ERI: Integration of Computational Modeling and Machine Learning for Clot Mechanics

这个工程研究启动（ERI）奖将支持研究，这将有助于有关血液凝块力学的新知识。凝块是一种固体物质，可以从血液的细胞和蛋白质自发形成。 血凝块对于止血是必不可少的，但如果它们不适当地阻塞血管，例如心脏病发作或中风，也可能是危险的。几十年的研究已经导致了对凝块形成的生物化学和细胞生物学的理解，但是凝块如何受到血流机械力的影响还没有很好的理解。导致心脏病发作和中风的凝块通常在流动的血液中形成，因此了解流动力如何影响凝块形成对于预防或治疗这些疾病可能很重要。 这项研究将开发一种凝血模型。该项目将使用人工智能，利用凝块组成数据建立凝块强度的广义预测模型。 该研究将通过实现患者特定的凝块建模来造福社会，目标是改善个性化医疗。该项目跨越多个学科，包括机械工程，计算科学，生物医学工程，艺术和设计。多学科的方法将被用来作为推广工作的一部分，以扩大在research.The研究的代表性不足的群体的参与，其目的是通过整合一种新的介观模型和机器学习，以提取其强度，韧性和动态模量的外部load.This研究的特点凝块的机械响应。新模型将考虑凝块成分，如红细胞，血小板，纤维蛋白网络和血浆。该研究的具体目标是开发和验证基于混合粒子连续方法的凝块力学多物理模型，该方法具有异质组件，并应用机器学习模型来预测凝块强度，韧性和动态模量使用神经网络在各种组合物下。该项目将推进我们对个体成分之间相互作用的认识，包括时间依赖性血小板收缩，有助于凝块的整体机械响应，并且还通过开发新的开放式来源高性能计算中尺度模型和机器学习模型。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

This Engineering Research Initiation (ERI) award will support research that will contribute new knowledge related to the mechanics of blood clots. A clot is a solid substance that can form spontaneously from the cells and proteins of blood. Clots are essential to stopping bleeding from a wound, but also can be dangerous if they improperly block a blood vessel causing, for example, a heart attack or stroke. Decades of research have led to an understanding of the biochemistry and cell biology of clot formation, but how clots are affected by the mechanical forces of blood flow is not well understood. Clots that cause heart attack and stroke often form in flowing blood, thus understanding how the flow forces affect clot formation may be important to preventing or treating these conditions. This research will develop a model for clotting. The project will use artificial intelligence to make a generalized predictive model of clot strength using clot composition data. The research will benefit society by enabling patient-specific clot modeling with the goal of improving personalized medicine. The project spans several disciplines including mechanical engineering, computational science, biomedical engineering, and art and design. The multi-disciplinary approach will be used as part of an outreach effort to broaden participation of underrepresented groups in research.The objective of this research is to characterize clot mechanical response under external load through integration of a novel mesoscopic model and machine learning to extract its strength, toughness, and dynamic modulus. The novel model will consider clot components such as red blood cells, platelets, fibrin networks, and plasma. The specific aims of the research are to develop and validate a multiphysics model for clot mechanics based on a hybrid particle-continuum approach with heterogeneous components and apply machine learning models to predict clot strength, toughness, and dynamic modulus under various compositions using neural networks. The project will advance our knowledge of how the interplay between individual components, including the time-dependent platelet contraction, contribute to the overall mechanical response of the clot and also to predict the clot mechanical properties with given composition by developing novel open-source high performance computing mesoscale models and machine learning models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.