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

ERI：凝块力学计算建模和机器学习的集成

• 批准号: 2301736
• 负责人: Jifu Tan
• 金额: $ 19.97万
• 依托单位: Northern Illinois University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301736&HistoricalAwards=false
• 关键词: ERI; Integration; Computational; Modeling; Machine

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.

该工程研究启动（ERI）奖将支持将贡献与血凝块力学相关的新知识的研究。血块是一种固体物质，可以由血液中的细胞和蛋白质自发形成。凝块对止血至关重要，但如果它们不恰当地阻塞血管，也可能是危险的，例如，心脏病发作或中风。几十年的研究已经使人们了解了凝块形成的生物化学和细胞生物学，但是凝块是如何受到血流机械力的影响还不是很清楚。导致心脏病发作和中风的血块通常是在流动的血液中形成的，因此了解血流如何影响血块的形成对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根据血块成分数据建立血块强度的广义预测模型。这项研究将造福社会，使患者特定的凝块建模，以提高个性化医疗的目标。该项目涉及多个学科，包括机械工程、计算科学、生物医学工程、艺术与设计。多学科方法将被用作扩大代表性不足群体参与研究的外联努力的一部分。本研究的目的是通过集成一种新的介观模型和机器学习来提取其强度、韧性和动态模量，来表征外部负载下凝块的力学响应。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颗粒-连续体方法的血块力学多物理场模型，并使用机器学习模型来预测不同成分下的血块强度、韧性和动态模量。该项目将提高我们对单个组件之间的相互作用的认识，包括时间依赖性血小板收缩，如何促进凝块的整体机械响应，并通过开发新型开源高性能计算中尺度模型和机器学习模型来预测给定成分的凝块力学特性。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。