An Object-Oriented Framework for Versatile Finite Element Based Simulations of Neurostimulation

Computational simulations of transcranial electrical stimulation (TES) are commonly utilized by the neurostimulation community, and while vastly different TES application areas can be investigated, the mathematical equations and physiological characteristics that govern this research are identical. The goal of this work was to develop a robust software framework for TES that efficiently supports the spectrum of computational simulations routinely utilized by the TES community and in addition easily extends to support alternative neurostimulation research objectives. Using well-established object-oriented software engineering techniques, we have designed a software framework based upon the physical and computational aspects of TES. The framework’s versatility is demonstrated with a set of diverse neurostimulation simulations that (i) reinforce the importance of using anisotropic tissue conductivities, (ii) demonstrate the enhanced precision of high-definition stimulation electrodes, and (iii) highlight the benefits of utilizing multigrid solution algorithms. Our approaches result in a framework that facilitates rapid prototyping of real-world, customized TES administrations and supports virtually any clinical, biomedical, or computational aspect of this treatment. Software reuse and maintainability are optimized, and in addition, the same code can be effortlessly augmented to provide support for alternative neurostimulation research endeavors.

Musculoskeletal Simulation for Assessment of Effect of Movement-Based Structure-Modifying Treatment Strategies

The better understanding of the complex mechanism between neural motor control and its resulting joint kinematics and muscle forces allows a better elucidation of the mechanisms behind body growth, aging progression, and disease development. This study aimed at investigating the impact of movement-based structure-modifying treatment strategies on joint kinematics, muscle forces, and muscle synergies of the gait with instrumented implant. A patient-specific musculoskeletal model was used to quantitatively assess the deviations of joint and muscle behaviors between the normal gait and 4 gait modifications (bouncy, medial thrust, midcrouch, and mtp (i.e., gait with forefoot strike)). Moreover, muscle synergy analysis was performed using EMG-based nonnegative matrix factorization. Large variation of 19 degrees and 190 N was found for knee flexion/extension and lower limb muscle forces, respectively. EMG-based muscle synergy analysis revealed that the activation levels of the vastus lateralis and tibialis anterior are dominant for the midcrouch gait. In addition, an important contribution of semimembranosus to the medial thrust and midcrouch gaits was also observed. In fact, such useful information could allow a better understanding of the joint function and muscle synergy strategies leading to deeper knowledge of joint and muscle mechanisms related to neural voluntary motor commands.

A Hertzian Integrated Contact Model of the Total Knee Replacement Implant for the Estimation of Joint Contact Forces

The prediction of lower limb muscle and contact forces may provide useful knowledge to assist the clinicians in the diagnosis as well as in the development of appropriate treatment for musculoskeletal disorders. Research studies have commonly estimated joint contact forces using model-based muscle force estimation due to the lack of a reliable contact model and material properties. The objective of this present study was to develop a Hertzian integrated contact model. Then, in vivo elastic properties of the Total Knee Replacement (TKR) implant were identified using in vivo contact forces leading to providing reliable material properties for modeling purposes. First, a patient specific rigid musculoskeletal model was built. Second, a STL-based implant model was designed to compute the contact area evolutions during gait motions. Finally, a Hertzian integrated contact model was defined for the in vivo identification of elastic properties (Young’s modulus and Poisson coefficient) of the instrumented TKR implant. Our study showed a potential use of a new approach to predict the contact forces without knowledge of muscle forces. Thus, the outcomes may lead to accurate and reliable prediction of human joint contact forces for new case study.

Computational Simulation of Tumor Surgical Resection Coupled with the Immune System Response to Neoplastic Cells

Numerous mathematical and computational models have arisen to study and predict the effects of diverse therapies against cancer (e.g., chemotherapy, immunotherapy, and even therapies under research with oncolytic viruses) but, unfortunately, few efforts have been directed towards development of tumor resection models, the first therapy against cancer. The model hereby presented was stated upon fundamental assumptions to produce a predictor of the clinical outcomes of patients undergoing a tumor resection. It uses ordinary differential equations validated for predicting the immune system response and the tumor growth in oncologic patients. This model could be further extended to a personalized prognosis predictor and tools for improving therapeutic strategies.

Genetic Algorithm Based Approach in Attribute Weighting for a Medical Data Set

Genetic algorithms have been utilized in many complex optimization and simulation tasks because of their powerful search method. In this research we studied whether the classification performance of the attribute weighted methods based on the nearest neighbour search can be improved when using the genetic algorithm in the evolution of attribute weighting. The attribute weights in the starting population were based on the weights set by the application area experts and machine learning methods instead of random weight setting. The genetic algorithm improved the total classification accuracy and the median true positive rate of the attribute weighted k-nearest neighbour method using neighbour’s class-based attribute weighting. With other methods, the changes after genetic algorithm were moderate.

On the Role of Optimal Counseling and Antiviral Therapy on Controlling HCV among Intravenous Drug Misusers

Hepatitis C virus (HCV) remains a major health challenge despite the availability of highly effective antiviral drugs. Prior studies suggest that many physicians are reluctant to treat intravenous drug misusers due to low levels of treatment adherence associated with intravenous drug misusers. HCV treatment guidelines and recommendations stipulate that HCV patients in treatment should abstain from intravenous drug misuse activities in order to reduce the likelihood of treatment failure, drug resistance, reinfection, superinfection, or mixed infection. In this paper, a mathematical model for exploring the transmission dynamics of HCV among intravenous drug misusers is proposed. The model incorporates essential characteristics of intravenous drug misusers such as relapse and nonadherence to treatment guidelines. With the aid of optimal control theory we assess the effects of time dependent HCV screening and treatment. Results from this study provide a framework for designing the appropriate strategies on controlling the long-term dynamics of HCV among intravenous drug users.

Mathematical Modeling of the Expert System Predicting the Severity of Acute Pancreatitis

The method of building the hyperplane which separates the convex hulls in the Euclidean space Rn is proposed. The algorithm of prediction of the presence of severity in patients based on this method is developed and applied in practice to predict the presence of severity in patients with acute pancreatitis.

2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

A set of 15 indolylpyrimidine derivatives with their antibacterial activities in terms of minimum inhibitory concentration against the gram-negative bacteria Pseudomonas aeruginosa and gram-positive Staphylococcus aureus were selected for 2D quantitative structure activity relationship (QSAR) analysis. QSAR was performed using a combination of various descriptors such as steric, electronic and topological. Stepwise regression method was used to derive the most significant QSAR equation for predicting the inhibitory activity of this class of molecules. The best QSAR model was further validated by a leave one out technique as well as by the random trials. A high correlation between experimental and predicted inhibitory values was observed. A comparative picture of behavior of indolylpyrimidines against both of the microorganisms is discussed.

Combined 3D QSAR Based Virtual Screening and Molecular Docking Study of Some Selected PDK-1 Kinase Inhibitors

Phosphoinositide-dependent kinase-1 (PDK-1) is an important therapeutic target for the treatment of cancer. In order to identify the important chemical features of PDK-1 inhibitors, a 3D QSAR pharmacophore model was developed based on 21 available PDK-1 inhibitors. The best pharmacophore model (Hypo1) exhibits all the important chemical features required for PDK-1 inhibitors. The correlation coefficient, root mean square deviation (RMSD), and cost difference were 0.96906, 1.0719, and 168.13, respectively, suggesting a good predictive ability of the model (Hypo1) among all the ten pharmacophore models that were analyzed. The best pharmacophore model (Hypo1) was further validated by Fisher’s randomization method (95%), test set method (r=0.87), and the decoy set with the goodness of fit (0.73). Further, this validated pharmacophore model Hypo1 was used as a 3D query to screen the molecules from databases like NCI database and Maybridge. The resultant hit compounds were subsequently subjected to filtration by Lipinski’s rule of five as well as the ADMET study. Docking study was done to refine the retrieved hits and as a result to reduce the rate of false positive. Best hits will further be subjected to in vitro study in future.

Reducing the Inconsistency between Doppler and Invasive Measurements of the Severity of Aortic Stenosis Using Aortic Valve Coefficient: A Retrospective Study on Humans

Background. It is not uncommon to observe inconsistencies in the diagnostic parameters derived from Doppler and catheterization measurements for assessing the severity of aortic stenosis (AS) which can result in suboptimal clinical decisions. In this pilot study, we investigate the possibility of improving the concordance between Doppler and catheter assessment of AS severity using the functional diagnostic parameter called aortic valve coefficient (AVC), defined as the ratio of the transvalvular pressure drop to the proximal dynamic pressure. Method and Results. AVC was calculated using diagnostic parameters obtained from retrospective chart reviews. AVC values were calculated independently from cardiac catheterization (AVCcatheter) and Doppler measurements (AVCdoppler). An improved significant correlation was observed between Doppler and catheter derived AVC (r=0.92, P<0.05) when compared to the correlation between Doppler and catheter measurements of mean pressure gradient (r=0.72, P<0.05) and aortic valve area (r=0.64, P<0.05). The correlation between Doppler and catheter derived AVC exhibited a marginal improvement over the correlation between Doppler and catheter derived aortic valve resistance (r=0.89, P<0.05). Conclusion. AVC is a refined clinical parameter that can improve the concordance between the noninvasive and invasive measures of the severity of aortic stenosis.