The influence of size, shape and vessel geometry on nanoparticle distribution

Tan, Jifu; Shah, Samar; Thomas, Antony; Ou-Yang, H. Daniel; Liu, Yaling

doi:10.1007/s10404-012-1024-5

Cited by 173 publications

(134 citation statements)

References 52 publications

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“…This is in agreement with the findings from adhesion studies (3,(11)(12)(13)(14)17,18), as well as a recent direct-particle-tracking study by D'Apolito et al (7) in which two spherical particle sizes (1 mm and 3 mm) were investigated. It is conjectured that larger particles interact more readily with RBCs and, as a result of their frequent collisions with RBCs, are propelled toward the walls of a blood vessel sooner than smaller particles, which have less frequent interactions.…”

Section: Effect Of Particle Sizesupporting

confidence: 91%

“…This so-called enhanced permeability and retention effect (8) further opens up the possibility of delivering chemotherapeutic drugs passively and more specifically to tumor sites, thereby limiting any damage to healthy tissues (9,10). A number of recent experimental (3,7,(11)(12)(13)(14)(15)(16)(17) and theoretical (18)(19)(20)(21)(22)(23) studies have suggested that particles of certain sizes and shapes have a higher margination propensity. This would facilitate the diffusion of these particles into tumor sites by allowing them to reach the leaky blood vessel walls and, ultimately, the tumors.…”

Section: Introductionmentioning

confidence: 99%

“…This work differs from previous studies in a number of ways. First, all previous experimental studies, with only two exceptions (7,38), assessed margination propensity based on the number density, or, more precisely, the fluorescence intensity, of fluorescent particles that adhered to the channel wall after a particle suspension and subsequently a buffer solution passed through the channel (3,(11)(12)(13)(14)(16)(17)(18)39). Although adhesion is related to margination, adhesion also depends on other factors such as hydrodynamic drag, the densities of ligands and receptors grafted onto the particle surface and the wall channel, the particle shape and contact orientation, and the particle size relative to the CFL (10,16,17,20,21).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Direct Tracking of Particles and Quantification of Margination in Blood Flow

Carboni

Bognet

Bouchillon

et al. 2016

Biophysical Journal

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Margination refers to the migration of particles toward blood vessel walls during blood flow. Understanding the mechanisms that lead to margination will aid in tailoring the attributes of drug-carrying particles for effective drug delivery. Most previous studies evaluated the margination propensity of these particles via an adhesion mechanism, i.e., by measuring the number of particles that adhered to the channel wall. Although particle adhesion and margination are related, adhesion also depends on other factors. In this study, we quantified the margination propensity of particles of varying diameters (0.53, 0.84, and 2.11 mm) and apparent wall shear rates (30, 61, and 121 s À1 ) by directly tracking fluorescent particles flowing through a microfluidic channel. The margination parameter, M, is defined as the total number of particles found within the cell-free layers normalized by the total number of particles that passed through the channel. In this study, an M-value of 0.2 indicated no margination, which was observed for all particle sizes in water. In the case of blood, larger particles were found to have higher M-values and thus marginated more effectively than smaller particles. The corresponding M-values at the device outlet were 0.203, 0.223, and 0.285 for 0.53-, 0.84-, and 2.11-mm particles, respectively. At the inlet, the M-values for all particle sizes in blood were <0.2, suggesting that non-fully-developed flow and constriction may lead to demargination. For particle velocities transverse to the flow direction (v y ), all particle sizes showed a larger standard deviation of v y as well as a higher effective diffusivity when the particles were suspended in blood relative to water. These higher values are attributed to collisions between the blood cells and particles, further supporting recent simulation results. In terms of flow rates, for a given particle size, the higher the flow rate, the higher the M-value.

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Section: Effect Of Particle Sizesupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direct Tracking of Particles and Quantification of Margination in Blood Flow

Carboni

Bognet

Bouchillon

et al. 2016

Biophysical Journal

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“…In addition to screening the interaction between tumor cells and nanoparticles, the transportation of nanoparticles in vessel tissues has been investigated to help create better nanoparticles. (75) Platforms for screening vascularization are still in the early stage of development. No long-term study of the formation of blood vessels has been carried out in microfluidic channels.…”

Section: Tumor-on-a-chipmentioning

confidence: 99%

Organ-on-a-Chip Platforms for Drug Delivery and Cell Characterization: A Review

2015

Sensors and Materials

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“…Interestingly, the plasma skimming is recently revisited to develop new microchannels for detecting specific DNAs, proteins and cells by efficiently separating plasma from whole blood [9][10][11]. Also, it has been highlighted to accurately predict drug carrier distribution in the microvasculature [12][13][14][15][16][17][18]. For utilizing the plasma skimming to new applications in vitro and in vivo, it is crucial to quantitatively predict the redistribution of RBCs and plasma at bifurcations.…”

mentioning

confidence: 99%

Effect of fractional blood flow on plasma skimming in the microvasculature

2017

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Although redistribution of red blood cells at bifurcated vessels is highly dependent on flow rate, it is still challenging to quantitatively express the dependency 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. For validating the new 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 plasma skimming parameter, M , along fractional flow rate shows the best performance in both cases.Red blood cells (RBCs) in microvessels are concentrated on the vessel core. Subsequently, a cell-free layer (CFL) with a few micrometer thickness is observed on the vessel wall. The CFL leads asymmetric redistribution of hematocrit at each bifurcation, called plasma skimming effect. As a continuous process of plasma skimming in microvascular networks, the average hematocrit in capillary beds is lower than the systemic hematocrit as reported in many previous studies [1][2][3][4][5][6][7][8]. Interestingly, the plasma skimming is recently revisited to develop new microchannels for detecting specific DNAs, proteins and cells by efficiently separating plasma from whole blood [9][10][11]. Also, it has been highlighted to accurately predict drug carrier distribution in the microvasculature [12][13][14][15][16][17][18]. For utilizing the plasma skimming to new applications in vitro and in vivo, it is crucial to quantitatively predict the redistribution of RBCs and plasma at bifurcations.From the early 70s, several experiments for quantifying the plasma skimming were performed [2,[19][20][21][22][23] In this paper, we aim to mathematically model fractional blood flow in a simple and generalized manner in order to computationally study its significance in plasma skimming, and also to accurately predict plasma skimming in the microvasculature. For this task, a recently developed plasma skimming model [26] is taken, and extended to take into account the effect of fractional blood flow. This new model is then validated with experimental data at single bifurcation level, and also at microvascular network level.While there are other plasma skimming models [25,27], the model developed by Gould and Linninger [26] is considered due to its easy extensibility. The model is as follows:where H is the hematocrit, M is the plasma skimming parameter, ζ is the hematocrit change coefficient due to plasma skimming, Q is the flow rate, A is the crosssectional area of each vessel, and subscript 0, 1, and 2 indicate the parent, and two daughter vessels, respectively. Specifically, the plasma skimming parameter M is related to the cross-sectional distribution of RBCs near bifurcation. Small M represents that RBCs are highly concentrated on the vessel core. In other words, plasma dominant region, or CFL, is developed on the near wall region. The two separated regions, expressed as RBCs and plasma areas, lead to strong...

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The influence of size, shape and vessel geometry on nanoparticle distribution

Cited by 173 publications

References 52 publications

Direct Tracking of Particles and Quantification of Margination in Blood Flow

Direct Tracking of Particles and Quantification of Margination in Blood Flow

Organ-on-a-Chip Platforms for Drug Delivery and Cell Characterization: A Review

Effect of fractional blood flow on plasma skimming in the microvasculature

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