Droplet Electrophoresis with Internal Free Ions: Effect of Permittivity Changes in the Electric Double Layer.

Droplet electrophoresis (EP) is of interest in biological systems, microfluidics, and separation techniques. We investigate EP of an oil droplet that contains free ions and is stabilized in an electrolyte solution through an amphoteric surfactant. The presence of mobile ions within the droplet leads to the creation of a distinct nonzero space charge density inside the droplet and consequently, formation of an inner EDL inside the droplet in addition to the traditionally considered outside EDL. While we assume the permittivity inside the inner EDL to remain constant, we consider both the case of constant and variable permittivity in the outer EDL. Our findings demonstrate a change in the droplet direction of motion in the electric field when transitioning from acidic to alkaline pH, regardless of permittivity and ionic strength in both oil and electrolyte. We further find a significant reduction in the magnitude of droplet velocity in the case of a variable permittivity due to reduction of the local space charge density within the EDL surrounding the droplet. When decreasing the viscosity ratio of the oil to the electrolyte, in all cases we find a reduction in droplet velocity. This decline is attributed mostly to the formation and strength of a vortex around the droplet. We finally demonstrate that with constant permittivity in the outer EDL, the variation in κaouter has a more significant effect on the droplet's EP velocity than altering κainner. However, in cases where the body forces inside of the droplet dominate, minor changes in the outer electrolyte concentration have no influence on the droplet motion, which is relevant for biological colloids that can contain significant free internal charges. Our results are important for the manipulation of biological colloids, water and waste treatment such as lubricant removal from processing streams.

Numerical investigation of the pharmacokinetics and pharmacodynamics of the chemotherapeutic drug in avascular and vascular stages of a brain tumor.

One of the most commonly used approaches for treating solid tumors is the systemic delivery of chemotherapeutic drugs. However, our understanding of the factors influencing treatment efficacy through this method is still limited. This study presents a comprehensive and realistic mathematical model that incorporates the dynamics of tumor growth, capillary network extension, and drug delivery in a coupled and simultaneous manner. The model covers two stages of tumor growth: avascular and vascular. For tumor growth, a continuum model is employed using the phase field interface-capturing method. The neo-vascularization process is modeled using a hybrid discrete-continuum approach. Additionally, a multi-scale model is used to describe the pharmacokinetics of doxorubicin, considering various agents. The study investigates the effect of haptotaxis and reveals that a higher haptotaxis coefficient leads to faster tumor growth (up to 2.6 times) and a quicker progression to angiogenesis. The impact of tumor-related and drug-related parameters is also examined, including tumor size, tumor sensitivity to the drug, chemotherapy initialization, treatment cycle duration, drug affinity to cells, and drug dose. The findings indicate that chemotherapy is more effective during the angiogenesis stage when active loops have formed. Other clinical methods such as radiotherapy and surgery may be more appropriate during the avascular stage or the transition period between angiogenesis initialization and loop formation. The penetration depth of the drug decreases by approximately 50% with an increase in the drug binding rate to surface-cell receptors. As a result, high-associate-rate drugs are preferred for chemotherapy after active loops have formed, while low-associate-rate drugs are suitable for earlier stages.

A novel in silico platform for a fully automatic personalized brain tumor growth.

Glioblastoma Multiforme is the most common and most aggressive type of brain tumors. Although accurate prediction of Glioblastoma borders and shape is absolutely essential for neurosurgeons, there are not many in silico platforms that can make such predictions. In the current study, an automatic patient-specific simulation of Glioblastoma growth would be described. A finite element approach is used to analyze the magnetic resonance images from patients in the early stages of their tumors. For segmentation of the tumor, the Support Vector Machine (SVM) method, which is an automatic segmentation algorithm, is used. Using in situ and in vivo data, the main parameters of tumor prediction and growth are estimated with high precision in proliferation-invasion partial differential equation, using the genetic algorithm optimization method. The results show that for a C57BL mouse, the differences between the area and perimeter of in vivo test and simulation prediction data, as the objective function, are 3.7% and 17.4%, respectively.

Stress analysis of two methods of ceramic inlay preparation by finite element.

OBJECTIVE: Ceramic inlays are bonded to tooth structure with resin cements. During the resin cement setting, shrinkage stress develops at the interfaces. During tooth preparation, the undercut areas formed due to the different patterns of caries progression can either be blocked out before taking impression with suitable cement such as glass ionomer cement, or before making the final restoration in the laboratory. Then, the relieved space will be filled with luting cement in clinic. The aim of this study was to compare these two methods of undercut filling in term of stress distribution in the ceramic inlay. MATERIALS AND METHODS: An axisymmetric finite element analysis was performed to study the stress distribution during inlay cementing. The solid model was generated from a longitudinal section of maxillary premolar in which a class I cavity with 60 degree undercut at the preparation wall and 20 degree divergence of the vertical walls was prepared. A thermal model was used to simulate the polymerization shrinkage of the resin cement. Finite element analysis was carried out in ANSYS environment. RESULTS: Filling the undercut by glass ionomer cement decreased the stress concentration at the ceramic/cement interface. The dominant normal stress at the tooth cement interface in absence of glass ionomer cement was tensile with maximum of 30 Mpa. Using glass ionomer, cement developed stresses with different compressive and tensile signs. With increasing the thickness of resin cement (100 µm, 150 µm, 200 µm), the stress increased. CONCLUSION: Cements with minimum shrinkage and as thin layer as possible should be used. Filling the undercut with glass ionomer cement decreases the stress. Other experimental and clinical studies must follow this research.