FAIR VISION for TOP-NT

项目摘要：脊髓和大脑创伤(神经创伤)合计影响超过250万人 美国每年的人口，在医疗保健和生产力损失方面的经济成本为800亿美元。但也很精确 影响康复的病理生理过程仍然知之甚少。这种知识的缺乏限制了 在动物模型中治疗发展的可靠性，并限制跨物种和进入人类的翻译。 问题的一部分是神经创伤本质上是复杂的，涉及到对中枢神经系统的异质性损害。 神经系统(CNS)，人体最复杂的器官系统。这导致了各种各样的中枢神经系统 跨不同数据源和多个分析尺度的症状。多尺度 异质性使脊髓损伤(SCI)和创伤性脑损伤(TBI)难以理解使用 关注单一终点以测试治疗效果的传统分析方法。单人 端点测试为了解描述整体的复杂更改系统提供了一个狭窄的窗口 脊髓损伤和脑外伤的证候。从这个意义上说，复杂的神经创伤从根本上来说是一个需要巨大- 数据分析，以评估基本发现和跨物种转换的重复性。对于建议的 顶视合作协议我们将：1)大规模整合临床前神经创伤数据；2) 开发尖端多维分析的新应用，以理解复杂的神经创伤 数据；3)在多PI单中心的目标大数据到平台实验中验证生物功能模式 (UG3期)和多中心(UH3期)研究。拟议项目的目标是开发一种 用于临床前发现、可重复性测试和翻译发现的集成工作流 不同类型的神经创伤。考虑到NIH之前的资助，我们的团队处于有利地位，可以执行这个项目 我们建立了迄今为止最大的多中心、多物种神经创伤数据存储库之一，存储了详细的 近N=5000名临床前受试者和20,000多个精选变量的多维结果数据。我们 将利用这些现有数据资源并应用数据科学的最新创新来呈现复杂的 将多维终端数据转换为强健的症状模式，可通过 研究人员和临床医生用于发现、假设生成和最终转换结果测试。

PROJECT SUMMARY: Trauma to the spinal cord and brain (neurotrauma) together impact over 2.5 million people per year in the US, with economic costs of $80 billion in healthcare and loss-of-productivity. Yet precise pathophysiological processes impacting recovery remain poorly understood. This lack of knowledge limits the reliability of therapeutic development in animal models and limits translation across species and into humans. Part of the problem is that neurotrauma is intrinsically complex, involving heterogeneous damage to the central nervous system (CNS), the most complex organ system in the body. This results in a multifarious CNS syndrome spanning across heterogeneous data sources and multiple scales of analysis. Multi-scale heterogeneity makes spinal cord injury (SCI) and traumatic brain injury (TBI) difficult to understand using traditional analytical approaches that focus on a single endpoint for testing therapeutic efficacy. Single endpoint-testing provides a narrow window into the complex system of changes that describe the holistic syndromes of SCI and TBI. In this sense, complex neurotrauma is fundamentally a problem that requires big- data analytics to evaluate reproducibility in basic discovery and cross-species translation. For the proposed TOP-VISION cooperative agreement we will: 1) integrate preclinical neurotrauma data on a large-scale; 2) develop novel applications of cutting-edge multidimensional analytics to make sense of complex neurotrauma data; and 3) validate bio-functional patterns in targeted big-data-to-bench experiments in multi-PI single center (UG3 phase), and multicenter (UH3 phase) studies. The goal of the proposed project is to develop an integrated workflow for preclinical discovery, reproducibility testing, and translational discovery both within and across neurotrauma types. Our team is well-positioned to execute this project given that with prior NIH funding we built one of the largest multicenter, multispecies repositories of neurotrauma data to-date, housing detailed multidimensional outcome data on nearly N=5000 preclinical subjects and over 20,000 curated variables. We will leverage these existing data resources and apply recent innovations from data science to render complex multidimensional endpoint data into robust syndromic patterns that can be visualized and explored by researchers and clinicians for discovery, hypothesis-generation and ultimately translational outcome testing.