GCR: Programmable Nanorobots Integration with Magnetically-Driven Neuron and Brain Tissue Regeneration

在这个融合项目中，机械和生物医学工程师，纳米科学家和神经科学家对如何编程磁性纳米机器人以有效地在神经元网络内部进行通信和交互有了新的理解。 如果成功，这可能为非侵入性脑治疗提供一种可能的方法。对磁性纳米机器人细胞相互作用行为的基本分析可能为治疗神经退行性疾病、癫痫、慢性疼痛和脊髓损伤等顽固性疾病的新方法奠定基础。此外，该研究促进了纳米科学，机械工程，生物医学工程，神经科学和实验治疗学的学科整合。该项目为高中教师和学生、本科生和研究生提供了研究经验。 由融合努力产生的知识将形成新的框架，以催化脑组织再生和修复方面的科学发现和创新，并将提供一种强大的，可扩展的和可控的自动驾驶纳米机器人技术，在大脑环境中运输和运作。它还将增强对退化神经元的特定神经回路活动的长期变化的基本理解。一个可解释的人工智能框架提供了额外的定量分析，以补充生物学上合理的，不断重塑的，分析性的微血管网络模型。此外，结合电力电子领域的最新进展，该项目具有很高的潜力，有助于开发新的机器学习模型，提高研究人员研究3D细胞外基质中神经元生长行为的能力。该奖项反映了NSF的法定使命，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。

In this convergent project, mechanical and biomedical engineers, a nanoscientist, and neuroscientists develop new understanding of how to program magnetic nanorobots to effectively communicate and interact inside networks of neurons. If successful, this may provide a possible approach for non-invasive brain therapeutics. The fundamental analysis of the cellular interaction behavior of magnetic nanorobots may lay a foundation for novel and translatable approaches to treat intractable disorders such as neurodegeneration, epilepsy, chronic pain, and spinal cord injury. Additionally, the research promotes disciplinary integration across nanoscience, mechanical engineering, biomedical engineering, neuroscience and experimental therapeutics. The project provides research experiences for high school teacher and students, undergraduate and graduate students. The knowledge generated by the convergent effort will form novel frameworks to catalyze scientific discovery and innovation in brain tissue regeneration and repair, and will provide a powerful, scalable and controllable technology of self-driving nanorobots transporting and functioning inside the brain environment. It will also enhance fundamental understanding of long-term changes in the activity of specific neural circuits with degenerative neurons. An explainable artificial intelligence framework provides additional quantitative assays to complement a biologically plausible, continuously remodeling, analytical, microvascular network model. Furthermore, combined with recent advances in power electronics, this project holds a high potential for contributing to the development of a new machine learning model that improves researchers’ capacity for studying the growth behavior of neurons inside a 3D extracellular matrix.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.