During clotting under flow, platelets bind and activate on collagen and release autocrinic factors such as ADP and thromboxane, while tissue factor (TF) on the damaged wall leads to localized thrombin generation. Towards patient-specific simulation of thrombosis, a multiscale approach was developed to account for: platelet signalling [neural network (NN) trained by pairwise agonist scanning (PAS), PAS-NN], platelet positions (lattice kinetic Monte Carlo, LKMC), wall-generated thrombin and platelet-released ADP/thromboxane convection-diffusion (partial differential equation, PDE) and flow over a growing clot (lattice Boltzmann). LKMC included shear-driven platelet aggregate restructuring. The PDEs for thrombin, ADP and thromboxane were solved by finite element method using cell activation-driven adaptive triangular meshing. At all times, intracellular calcium was known for each platelet by PAS-NN in response to its unique exposure to local collagen, ADP, thromboxane and thrombin. When compared with microfluidic experiments of human blood clotting on collagen/TF driven by constant pressure drop, the model accurately predicted clot morphology and growth with time. In experiments and simulations at TF at 0.1 and 10 molecule-TF/$\mu$m$^{2}$ and initial wall shear rate of 200 s$^{-1}$, the occlusive blockade of flow for a 60-$\mu$m channel occurred relatively abruptly at 600 and 400 s, respectively (with no occlusion at zero TF). Prior to occlusion, intrathrombus concentrations reached 50 nM thrombin, ~ 1 $\mu$M thromboxane and ~ 10 $\mu$M ADP, while the wall shear rate on the rough clot peaked at ~ 1000-2000 s$^{-1}$. Additionally, clotting on TF/collagen was accurately simulated for modulators of platelet cyclooxygenase-1, P2Y$_{1}$ and IP-receptor. This multiscale approach facilitates patient-specific simulation of thrombosis under hemodynamic and pharmacological conditions.

中文翻译：

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使用组合血小板信号的数据驱动模型对胶原蛋白/组织因子触发的血栓生长和血管闭塞进行多尺度模拟。

在流动状态下的凝结过程中，血小板在胶原蛋白上结合并激活，并释放诸如ADP和血栓烷之类的自激因子，而受损壁上的组织因子（TF）则导致局部凝血酶生成。为了针对患者的血栓形成模拟，开发了一种多尺度方法来解释：血小板信号[通过成对激动剂扫描（PAS），PAS-NN训练的神经网络（NN），血小板位置（晶格动力学Monte Carlo，LKMC），壁产生的凝血酶和血小板释放的ADP /血栓烷对流扩散（偏微分方程，PDE）并流过不断增长的血块（格子Boltzmann）。LKMC包括剪切驱动的血小板聚集体重组。凝血酶，ADP和血栓烷的PDE用细胞活化驱动的自适应三角网格法通过有限元方法求解。每时每刻，PAS-NN知道每种血小板的胞内钙是由于其对局部胶原，ADP，血栓烷和凝血酶的独特暴露所致。与通过恒压降驱动的胶原/ TF上的人血凝块的微流实验相比，该模型可以准确预测血凝块的形态和随时间的增长。在TF为0.1和10分子TF / $ \ mu $ m $ ^ {2} $和初始壁剪切率为200 s $ ^ {-1} $的实验和模拟中，对于60-通道分别在600和400 s相对突然发生（在零TF处没有遮挡）。闭塞前，血栓内浓度达到50 nM凝血酶，〜1 $ \ mu $ M血栓烷和〜10 $ \ mu $ M ADP，而粗凝块上的壁剪切速率达到约1000-2000 s $ ^ {-1 } $。另外，准确模拟了TF /胶原蛋白上的凝血，用于血小板环加氧酶-1，P2Y $ _ {1} $和IP受体的调节剂。这种多尺度方法有助于在血液动力学和药理学条件下针对患者的血栓形成进行特定模拟。