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CAREER: Multiscale Modeling of Thrombus Formation and its Response to External Loads

CAREER: Multiscale Modeling of Thrombus Formation and its Response to External Loads
职业:血栓形成的多尺度建模及其对外部负载的响应
批准号:
2340696
负责人:
Jifu Tan
金额:
$50.68万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2024
资助国家:
美国
项目状态:
未结题
起止时间:
2024-09-01 至 2029-08-31

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中文摘要
翻译
该学院早期职业发展(CAREER)奖将资助旨在提高我们在建模和预测患者血管中血栓(或凝块)形成和破裂方面的知识的研究。血凝块在帮助止血的同时,也会阻碍血管中的血液流动,导致高血压、心脏病发作和中风等心血管疾病。事实上,心血管疾病是全球死亡的主要原因。因此,了解血凝块形成及其在血液环境中的反应对于预测和治疗血凝块生长至关重要。然而,预测凝块生长和破裂是具有挑战性的,因为血流影响凝块的形成和组成,这反过来又决定了凝块的强度及其对流动的反应。另一方面,凝块的生长和组成也会改变血流。该奖项将支持致力于开发多尺度建模工具的研究,这些工具使用强大的计算机和人工智能来更好地预测凝块生长及其对脉动流、血细胞碰撞和血管扩张的反应。将使用3D打印硅基血管网络复制品进行实验确认。 此外,该项目将通过开发课程,采用交互式笔记本等创新教学工具来培训下一代工程师和科学家,重点关注代表性不足的少数民族,在计算思维,高性能计算和机器学习方面整合研究和教育。外展活动包括参加STEM节和组织工程夏令营,以激励K12学生进入STEM领域。 该CAREER项目的总体目标是开发一个多尺度模型,以预测不同流量条件下的凝块生长和反应。具体而言,它将:1)识别和研究关键无量纲参数对凝块生长和结构的影响,考虑流动、红细胞(RBC)、血小板和其他生物聚合物,例如纤维蛋白和血管性血友病因子;和3)预测凝块生长,破裂,并使用物理信息神经网络(或PINN)增强的一个-该项目是了解凝块力学和为手术和治疗的临床决策提供支持的关键一步。该项目将使首席研究员能够推进计算科学和工程的知识基础,并在生物力学和机械生物学方面建立长期的职业生涯。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This Faculty Early Career Development (CAREER) award will fund research that intends to advance our knowledge in modeling and predicting thrombus (or clot) formation and rupture in patient’s blood vessels. Clots, while aiding in stopping bleeding, can also obstruct blood flow in vessels, leading to cardiovascular diseases like hypertension, heart attack, and stroke. In fact, cardiovascular disease is the leading cause of death worldwide. Thus, understanding clot formation and its response in the live blood environment is crucial for predicting and treating clot growth. However, predicting clot growth and rupture is challenging, as blood flow impacts the formation and composition of a clot, which in-return determines the strength of the clot and its response to flow. On the other hand, the growth and composition of the clot changes the flow as well. This award will support research striving to develop multiscale modeling tools that use powerful computers and artificial intelligence to better predict clot growth and its response to pulsatile flows, collision from blood cells, and vessel dilation. Experimental validations will be performed using 3D printed silicon based vascular network replica. In addition, the project will integrate research and education by developing curriculum, employing innovative teaching tools like interactive notebooks to train the next generation or engineers and scientists, with a focus on underrepresented minorities, in computational thinking, high-performance computing, and machine learning. Outreach activities involve participation in STEM festivals and organizing engineering summer camps to inspire K12 students to enter the STEM field. The overall objective of this CAREER project is to develop a multiscale model to predict clot growth and response, under different flow conditions. Specifically, it will: 1) identify and investigate the impact of key dimensionless parameters on clot growth and structure considering flow, red blood cells (RBCs), platelets, and other biopolymers such as fibrin and von Willebrand factor; 2) characterize clot response including deformation and rupture due to pulsatile flow and vessel wall deformation with given composition; and 3) predict clot growth, rupture, and quantify its uncertainties in large scale vascular network using physics informed neural network (or PINN) augmented one-dimensional models. This project is a critical step toward understanding clot mechanics and providing support for clinical decision-making for surgery and treatment. This project will allow the Principal Investigator to advance the knowledge base in computational science and engineering and establish a long-term career in Biomechanics and Mechanobiology.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.
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ERI: Integration of Computational Modeling and Machine Learning for Clot Mechanics
  • 批准号:
    2301736
  • 项目类别:
    Standard Grant
  • 资助金额:
    $19.97万
  • 财政年份:
    2023
  • 负责人:
    Jifu Tan
  • 依托单位:
海外基金