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）奖将支持研究，该研究将为与血块机制有关的新知识提供贡献。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。 凝块对于阻止伤口流血至关重要，但是如果不正确地阻止血管引起的血管，例如心脏病发作或中风也可能是危险的。数十年的研究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的机械力的影响。引起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可能对防止或治疗这些疾病很重要。 这项研究将开发一个用于凝结的模型。该项目将使用人工智能使用凝块组成数据来制作凝块强度的广义预测模型。 这项研究将通过启用患者特定的凝块建模，以改善个性化医学来使社会受益。该项目涵盖了几个学科，包括机械工程，计算科学，生物医学工程以及艺术与设计。多学科的方法将用作外展努力的一部分，以扩大代表性不足的小组的参与研究。该研究的目的是通过整合新型的介质模型和机器学习来提取其提取其外部负载下的凝块机械响应的目的力量，韧性和动态模量。新型模型将考虑凝块成分，例如红细胞，血小板，纤维蛋白网络和血浆。该研究的具体目的是基于与异质组件的杂化粒子 - 胞源方法开发和验证凝块机械师的多物理模型，并应用机器学习模型来预测凝块强度，韧性和动力模量在各种使用神经网络的情况下。该项目将促进我们了解各个组件之间的相互作用（包括时间依赖性血小板收缩）如何有助于凝块的整体机械响应，并通过开发新型的开放源高性能来预测具有给定组成的凝块机械性能计算中尺度模型和机器学习模型。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被视为值得通过评估的支持。