are not chosen a priori but instead are dependent on the data set used, increasing flexibility to the analysis. The procedure is illustrated using the RR interval time series of a healthy, congestive heart failure, and atrial fibrillation subject. R. A. Thuraisingham Copyright © 2013 R. A. Thuraisingham. All rights reserved. Molecular Docking Assessment of Efficacy of Different Clinically Used Arsenic Chelator Drugs Sun, 15 Dec 2013 14:46:54 +0000 Arsenic contamination of ground water has become a global problem affecting specially, south-east Asian countries like Bangladesh and eastern parts of India. It also affects South America and some parts of the US. Different organs of the physiological system are affected due to contamination of inorganic arsenic in water. Animal studies with different chelators are not very conclusive as far as the multi/differential organ effect(s) of arsenic is concerned. Our docking study establishes the molecular rationale of blood test for early detection of arsenic toxicity; as arsenic has a high affinity to albumin, a plasma protein and actin, a structural protein of all cells including Red Blood Cells. This study also shows that there is a little possibility of male reproductive organs toxicity by different forms of inorganic arsenic; however, female reproductive system is very much susceptible to sodium-arsenite. Through comparative analysis regarding the chelating effectiveness among the available arsenic chelator drugs, meso-2,3 dimercaptosuccinic acid (DMSA) and in some cases lipoic acid is the most preferred choice of drug for removing of arsenic deposits. This computational method actually reinforces the clinical finding regarding DMSA as the most preferred drug in removal of arsenic deposits from majority of the human tissues. Durjoy Majumder and Sayan Mukherjee Copyright © 2013 Durjoy Majumder and Sayan Mukherjee. All rights reserved. Mathematical Modeling of Melanoma Cell Migration with an Elastic Continuum Model for the Evaluation of the Influence of Tumor Necrosis Factor-Alpha on Migration Wed, 25 Sep 2013 14:45:38 +0000 An elastic continuum mathematical model was implemented to study collective C8161 melanoma cell migration during a “scratch wound” assay, in control and under the influence of the proinflammatory cytokine tumour necrosis factor-alpha (TNF-α). The model has four constants: force that results from lamellipod formation (F), adhesion constant between cells and extracellular matrix (ECM) (b), cell layer elasticity modulus (k), and growth rate (ρ). A nonlinear regression routine was used to obtain the parameters of the model with data from an experiment made with C8161 melanoma cells, with and without TNF-α. Coefficient of determination for both situations was and , respectively. The parameters values obtained were similar to the ones found in the literature. However, the adhesion constant value decreased with the introduction of TNF-α, which is not in accordance with expected since the presence of TNF-α is associated with an increased expression of integrins that would promote an enhanced adhesion among cells. The model was used in a study relating to the adhesion constant and cell migration, and the results suggested that cell migration decreases with higher adhesion, which is also not in accordance with expected. These differences would not occur if it was considered that TNF-α increases the elasticity modulus of the cell layer. Julia Vianna Gallinaro, Claudia Mirian de Godoy Marques, Fernando Mendes de Azevedo, and Daniela Ota Hisayasu Suzuki Copyright © 2013 Julia Vianna Gallinaro et al. All rights reserved. Structure-Based Virtual Screening and Molecular Dynamic Simulation Studies to Identify Novel Cytochrome bc1 Inhibitors as Antimalarial Agents Sat, 24 Aug 2013 09:02:38 +0000 Cytochrome bc1 (EC, bc1) is an essential component of the cellular respiratory chain, which catalyzes electron transfer from quinol to cytochrome c and concomitantly the translocation of protons across the membrane. It has been identified as a promising target in malaria parasites. The structure-based pharmacophore modelling and molecular dynamic simulation approach have been employed to identify novel inhibitors of cytochrome bc1. The best structure-based pharmacophore hypothesis (Hypo1) consists of one hydrogen bond acceptor (HBA), one general hydrophobic (HY), and two hydrophobic aromatic features (HYAr). Further, hydrogen interactions and hydrophobic interactions of known potent inhibitors with cytochrome bc1 were compared with Hypo1, which showed that the Hypo1 has good predictive ability. The validated Hypo1 was used to screen the chemical databases. The hits obtained were subsequently subjected to the molecular docking analysis to identify false-positive hits. Moreover, the molecular docking results were further validated by molecular dynamics simulations. Binding-free energy analysis using MM-GBSA method reveals that the van der Waals interactions and the electrostatic energy provide the basis for favorable absolute free energy of the complex. The five virtual hits were identified as possible candidates for the designing of potent cytochrome bc1 inhibitors. Rahul P. Gangwal, Gaurao V. Dhoke, Mangesh V. Damre, Kanchan Khandelwal, and Abhay T. Sangamwar Copyright © 2013 Rahul P. Gangwal et al. All rights reserved. LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening Mon, 24 Jun 2013 09:52:03 +0000 Nuclear receptors (NRs) are important biological macromolecular transcription factors that are implicated in multiple biological pathways and may interact with other xenobiotics that are endocrine disruptors present in the environment. Examples of important NRs include the androgen receptor (AR), estrogen receptors (ER), and the pregnane X receptor (PXR). In this study we have utilized the Ligand Activity by Surface Similarity Order (LASSO) method, a ligand-based virtual screening strategy to derive structural (surface/shape) molecular features used to generate predictive models of biomolecular activity for AR, ER, and PXR. For PXR, twenty-five models were built using between 8 to 128 agonists and tested using 3000, 8000, and 24,000 drug-like decoys including PXR inactive compounds . Preliminary studies with AR and ER using LASSO suggested the utility of this approach with 2-fold enrichment factors at 20%. We found that models with 64–128 PXR actives provided enrichment factors of 10-fold (10% actives in the top 1% of compounds screened). The LASSO models for AR and ER have been deployed and are freely available online, and they represent a ligand-based prediction method for putative NR activity of compounds in this database. Sean Ekins, Michael-R. Goldsmith, Aniko Simon, Zsolt Zsoldos, Orr Ravitz, and Antony J. Williams Copyright © 2013 Sean Ekins et al. All rights reserved. QSAR Investigation on Quinolizidinyl Derivatives in Alzheimer’s Disease Tue, 30 Apr 2013 08:48:33 +0000 Sets of quinolizidinyl derivatives of bi- and tri-cyclic (hetero) aromatic systems were studied as selective inhibitors. On the pattern, quantitative structure-activity relationship (QSAR) study has been done on quinolizidinyl derivatives as potent inhibitors of acetylcholinesterase in alzheimer’s disease (AD). Multiple linear regression (MLR), partial least squares (PLSs), principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) were used to create QSAR models. Geometry optimization of compounds was carried out by B3LYP method employing 6–31 G basis set. HyperChem, Gaussian 98 W, and Dragon software programs were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. Finally, Unscrambler program was used for the analysis of data. In the present study, the root mean square error of the calibration and R2 using MLR method were obtained as 0.1434 and 0.95, respectively. Also, the R and R2 values were obtained as 0.79, 0.62 from stepwise MLR model. The R2 and mean square values using LASSO method were obtained as 0.766 and 3.226, respectively. The root mean square error of the calibration and R2 using PLS method were obtained as 0.3726 and 0.62, respectively. According to the obtained results, it was found that MLR model is the most favorable method in comparison with other statistical methods and is suitable for use in QSAR models. Ghasem Ghasemi, Sattar Arshadi, Alireza Nemati Rashtehroodi, Mahyar Nirouei, Shahab Shariati, and Zinab Rastgoo Copyright © 2013 Ghasem Ghasemi et al. All rights reserved. SAR and Computer-Aided Drug Design Approaches in the Discovery of Peroxisome Proliferator-Activated Receptor γ Activators: A Perspective Thu, 04 Apr 2013 17:14:13 +0000 Activators of PPARγ, Troglitazone (TGZ), Rosiglitazone (RGZ), and Pioglitazone (PGZ) were introduced for treatment of Type 2 diabetes, but TGZ and RGZ have been withdrawn from the market along with other promising leads due cardiovascular side effects and hepatotoxicity. However, the continuously improving understanding of the structure/function of PPARγ and its interactions with potential ligands maintain the importance of PPARγ as an antidiabetic target. Extensive structure activity relationship (SAR) studies have thus been performed on a variety of structural scaffolds by various research groups. Computer-aided drug discovery (CADD) approaches have also played a vital role in the search and optimization of potential lead compounds. This paper focuses on these approaches adopted for the discovery of PPARγ ligands for the treatment of Type 2 diabetes. Key concepts employed during the discovery phase, classification based on agonistic character, applications of various QSAR, pharmacophore mapping, virtual screening, molecular docking, and molecular dynamics studies are highlighted. Molecular level analysis of the dynamic nature of ligand-receptor interaction is presented for the future design of ligands with better potency and safety profiles. Recently identified mechanism of inhibition of phosphorylation of PPARγ at SER273 by ligands is reviewed as a new strategy to identify novel drug candidates. Vaibhav A. Dixit and Prasad V. Bharatam Copyright © 2013 Vaibhav A. Dixit and Prasad V. Bharatam. All rights reserved. Molecular Docking Study on the Interaction of Riboflavin (Vitamin ) and Cyanocobalamin (Vitamin ) Coenzymes Sun, 31 Mar 2013 13:49:22 +0000 Cobalamins are the largest and structurally complex cofactors found in biological systems and have attracted considerable attention due to their participation in the metabolic reactions taking place in humans, animals, and microorganisms. Riboflavin (vitamin B2) is a micronutrient and is the precursor of coenzymes, FMN and FAD, required for a wide variety of cellular processes with a key role in energy-based metabolic reactions. As coenzymes of both vitamins are the part of enzyme systems, the possibility of their mutual interaction in the body cannot be overruled. A molecular docking study was conducted on riboflavin molecule with B12 coenzymes present in the enzymes glutamate mutase, diol dehydratase, and methionine synthase by using ArgusLab 4.0.1 software to understand the possible mode of interaction between these vitamins. The results from ArgusLab showed the best binding affinity of riboflavin with the enzyme glutamate mutase for which the calculated least binding energy has been found to be −7.13 kcal/mol. The results indicate a significant inhibitory effect of riboflavin on the catalysis of B12-dependent enzymes. This information can be utilized to design potent therapeutic drugs having structural similarity to that of riboflavin. Ambreen Hafeez, Zafar Saied Saify, Afshan Naz, Farzana Yasmin, and Naheed Akhtar Copyright © 2013 Ambreen Hafeez et al. All rights reserved. Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm Sun, 03 Feb 2013 08:55:35 +0000 Magnetic resonance imaging (MRI) segmentation is a complex issue. This paper proposes a new method for estimating the right number of segments and automatic segmentation of human normal and abnormal MR brain images. The purpose of automatic diagnosis of the segments is to find the number of divided image areas of an image according to its entropy and with correctly diagnose of the segment of an image also increased the precision of segmentation. Regarding the fact that guessing the number of image segments and the center of segments automatically requires algorithm test many states in order to solve this problem and to have a high accuracy, we used a combination of the genetic algorithm and the fuzzy c-means (FCM) method. In this method, it has been tried to change the FCM method as a fitness function for combination of it in genetic algorithm to do the image segmentation more accurately. Our experiment shows that the proposed method has a significant improvement in the accuracy of image segmentation in comparison to similar methods. Omid Jamshidi and Abdol Hamid Pilevar Copyright © 2013 Omid Jamshidi and Abdol Hamid Pilevar. All rights reserved.