Blood flow and diameter effect in the navigation process of magnetic nanocarriers inside the carotid artery.

BACKGROUND AND OBJECTIVE: Serious side effects are occurred during the cancer therapy. Magnetic driving of nanoparticles is a novel method for the elimination of these effects by supplying with anticancer drug or increase the temperature of the infected area. For this reason, a numerical model for optimal guidance of nanoparticles, through the gradient magnetic field, inside the human artery system is presented in this study. METHODS: The present method couples Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) techniques. In addition, the optimum magnetic intensity each time is evaluated by using the covariance matrix adaptation evolution strategy (CMA-ES). Under five feature blood flow velocities in cardiac cycle, the developed method evaluate and select the optimum gradient magnetic field in order to eliminate the deviation of the guided nanoparticles from a pre-described trajectory. RESULTS: Results of the simulations indicate both the influence of the blood flow and the volume of nanocarriers in the magnetic driving process in real conditions. Specifically, the blood flow and the volume of particles are inversely proportional parameters in the magnetic navigation process. As the blood flow is decreased, the deviation of nanoparticles compared to the desired path is minimized. On the contrary, the decrease of the volume of nanocarriers increase the distance of particles from the described trajectory. However, greater magnetic gradient values are needed as the blood flow is increased. Furthermore, the imposed gradient magnetic values are strongly connected with the position of the nanoparticles and the blood blow velocity. CONCLUSIONS: Based on the results of the present study, the most important parameter in the navigation process is the magnetic volume of particles. Under real conditions, the effect of the blood flow is insignificant compared to the volume of particles in the navigation process. In addition, great differences in the optimized magnetic sequence are presented both among the different blood flows and the volume of particles.

Uncertainty quantification implementations in human hemodynamic flows.

BACKGROUND AND OBJECTIVE: Human hemodynamic modeling is usually influenced by uncertainties occurring from a considerable unavailability of information linked to the boundary conditions and the physical properties used in the numerical models. Calculating the effect of these uncertainties on the numerical findings along the cardiovascular system is a demanding process due to the complexity of the morphology of the body and the area dynamics. To cope with all these difficulties, Uncertainty Quantification (UQ) methods seem to be an ideal tool. RESULTS: This study focuses on analyzing and summarizing some of the recent research efforts and directions of implementing UQ in human hemodynamic flows by analyzing 139 research papers. Initially, the suitability of applying this approach is analyzed and demonstrated. Then, an overview of the most significant research work in various fields of biomedical hemodynamic engineering is presented. Finally, it is attempted to identify any possible forthcoming directions for research and methodological progress of UQ in biomedical sciences. CONCLUSION: This review concludes that by finding the best statistical methods and parameters to represent the propagated uncertainties, while achieving a good interpretation of the interaction between input-output, is crucial for implementing UQ in biomedical sciences.

On the magnetic aggregation of Fe3O4 nanoparticles.

Background and objective In-vivo MRI-guided drug delivery concept is a personalized technique towards cancer treatment. A major bottleneck of this method, is the weak magnetic response of nanoparticles. A crucial improvement is the usage of paramagnetic nanoparticles aggregates since they can easier manipulated in human arteries than isolated particles. However its significance, not a comprehensive study to estimate the mean length and time to aggregate exists. Methods The present detailed numerical study includes all major discrete and continues forces and moments of the nanoscale in a global model. The effort is given in summarizing the effects of particle diameter and concentration, and magnetic field magnitude to comprehensive relations. Therefore, several cases with nanoparticles having various diameters and concentrations are simulated as magnetic field increases. Results It is found that aggregations with maximum length equal to 2000nm can be formed. In addition, the increase of the concentration leads to a decrease in the amount of the isolated particles. Consequently, 33% of the particles are isolated for the concentration of 2.25mg/ml while 13% for the concentration of 10mg/ml. Moreover, the increase of the permanent magnetic field and diameter of particles gives rise to an asymptotic behavior in the number of isolated particles. Furthermore, the mean length of aggregates scales linear with diameter and magnetic field, however, concentration increase results in a weaker effect. The larger aggregation that is formed is composed by 21 particles. Smaller time is needed for the completion of the aggregation process with larger particles. Additionally, the increase of the magnitude of the magnetic field leads to a decrease in the aggregation time process. Therefore, 8.5ms are needed for the completion of the aggregation process for particles of 100nm at B0=0.1T while 7ms at B0=0.9T. Surprisedly, the mean time to aggregate is of the same order as in microparticles, although, with an opposite trend. Conclusions In this study, the evolution of the mean length of aggregations as well as the completion time of the aggregation process in the nano and micro range is evaluated. The present results could be useful to improve the magnetic nanoparticles assisted drug delivery method in order to minimize the side effects from the convectional cancer treatments like radiation and chemotherapy.

Rheological properties of synovial fluid due to viscosupplements: A review for osteoarthritis remedy.

The synovial fluid is a transparent electrolyte solution included in joints to provide lubrication helping the proper movement. It exhibits complex rheological properties due to the interaction among its constituents i.e. hyaluronic acid, albumin, lubricin and phospholipids. In degenerative osteoarthritis and inflammatory rheumatoid arthritis diseases, the quantity of synovial fluid and lubrication efficiency significantly deteriorates. In that case, viscosupplementation with intra-articular hyaluronic acid may be prescribed to replenish the concentration, the molecular weight and the rheological properties of natural synovial fluid. The present review concentrates on the recent advancements in viscosupplementation with emphasis into their rheological properties, its effects on the rheological behavior of synovial fluid, and finally its clinical effectiveness. Initially, the properties of synovial fluid are summarized, and then a discussion on commercial viscosupplements, the role of polymeric properties and their rheological properties are reviewed. Moreover, a detailed discussion on the clinical effectiveness and challenges of viscosupplements are provided.

Development of a new theoretical model for blood-CNTs effective thermal conductivity pertaining to hyperthermia therapy of glioblastoma multiform.

BACKGROUND AND OBJECTIVE: The present study deals with the hyperthermia therapy, which is the type of treatment in which tissues are exposed to high temperatures in order to destroy cancer cells with minimal injury to healthy tissues. In particular, it focuses on glioblastoma multiform, which is the most aggressive cancer that begins within the brain. Conventional treatments display limitations that can be overcome by using nanoparticles for targeted heating. Out of the proposed nanoparticles, this investigation focuses on a new field that utilizes carbon nanotubes (CNTs) which are able to selectively heat the cancer cells since they can convert near infrared light into heat. In the absence of any experiment or theoretical model for the estimation of an effective thermal conductivity of blood and CNTs, a first principle model is developed in this study which takes into account the blood micro-structure. Besides, a number of factors are included, namely the shape and the size of the nanoparticles, the interfacial layer formed around them and their volume fraction. METHODS: Firstly, assuming that the blood consists of blood cells and plasma, the thermal conductivity of the former is estimated. Then, the effective thermal conductivity of plasma/CNTs is calculated for various parameters. Finally, the resulting "bio-nanofluid" consisting of plasma/CNTs and blood cells is formed. RESULTS: It is ascertained that thin and elongated CNTs with relatively large nanolayer thickness as well as large concentrations of CNTs contribute to the increase of the thermal conductivity and, thus, in the enhancement of the heat transfer. CONCLUSIONS: Investigating of how design parameters pertaining to CNTs, such as their size and shape, affect the effective thermal conductivity of blood-CNTs, possible regulating ways are suggested regarding the hyperthermic treatment. Finally, the present simple estimation of the effective thermal conductivity can be used as an effective property of the nanofluid when it comes to numerical investigations regarding heat transfer occurring during hyperthermia or other potential clinical uses (for example targeted heat of living tissues).

Effect of fractal-shaped outer boundary of glioblastoma multiforme on drug delivery.

BACKGROUND AND OBJECTIVE: The present study investigates the transport of drugs in the proximity of the glioblastoma multiforme, a brain neoplasm which is regarded to be the most aggressive type of cancer. In such a small distance from the tumor, diffusion dominates and is driven by the concentration gradient of drug that acquires its maximum at regions where the drugs are released and its minimum at the cancer cell boundary. Undoubtedly, the morphology of the aforementioned boundary is going to play a crucial role in the drug delivery and should be taken into account for the optimal design of the treatment. As first step in order to simulate the topography of glioblastoma multiforme, a fractal boundary is examined which mimics an acceptable model-problem for prognosis and diagnosis of a number of cancer tumors in breast, lungs and brain. METHODS: The drug diffusion is investigated for two concentrations, namely a strong and a mild diffusion, while the outer boundary of the glioblastoma multiforme is approximated via triangular Von Koch shapes. Besides, a Finite Element Method is utilized via FEniCS, which is a Python-based open-source computing platform. Finally, after ascertaining the accuracy of the present numerical model, the concentration of the drug, the entropy production and the mass fluxes in the horizontal and vertical directions are estimated up to the fifth order of Von Koch fractal iterations. RESULTS: It is ascertained that as the boundaries become more and more irregular, the entropy production in specific areas increases and as a consequence the delivery of the drug is facilitated. Hence, the mass fluxes in these sites appear to be larger comparing to the rest of the boundary and increase, as expected, for the case of strong diffusion. CONCLUSIONS: These active regions, which are referred as "hot spots", are of great importance since they seem to be the sites where the drug ultimately penetrates the glioblastoma. This first-principles investigation is anticipated to shed light on a very significant part of drug delivery, which deals with the vicinity of the glioblastoma multiforme, stress the importance of the topography and give rise to future studies to be conducted based on subject-specific geometries.

A numerical model for aggregations formation and magnetic driving of spherical particles based on OpenFOAM®.

BACKGROUND AND OBJECTIVE: This work presents a numerical model for the formation of particle aggregations under the influence of a permanent constant magnetic field and their driving process under a gradient magnetic field, suitably created by a Magnetic Resonance Imaging (MRI) device. METHODS: The model is developed in the OpenFOAM platform and it is successfully compared to the existing experimental and numerical results in terms of aggregates size and their motion in water solutions. Furthermore, several series of simulations are performed for two common types of particles of different diameter in order to verify their aggregation and flow behaviour, under various constant and gradient magnetic fields in the usual MRI working range. Moreover, the numerical model is used to measure the mean length of aggregations, the total time needed to form and their mean velocity under different permanent and gradient magnetic fields. RESULTS: The present model is found to predict successfully the size, velocity and distribution of aggregates. In addition, our simulations showed that the mean length of aggregations is proportional to the permanent magnetic field magnitude and particle diameter according to the relation : l¯a=7.5B0di3/2. The mean velocity of the aggregations is proportional to the magnetic gradient, according to : u¯a=6.63G˜B0 and seems to reach a steady condition after a certain period of time. The mean time needed for particles to aggregate is proportional to permanent magnetic field magnitude, scaled by the relationship : t¯a∝7B0. CONCLUSIONS: A numerical model to predict the motion of magnetic particles for medical application is developed. This model is found suitable to predict the formation of aggregations and their motion under the influence of permanent and gradient magnetic fields, respectively, that are produced by an MRI device. The magnitude of the external constant magnetic field is the most important parameter for the aggregations formation and their driving.