Characterization of Nanoparticle Dispersion in Red Blood Cell Suspension by the Lattice Boltzmann-Immersed Boundary Method

A mathematical model of fibrinogen-mediated erythrocyte-erythrocyte adhesion

Erythrocytes are deformable cells that undergo progressive biophysical and biochemical changes affecting the normal blood flow. Fibrinogen, one of the most abundant plasma proteins, is a primary determinant for changes in haemorheological properties, and a major independent risk factor for cardiovascular diseases. In this study, the adhesion between human erythrocytes is measured by atomic force microscopy (AFM) and its effect observed by micropipette aspiration technique, in the absence and presence of fibrinogen. These experimental data are then used in the development of a mathematical model to examine the biomedical relevant interaction between two erythrocytes. Our designed mathematical model is able to explore the erythrocyte-erythrocyte adhesion forces and changes in erythrocyte morphology. AFM erythrocyte-erythrocyte adhesion data show that the work and detachment force necessary to overcome the adhesion between two erythrocytes increase in the presence of fibrinogen. The changes in erythrocyte morphology, the strong cell-cell adhesion and the slow separation of the two cells are successfully followed in the mathematical simulation. Erythrocyte-erythrocyte adhesion forces and energies are quantified and matched with experimental data. The changes observed on erythrocyte-erythrocyte interactions may give important insights about the pathophysiological relevance of fibrinogen and erythrocyte aggregation in hindering microcirculatory blood flow.

Application of machine learning in predicting blood flow and red cell distribution in capillary vessel networks

Capillary blood vessels in the body partake in the exchange of gas and nutrients with tissues. They are interconnected via multiple vascular junctions forming the microvascular network. Distributions of blood flow and red cells (RBCs) in such networks are spatially uneven and vary in time. Since they dictate the pathophysiology of tissues, their knowledge is important. Theoretical models used to obtain flow and RBC distribution in large networks have limitations as they treat each vessel as a one-dimensional segment and do not explicitly consider cell-cell and cell-vessel interactions. High-fidelity computational models that accurately model each individual RBC are computationally too expensive to predict haemodynamics in large vascular networks and over a long time. Here we investigate the applicability of machine learning (ML) techniques to predict blood flow and RBC distributions in physiologically realistic vascular networks. We acquire data from high-fidelity simulations of deformable RBC suspension flowing in the networks. With the flow and haematocrit specified at an inlet of vasculature, the ML models predict the time-averaged flow rate and RBC distributions in the entire network, time-dependent flow rate and haematocrit in each vessel and vascular bifurcation in isolation over a long time, and finally, simultaneous spatially and temporally evolving quantities through the vessel hierarchy in the networks.

A new membrane formulation for modelling the flow of stomatocyte, discocyte, and echinocyte red blood cells

In this work, a numerical model that enables simulation of the deformation and flow behaviour of differently aged Red Blood Cells (RBCs) is developed. Such cells change shape and decrease in deformability as they age, thus impacting their ability to pass through the narrow capillaries in the body. While the body filters unviable cells from the blood naturally, cell aging poses key challenges for blood stored for transfusions. Therefore, understanding the influence RBC morphology and deformability have on their flow is vital. While several existing models represent young Discocyte RBC shapes well, a limited number of numerical models are developed to model aged RBC morphologies like Stomatocytes and Echinocytes. The existing models are also limited to shear and stretching simulations. Flow characteristics of these morphologies are yet to be investigated. This paper aims to develop a new membrane formulation for the numerical modelling of Stomatocyte, Discocytes and Echinocyte RBC morphologies to investigate their deformation and flow behaviour. The model used represents blood plasma using the Lattice Boltzmann Method (LBM) and the RBC membrane using the discrete element method (DEM). The membrane and the plasma are coupled by the Immersed Boundary Method (IBM). Previous LBM-IBM-DEM formulations represent RBC membrane response based on forces generated from changes in the local area, local length, local bending, and cell volume. In this new model, two new force terms are added: the local area difference force and the local curvature force, which are specially incorporated to model the flow and deformation behaviour of Stomatocytes and Echinocytes. To verify the developed model, the deformation behaviour of the three types of RBC morphologies are compared to well-characterised stretching and shear experiments. The flow modelling capabilities of the method are then demonstrated by modelling the flow of each cell through a narrow capillary. The developed model is found to be as accurate as benchmark Smoothed Particle Hydrodynamics (SPH) approaches while being significantly more computationally efficient.

A numerical study on drug delivery via multiscale synergy of cellular hitchhiking onto red blood cells

Red blood cell (RBC)-hitchhiking, in which different nanocarriers (NCs) shuttle on the erythrocyte membrane and disassociate from RBCs to the first organ downstream of the intravenous injection spot, has recently been introduced as a solution to enhance target site uptake. Several experimental studies have already approved that cellular hitchhiking onto the RBC membrane can improve the delivery of a wide range of NCs in mice, pigs, and ex vivo human lungs. In these studies, the impact of NC size, NC surface chemistry, and shear rate on the delivery process and biodistribution has been widely explored. To shed light on the underlying physics in this type of drug delivery system, we present a computational platform in the context of the lattice Boltzmann method, spring connected network, and frictional immersed boundary method. The proposed algorithm simulates nanoparticle (NP) dislodgment from the RBC surface in shear flow and biomimetic microfluidic channels. The numerical simulations are performed for various NP sizes and RBC-NP adhesion strengths. In shear flow, NP detachment increases upon increasing the shear rate. RBC-RBC interaction can also significantly boost shear-induced particle detachment. Larger NPs have a higher propensity to be disconnected from the RBC surface. The results illustrate that changing the interaction between the NPs and RBCs can control the desorption process. All the findings agree with in vivo and in vitro experimental observations. We believe that the proposed setup can be exploited as a predictive tool to estimate optimum parameters in NP-bound RBCs for better targeting procedures in tissue microvasculature.

Aqueous humor dynamics in human eye: A lattice Boltzmann study

This paper presents a lattice Boltzmann model to simulate the aqueous humor (AH) dynamics in the human eye by involving incompressible Navier-Stokes flow, heat convection and diffusion, and Darcy seepage flow. Verifying simulations indicate that the model is stable, convergent and robust. Further investigations were carried out, including the effects of heat convection and buoyancy, AH production rate, permeability of trabecular meshwork, viscosity of AH and anterior chamber angle on intraocular pressure (IOP). The heat convection and diffusion can significantly affect the flow patterns in the healthy eye, and the IOP can be controlled by increasing the anterior chamber angle or decreasing the secretion rate, the drainage resistance and viscosity of AH. However, the IOP is insensitive to the viscosity of AH, which may be one of the causes that the viscosity would not have been considered as a factor for controlling the IOP. It's interesting that all these factors have more significant influences on the IOP in pathologic eye than healthy one. The temperature difference and the eye-orientation have obvious influence on the cornea and iris wall shear stresses. The present model and simulation results are expected to provide an alternative tool and theoretical reference for the study of AH dynamics.

Numerical simulation of transport and adhesion of thermogenic nano-carriers in microvessels

Externally triggered thermogenic nanoparticles (NPs) are potential drug carriers and heating agents for drug delivery and hyperthermia. A good understanding of the transport and adhesion behaviors of NPs in microvessels is conducive to improving the efficiency of NP-mediated treatment. Given the thermogenesis of NPs and interactions of NP-blood flow, NP-NP, NP-red blood cell (RBC) and ligand-receptor, the movement of NPs in blood flow was modeled using a hybrid immersed boundary and coupled double-distribution-function lattice Boltzmann method. Results show that the margination probability of NPs toward the vessel wall was evidently increased by NP thermogenesis owing to the noticeable variation in blood flow velocity distribution, thereby enhancing their adhesion to the target region. NP-RBC collision can promote NP movement to the acellular layer in microvessels to increase the NP adhesion rate. The number of adhered smaller NPs was larger than that of the larger NPs having the same ligand density due to the enhancement of Brownian force although their adhesion was relatively less firm. Compared with the NPs with a regular shape, the irregularly shaped NPs can adhere to the vessel wall more readily and strongly as a result of the higher turbulence levels caused by NP-blood flow interaction and relatively higher ligand density, which led to a higher rate of NP adhesion.

Multiscale modeling of protein membrane interactions for nanoparticle targeting in drug delivery

Nanoparticle (NP)-based imaging and drug delivery systems for systemic (e.g. intravenous) therapeutic and diagnostic applications are inherently a complex integration of biology and engineering. A broad range of length and time scales are essential to hydrodynamic and microscopic molecular interactions mediating NP (drug nanocarriers, imaging agents) motion in blood flow, cell binding/uptake, and tissue accumulation. A computational model of time-dependent tissue delivery, providing in silico prediction of organ-specific accumulation of NPs, can be leveraged in NP design and clinical applications. In this article, we provide the current state-of-the-art and future outlook for the development of predictive models for NP transport, targeting, and distribution through the integration of new computational schemes rooted in statistical mechanics and transport. The resulting multiscale model will comprehensively incorporate: (i) hydrodynamic interactions in the vascular scales relevant to NP margination; (ii) physical and mechanical forces defining cellular and tissue architecture and epitope accessibility mediating NP adhesion; and (iii) subcellular and paracellular interactions including molecular-level targeting impacting NP uptake.

Endothelial Cell Targeting by cRGD-Functionalized Polymeric Nanoparticles under Static and Flow Conditions

Since α_vβ₃ integrin is a key component of angiogenesis in health and disease, Arg-Gly-Asp (RGD) peptide-functionalized nanocarriers have been investigated as vehicles for targeted delivery of drugs to the α_vβ₃ integrin-overexpressing neovasculature of tumors. In this work, PEGylated nanoparticles (NPs) based on poly(lactic-co-glycolic acid) (PLGA) functionalized with cyclic-RGD (cRGD), were evaluated as nanocarriers for the targeting of angiogenic endothelium. For this purpose, NPs (~300 nm) functionalized with cRGD with different surface densities were prepared by maleimide-thiol chemistry and their interactions with human umbilical vein endothelial cells (HUVECs) were evaluated under different conditions using flow cytometry and microscopy. The cell association of cRGD-NPs under static conditions was time-, concentration- and cRGD density-dependent. The interactions between HUVECs and cRGD-NPs dispersed in cell culture medium under flow conditions were also time- and cRGD density-dependent. When washed red blood cells (RBCs) were added to the medium, a 3 to 8-fold increase in NPs association to HUVECs was observed. Moreover, experiments conducted under flow in the presence of RBC at physiologic hematocrit and shear rate, are a step forward in the prediction of in vivo cell–particle association. This approach has the potential to assist development and high-throughput screening of new endothelium-targeted nanocarriers.

Multiscale modeling of hemolysis during microfiltration

In this paper, we propose a multiscale numerical algorithm to simulate the hemolytic release of hemoglobin (Hb) from red blood cells (RBCs) flowing through sieves containing micropores with mean diameters smaller than RBCs. Analyzing the RBC damage in microfiltration is important in the sense that it can quantify the sensitivity of human erythrocytes to mechanical hemolysis while they undergo high shear rate and high deformation. Here, the numerical simulations are carried out via lattice Boltzmann method and spring connected network (SN) coupled by an immersed boundary method. To predict the RBC sublytic damage, a sub-cellular damage model derived from molecular dynamic simulations is incorporated in the cellular solver. In the proposed algorithm, the local RBC strain distribution calculated by the cellular solver is used to obtain the pore radius on the RBC membrane. Index of hemolysis (IH) is calculated by resorting to the resulting pore radius and solving a diffusion equation considering the effects of steric hinderance and increased hydrodynamic drag due to the size of the hemoglobin molecule. It should be mentioned that current computational hemolysis models usually utilize empirical fitting of the released free hemoglobin (Hb) in plasma from damaged RBCs. These empirical correlations contain ad hoc parameters that depend on specific device and operating conditions, thus cannot be used to predict hemolysis under different conditions. In contrast to the available hemolysis model, the proposed algorithm does not have any empirical parameters. Therefore, it can predict the IH in microfilter with different sieve pore sizes and filtration pressures. Also, in contrast to empirical correlations in which the Hb release is related to shear stress and exposure time without considering the physical processes, the proposed model links flow-induced deformation of the RBC membrane to membrane permeabilization and hemoglobin release. In this paper, the cellular solver is validated by simulating optical tweezers experiment, shear flow experiment as well as an experiment to measure RBC deformability in a very narrow microchannel. Moreover, the shape of a single RBC at the rupture moment is compared with corresponding experimental data. Finally, to validate the damage model, the results obtained from our parametric study on the role of filtration pressure and sieve pore size in Hb release are compared with experimental data. Numerical results are in good agreement with experimental data. Similar to the corresponding experiment, the numerical results confirm that hemolysis increases with increasing the filtration pressure and reduction in pore size on the sieve. While in experiment, the RBC pore size cannot be measured, the numerical results can quantify the RBC pore size. The numerical results show that at the sieve pore size of 2.2 μm above 25 cm Hg, RBC pore size is above 75 nm and RBCs experience rupture. More importantly, the results demonstrate that the proposed approach is independent from the operating conditions and it can estimate the hemolysis in a wide range of filtration pressure and sieve pore size with reasonable accuracy.

High-throughput 3D visualization of nanoparticles attached to the surface of red blood cells

Blood circulation is the main distribution route for systemic delivery and the possibility to manipulate red blood cells (RBCs) by attaching nanoparticles to their surface provides a great opportunity for cargo delivery into tissues. Nanocarriers attached to RBCs can be delivered to specific organs orders of magnitude faster than if injected directly into the bloodstream. Another advantage is a shielding from recognition by the immune system, thereby increasing the efficiency of delivery. We present a high-throughput microfluidic method that can monitor the shape of drifting cells due to interactions with nanoparticles and characterize the 3D dispersion of fluorescent silica nanoparticles at the surface of RBCs. The combination of fluorescence microscopy with image analysis demonstrates that the adsorption of silica nanoparticles onto the surface of RBCs is strongly influenced by electrostatic interactions. A reduced number of intact RBCs with increasing nanoparticle concentration beyond a certain threshold points to a toxicity mechanism associated with the nanoparticle adsorption at the surface of RBCs.

Heterogeneous blood flow in microvessels with applications to nanodrug transport and mass transfer into tumor tissue

Nanodrug transport in tumor microvasculature and deposition/extravasation into tumor tissue are an important link in the nanodrug delivery process. Considering heterogeneous blood flow, such a dual process is numerically studied. The hematocrit distribution is solved by directly considering the forces experienced by the red blood cells (RBCs), i.e., the wall lift force and the random cell collision force. Using a straight microvessel as a test bed, validated computer simulations are performed to determine blood flow characteristics as well as the resulting nanodrug distribution and extravasation. The results confirm that RBCs migrate away from the vessel wall, leaving a cell-free layer (CFL). Nanodrug particles tend to preferentially accumulate in the CFL, leading to increased concentration near the endothelial surface layer. However, shear-induced NP diffusion is diminished within the CFL, causing to a much slower lateral transport rate into tumor tissue. These competing effects determine the NP deposition/extravasation rates. The present modeling framework and NP flux results provide new physical insight. The analysis can be readily extended to simulations of NP transport in blood microvessels of actual tumors.

Modeling thermal inkjet and cell printing process using modified pseudopotential and thermal lattice Boltzmann methods

Pseudopotential lattice Boltzmann methods (LBMs) can simulate a phase transition in high-density ratio multiphase flow systems. If coupled with thermal LBMs through equation of state, they can be used to study instantaneous phase transition phenomena with a high-temperature gradient where only one set of formulations in an LBM system can handle liquid, vapor, phase transition, and heat transport. However, at lower temperatures an unrealistic spurious current at the interface introduces instability and limits its application in real flow system. In this study, we proposed new modifications to the LBM system to minimize a spurious current which enables us to study nucleation dynamic at room temperature. To demonstrate the capabilities of this approach, the thermal ejection process is modeled as one example of a complex flow system. In an inkjet printer, a thermal pulse instantly heats up the liquid in a microfluidic chamber and nucleates bubble vapor providing the pressure pulse necessary to eject droplets at high speed. Our modified method can present a more realistic model of the explosive vaporization process since it can also capture a high-temperature/density gradient at nucleation region. Thermal inkjet technology has been successfully applied for printing cells, but cells are susceptible to mechanical damage or death as they squeeze out of the nozzle head. To study cell deformation, a spring network model, representing cells, is connected to the LBM through the immersed boundary method. Looking into strain and stress distribution of a cell membrane at its most deformed state, it is found that a high stretching rate effectively increases the rupture tension. In other words, membrane deformation energy is released through creation of multiple smaller nanopores rather than big pores. Overall, concurrently simulating multiphase flow, phase transition, heat transfer, and cell deformation in one unified LB platform, we are able to provide a better insight into the bubble dynamic and cell mechanical damage during the printing process.

Label-free sorting of soft microparticles using a bioinspired synthetic cilia array

Isolating cells of interest from a heterogeneous population has been of critical importance in biological studies and clinical applications. In this study, a novel approach is proposed for utilizing an active ciliary system in microfluidic devices to separate particles based on their physical properties. In this approach, the bottom of the microchannel is covered with an equally spaced cilia array of various patterns which is actuated by an external stimuli. 3D simulations are carried out to study cilia-particle interaction and isolation dynamic in a microfluidic channel. It is observed that these elastic hair-like filaments can influence particle's trajectories differently depending on their biophysical properties. This modeling study utilizes immersed boundary method coupled with the lattice Boltzmann method. Soft particles and cilia are implemented through the spring connected network model and point-particle scheme, respectively. It is shown that cilia array with proper stimulation is able to continuously and non-destructively separate cells into subpopulations based on their size, shape, and stiffness. At the end, a design map for fabrication of a programmable microfluidic device capable of isolating various subpopulations of cells is developed. This biocompatible, label-free design can separate cells/soft microparticles with high throughput which can greatly complement existing separation technologies.

A parallel fluid-solid coupling model using LAMMPS and Palabos based on the immersed boundary method

The study of viscous fluid flow coupled with rigid or deformable solids has many applications in biological and engineering problems, e.g., blood cell transport, drug delivery, and particulate flow. We developed a partitioned approach to solve this coupled Multiphysics problem. The fluid motion was solved by Palabos (Parallel Lattice Boltzmann Solver), while the solid displacement and deformation was simulated by LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator). The coupling was achieved through the immersed boundary method (IBM). The code modeled both rigid and deformable solids exposed to flow. The code was validated with the Jeffery orbits of an ellipsoid particle in shear flow, red blood cell stretching test, and effective blood viscosity flowing in tubes. It demonstrated essentially linear scaling from 512 to 8192 cores for both strong and weak scaling cases. The computing time for the coupling increased with the solid fraction. An example of the fluid-solid coupling was given for flexible filaments (drug carriers) transport in a flowing blood cell suspensions, highlighting the advantages and capabilities of the developed code.

Numerical simulation of cell squeezing through a micropore by the immersed boundary method

The deformability of cells has been used as a biomarker to detect circulating tumor cells (CTCs) from patient blood sample using microfluidic devices with microscale pores. Successful separations of CTCs from a blood sample requires careful design of the micropore size and applied pressure. This paper presented a parametric study of cell squeezing through micropores with different size and pressure. Different membrane compressibility modulus was used to characterize the deformability of varying cancer cells. Nucleus effect was also considered. It shows that the cell translocation time though the micropore increases with cell membrane compressibility modulus and nucleus stiffness. Particularly, it increases exponentially as the micropore diameter or pressure decreases. The simulation results such as the cell squeezing shape and translocation time agree well with experimental observations. The simulation results suggest that special care should be taken in applying Laplace-Young equation (LYE) to microfluidic design due to the nonuniform stress distribution and membrane bending resistance.

Modeling and Synthesis of Ag and Ag/Ni Allied Bimetallic Nanoparticles by Green Method: Optical and Biological Properties

In the quest for environmental remediation which involves eco-friendly synthetic routes, we herein report synthesis and modeling of silver nanoparticles (Ag NPs) and silver/nickel allied bimetallic nanoparticles (Ag/Ni NPs) using plant-extract reduction method. Secondary metabolites in the leaf extract of Canna indica acted as reducing agent. Electronic transitions resulted in emergence of surface plasmon resonance in the regions of 416 nm (Ag NPs) and 421 nm (Ag/Ni NPs) during optical measurements. Further characterizations were done using TEM and EDX. Antimicrobial activity of the nanoparticles against clinical isolates was highly significant as P < 0.05. These findings suggest application of Ag NPs as antibacterial agent against E. coli, S. pyogenes, and antifungal agent against C. albicans. Possible antibacterial drugs against S. pyogenes and E. coli can also be designed using Ag/Ni nanohybrid based on their strong inhibition activities. Similarly, the enhanced SPR in the nanoparticles is suggested for applications in optical materials, as good absorbers and scatters of visible light. Theoretical model clarified that the experiment observation on the relationship between metallic nanoparticles penetration through peptidoglycan layers and the activeness of microbial species depends on the nature of the nanoparticles and pore size of the layer.

Targeting tumor associated macrophages: The new challenge for nanomedicine

The engineering of new nanomedicines with ability to target and kill or re-educate Tumor Associated Macrophages (TAMs) stands up as a promising strategy to induce the effective switching of the tumor-promoting immune suppressive microenvironment, characteristic of tumors rich in macrophages, to one that kills tumor cells, is anti-angiogenic and promotes adaptive immune responses. Alternatively, the loading of monocytes/macrophages in blood circulation with nanomedicines, may be used to profit from the high infiltration ability of myeloid cells and to allow the drug release in the bulk of the tumor. In addition, the development of TAM-targeted imaging nanostructures, can be used to study the macrophage content in solid tumors and, hence, for a better diagnosis and prognosis of cancer disease. The major challenges for the effective targeting of TAM with nanomedicines and their application in the clinic have already been identified. These challenges are associated to the undesirable clearance of nanomedicines by, the mononuclear phagocyte system (macrophages) in competing organs (liver, lung or spleen), upon their intravenous injection; and also to the difficult penetration of nanomedicines across solid tumors due to the abnormal vasculature and the excessive extracellular matrix present in stromal tumors. In this review we describe the recent nanotechnology-base strategies that have been developed to target macrophages in tumors.

Numerical simulation of the red blood cell aggregation and deformation behaviors in ultrasonic field

The objective of this paper is to propose an immersed boundary lattice Boltzmann method (IB-LBM) considering the ultrasonic effect to simulate red blood cell (RBC) aggregation and deformation in ultrasonic field. Numerical examples involving the typical streamline, normalized out-of-plane vorticity contours and vector fields in pure plasma under three different ultrasound intensities are presented. Meanwhile, the corresponding transient aggregation behavior of RBCs, with special emphasis on the detailed process of RBC deformation, is shown. The numerical results reveal that the ultrasound wave acted on the pure plasma can lead to recirculation flow, which contributes to the RBCs aggregation and deformation in microvessel. Furthermore, increasing the intensity of the ultrasound wave can significantly enhance the aggregation and deformation of the RBCs. And the formation of the RBCs aggregation leads to the fluctuated and dropped vorticity value of plasma in return.

Effect of fractional blood flow on plasma skimming in the microvasculature

Although redistribution of red blood cells at bifurcated vessels is highly dependent on flow rate, it is still challenging to quantitatively express the dependence of flow rate in plasma skimming due to nonlinear cellular interactions. We suggest a plasma skimming model that can involve the effect of fractional blood flow at each bifurcation point. To validate the model, it is compared with in vivo data at single bifurcation points, as well as microvascular network systems. In the simulation results, the exponential decay of the plasma skimming parameter M along fractional flow rate shows the best performance in both cases.

Nanoparticle transport and delivery in a heterogeneous pulmonary vasculature

Quantitative understanding of nanoparticles delivery in a complex vascular networks is very challenging because it involves interplay of transport, hydrodynamic force, and multivalent interactions across different scales. Heterogeneous pulmonary network includes up to 16 generations of vessels in its arterial tree. Modeling the complete pulmonary vascular system in 3D is computationally unrealistic. To save computational cost, a model reconstructed from MRI scanned images is cut into an arbitrary pathway consisting of the upper 4-generations. The remaining generations are represented by an artificially rebuilt pathway. Physiological data such as branch information and connectivity matrix are used for geometry reconstruction. A lumped model is used to model the flow resistance of the branches that are cut off from the truncated pathway. Moreover, since the nanoparticle binding process is stochastic in nature, a binding probability function is used to simplify the carrier attachment and detachment processes. The stitched realistic and artificial geometries coupled with the lumped model at the unresolved outlets are used to resolve the flow field within the truncated arterial tree. Then, the biodistribution of 200nm, 700nm and 2µm particles at different vessel generations is studied. At the end, 0.2-0.5% nanocarrier deposition is predicted during one time passage of drug carriers through pulmonary vascular tree. Our truncated approach enabled us to efficiently model hemodynamics and accordingly particle distribution in a complex 3D vasculature providing a simple, yet efficient predictive tool to study drug delivery at organ level.

A Cellular Model of Shear-Induced Hemolysis

A novel model is presented to study red blood cell (RBC) hemolysis at cellular level. Under high shear rates, pores form on RBC membranes through which hemoglobin (Hb) leaks out and increases free Hb content of plasma leading to hemolysis. By coupling lattice Boltzmann and spring connected network models through immersed boundary method, we estimate hemolysis of a single RBC under various shear rates. First, we use adaptive meshing to find local strain distribution and critical sites on RBC membranes, and then we apply underlying molecular dynamics simulations to evaluate damage. Our approach comprises three sub-models: defining criteria of pore formation, calculating pore size, and measuring Hb diffusive flux out of pores. Our damage model uses information of different scales to predict cellular level hemolysis. Results are compared with experimental studies and other models in literature. The developed cellular damage model can be used as a predictive tool for hydrodynamic and hematologic design optimization of blood-wetting medical devices.

Predicting different adhesive regimens of circulating particles at blood capillary walls

A fundamental step in the rational design of vascular targeted particles is the firm adhesion at the blood vessel walls. Here, a combined lattice Boltzmann-immersed boundary model is presented for predicting the near-wall dynamics of circulating particles. A moving least squares algorithm is used to reconstruct the forcing term accounting for the immersed particle, whereas ligand-receptor binding at the particle-wall interface is described via forward and reverse probability distributions. First, it is demonstrated that the model predicts with good accuracy the rolling velocity of tumor cells over an endothelial layer in a microfluidic channel. Then, particle-wall interactions are systematically analyzed in terms of particle geometries (circular, elliptical with aspect ratios 2 and 3), surface ligand densities (0.3, 0.5, 0.7 and 0.9), ligand-receptor bond strengths (1 and 2) and Reynolds numbers (Re = 0.01, 0.1 and 1.0). Depending on these conditions, four different particle-wall interaction regimens are identified, namely not adhering, rolling, sliding and firmly adhering particles. The proposed computational strategy can be efficiently used for predicting the near-wall dynamics of particles with arbitrary geometries and surface properties and represents a fundamental tool in the rational design of particles for the specific delivery of therapeutic and imaging agents.

The Configuration of Copolymer Ligands on Nanoparticles Affects Adhesion and Uptake

Nanoparticles (NPs) are promising carriers for targeted drug delivery, photodynamic therapy, and imaging probes. A fundamental understanding of the dynamics of polymeric NP targeting to bilayer membranes is important to enhance the design of NPs for higher adhesion, binding percentage, and efficiency. In this study, dissipative particle dynamics simulations are applied to investigate the adhesion and uptake processes of the rod, spherical, and striped NPs to cell membranes. It is observed that the striped ligands can prevent NPs from rotating even in active rotation. We further optimize striped NP to a more stabilized structure. Uptake processes of NPs with different configurations are thoroughly investigated in our simulations and among which Janus NP are indicated to improve the penetration rate to 100%. These findings provide better insight into patterned NP design and may help fabricate new NPs for biomedical applications.

Generalized plasma skimming model for cells and drug carriers in the microvasculature

In microvascular transport, where both blood and drug carriers are involved, plasma skimming has a key role on changing hematocrit level and drug carrier concentration in capillary beds after continuous vessel bifurcation in the microvasculature. While there have been numerous studies on modeling the plasma skimming of blood, previous works lacked in consideration of its interaction with drug carriers. In this paper, a generalized plasma skimming model is suggested to predict the redistributions of both the cells and drug carriers at each bifurcation. In order to examine its applicability, this new model was applied on a single bifurcation system to predict the redistribution of red blood cells and drug carriers. Furthermore, this model was tested at microvascular network level under different plasma skimming conditions for predicting the concentration of drug carriers. Based on these results, the applicability of this generalized plasma skimming model is fully discussed and future works along with the model's limitations are summarized.

Characterization of nanoparticle binding dynamics in microcirculation using an adhesion probability function

Quantitative understanding of nanoparticles transport and adhesion dynamic in microcirculation is very challenging due to complexity of fluid dynamics and imaging setup. In-vitro experiments within microfluidic channels showed the significant influence of shear rate, carrier size, particle-substrate chemistry and vessel geometry on particle deposition rate. However, there are few theoretical models that can accurately predict experimental results. We have developed a numerical model to predict nanoparticle transport and binding dynamics and verified with our previous in-vitro tests results. A binding probability function is used to simplify the carrier attachment and detachment processes. Our results showed that due to the complex dynamics of particle transport and adhesion mechanism, the correlation between binding probability and actual deposition rate is not linear. Using experimental data, it is shown that the binding probability of small particles changes slightly with shear rate whereas the chance of binding for big particles decreases exponentially with shear. Our particulate model also captured some phenomena that cannot be achieved by continuum approach such as accumulation of drug particles in close vicinity of vessel wall. In addition, the effects of channel geometry and antibody density on particle binding are discussed extensively. The results from our particulate approach agrees well with experimental data suggesting that it can be used as a simple, yet efficient predictive tool for studying drug carrier binding in microcirculation.

Nanomaterials for Biosensing Applications

Nanomaterials have shown tremendous potentials to impact the broad field of biological sensing. Nanomaterials, with extremely small sizes and appropriate surface modifications, allow intimate interaction with target biomolecules. [...].