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The behaviors of ferromagnetic nano-particles in and around blood vessels under applied magnetic fields

机译:磁场作用下铁磁性纳米粒子在血管内及其周围的行为

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摘要

In magnetic drug delivery, therapeutic magnetizable particles are typically injected into the blood stream and magnets are then used to concentrate them to disease locations. The behavior of such particles in-vivo is complex and is governed by blood convection, diffusion (in blood and in tissue), extravasation, and the applied magnetic fields. Using physical first-principles and a sophisticated vessel-membrane-tissue (VMT) numerical solver, we comprehensively analyze in detail the behavior of magnetic particles in blood vessels and surrounding tissue. For any blood vessel (of any size, depth, and blood velocity) and tissue properties, particle size and applied magnetic fields, we consider a Krogh tissue cylinder geometry and solve for the resulting spatial distribution of particles. We find that there are three prototypical behaviors (blood velocity dominated, magnetic force dominated, and boundary-layer formation) and that the type of behavior observed is uniquely determined by three non-dimensional numbers (the magnetic-Richardson number, mass Pclet number, and Renkin reduced diffusion coefficient). Plots and equations are provided to easily read out which behavior is found under which circumstances (Figs. 58). We compare our results to previously published in-vitro and in-vivo magnetic drug delivery experiments. Not only do we find excellent agreement between our predictions and prior experimental observations, but we are also able to qualitatively and quantitatively explain behavior that was previously not understood.
机译:在磁性药物输送中,通常将可治疗的可磁化颗粒注入血流,然后使用磁体将其集中到疾病部位。这种颗粒在体内的行为是复杂的,并受血液对流,扩散(在血液和组织中),外渗和施加的磁场的控制。使用物理第一性原理和复杂的血管膜组织(VMT)数值求解器,我们全面详细地分析了血管和周围组织中磁性颗粒的行为。对于任何血管(具有任何大小,深度和血流速度)以及组织特性,粒径和所施加的磁场,我们考虑克罗格组织圆柱体的几何形状并求解所得的粒子空间分布。我们发现存在三种原型行为(以风速为主,以磁力为主导以及边界层形成),并且观察到的行为类型由三个无量纲的数字(磁-理查森数,质量Pclet数,和Renkin降低了扩散系数)。提供了图表和方程式,以轻松读取在哪种情况下发现的行为(图58)。我们将我们的结果与以前发表的体外和体内磁性药物递送实验进行比较。我们不仅发现我们的预测与先前的实验观察结果之间具有极好的一致性,而且还能够定性和定量地解释以前无法理解的行为。

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