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

该工程研究启动奖(ERI)将支持将贡献与血液凝块机制相关的新知识的研究。凝块是一种固体物质，可以由血液中的细胞和蛋白质自发形成。血栓对止血是必不可少的，但如果它们不适当地阻塞血管，例如导致心脏病发作或中风，也可能是危险的。几十年的研究使人们对血栓形成的生物化学和细胞生物学有了了解，但血液流动的机械力是如何影响血栓的还不是很清楚。导致心脏病发作和中风的血栓通常在流动的血液中形成，因此了解流动力如何影响血栓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，利用凝块成分数据建立凝块强度的通用预测模型。这项研究将使患者特定的血栓建模成为可能，从而使社会受益，目的是改善个性化药物。该项目跨越了几个学科，包括机械工程、计算科学、生物医学工程和艺术与设计。这一多学科的方法将被用作扩大代表性不足群体参与研究的外展努力的一部分。本研究的目标是通过集成新的介观模型和机器学习来表征凝块在外部负荷下的力学响应，以提取其强度、韧性和动态弹性系数。新的模型将考虑血块成分，如红细胞、血小板、纤维蛋白网络和血浆。这项研究的具体目的是开发和验证基于混合粒子-连续介质方法的凝块力学多物理模型，并应用机器学习模型预测不同组成下的凝块强度、韧性和动态弹性系数。该项目将提高我们对单个组件之间的相互作用，包括依赖时间的血小板收缩，如何促进凝块的整体机械反应的了解，并通过开发新的开源高性能计算中尺度模型和机器学习模型来预测给定组成的凝块的力学特性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.