预测血小板壁上不同粘附制度的循环粒子行为
Predicting different adhesive regimens of circulating particles at blood capillary walls
AI总结：
本文提出结合格子玻尔兹曼浸入边界模型，用于预测循环粒子近壁动力学，通过分析粒子几何、表面配体密度等参数，识别四种粘附制度。
摘要：
在血管靶向粒子的理性设计中，血 vessel 壁上的牢固粘附是一个基本步骤。本文提出一种结合格子玻尔兹曼浸入边界模型的方法，用于预测循环粒子的近壁动力学。采用移动最小二乘算法重建浸入粒子的力项，通过正向和反向概率分布描述粒子-壁界面的配体-受体结合。首先证明该模型能准确预测微流体通道中肿瘤细胞在内皮层上的滚动速度。然后系统分析粒子-壁相互作用，考虑粒子几何（圆形、椭圆形，长宽比2和3）、表面配体密度（0.3, 0.5, 0.7和0.9）、配体-受体键强度（1和2）以及雷诺数（Re=0.01, 0.1和1.0）。根据这些条件，识别出四种不同的粒子-壁相互作用制度：不粘附、滚动、滑动和牢固粘附。所提出的计算策略可高效预测具有任意几何形状和表面性质的粒子的近壁动力学，代表了在特定递送治疗和成像剂的粒子理性设计中的基本工具。
英文摘要：
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.
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