Translational Outcomes Project: Visualizing Syndromic Information and Outcomes for Neurotrauma (TOP-VISION)

转化结果项目：可视化神经创伤的症状信息和结果 (TOP-VISION)

• 批准号: 10092617
• 负责人: JACQUELINE C BRESNAHAN
• 金额: $ 32.3万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10092617
• 关键词: Address; Affect; Anatomy; Animal Model; Area; Behavioral; Big Data; Big Data Methods; Biological; Biological Markers; Brain; Brain Injuries; Clinical; Collaborations; Complex; Data; Data Collection; Data Commons; Data Element; Data Pooling; Data Science; Data Sources; Detection; Drug Targeting; FAIR principles; Functional disorder; Funding; Generations; Goals; Healthcare; Heterogeneity; Housing; Human; Individual; Injury; Knowledge; Machine Learning; Medical; Modeling; Modernization; Molecular; Multiple Trauma; Mus; Nervous System Trauma; Neuraxis; Outcome; Outcome Assessment; Pattern; Phase; Physiological; Positioning Attribute; Precision Health; Prevalence; Process; Rattus; Recovery; Recovery of Function; Reproducibility; Research Personnel; Rodent; Sensitivity and Specificity; Severities; Site; Spinal Cord; Spinal Injuries; Spinal cord injury; Syndrome; System; Testing; Therapeutic; Time; Translations; Trauma; Traumatic Brain Injury; Traumatic CNS injury; Treatment Efficacy; U-Series Cooperative Agreements; United States National Institutes of Health; Validation; Weight; Work; bench to bedside; biobehavior; biomarker discovery; body system; clinically relevant; cost; data curation; data integration; data resource; data warehouse; diverse data; economic cost; economic impact; experimental study; functional outcomes; heterogenous data; innovation; insight; large scale data; neuroinflammation; novel; pre-clinical; precision medicine; preclinical study; predictive modeling; productivity loss; repository; response to injury; spinal cord and brain injury; success; therapeutic development; therapeutic evaluation; tool

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.

脊髓和大脑创伤（神经创伤）共同影响超过250万人 在美国，每年有800亿人死亡，医疗保健和生产力损失的经济成本高达800亿美元。但很精确 影响恢复的病理生理过程仍然知之甚少。这种知识的缺乏限制了 在动物模型中的治疗开发的可靠性和限制跨物种和人类的翻译。 部分问题在于神经创伤本质上是复杂的，涉及到中枢神经系统的异质性损伤。 神经系统（CNS）是人体内最复杂的器官系统。这导致了一个多样化的中枢神经系统 综合征跨越异构数据源和多个分析尺度。多尺度 异质性使得脊髓损伤（SCI）和创伤性脑损伤（TBI）难以使用 传统的分析方法侧重于测试治疗效果的单一终点。单个 端点测试提供了一个狭窄的窗口，进入描述整体变化的复杂系统。 SCI和TBI综合征。从这个意义上说，复杂的神经创伤从根本上说是一个需要大- 数据分析，以评估基本发现和跨物种翻译的可重复性。拟议 TOP-VISION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;和3）在多PI单中心的目标大数据到实验室实验中验证生物功能模式 (UG3期）和多中心（UH 3期）研究。拟议项目的目标是开发一个 用于临床前发现、再现性测试和转化发现的集成工作流程， 不同类型的神经创伤我们的团队有能力执行这个项目，因为有了之前的NIH资助， 我们建立了迄今为止最大的多中心，多物种神经创伤数据库之一， 近N=5000名临床前受试者和超过20，000个策划变量的多维结局数据。我们 我将利用这些现有的数据资源，并应用数据科学的最新创新， 多维终点数据转化为强大的综合征模式，可以可视化和探索， 研究人员和临床医生的发现，假设生成和最终的翻译结果测试。