A nanomagnetic platform technology to characterize traumatic brain injury using brain derived extracellular vesicles

在美国，每年有280万人因脑外伤而寻求医疗救治 损伤(TBI)，其中80%被认为是轻微的TBI(即脑震荡)。即使是在那些 对于轻度损伤，10%-20%的人将遭受长期残疾，包括癫痫发作和 情绪和行为问题。创伤性脑损伤护理面临的主要挑战之一是 对这种异质性损伤进行分类，并确定这些慢性损伤的风险 减损。常规成像研究，包括磁共振成像(MRI)和 计算机断层扫描(CT)通常用于脑外伤的分类，但不能可靠地捕捉到 全面的损伤，特别是在那些轻伤的患者。目前，几乎没有 分子标记有助于评估个体独特的损伤和随后的 油田迫切需要与这些变化相关的恢复和生物标志物 内表型，跟踪疾病的进展，并预测临床结果。致信地址 这一挑战，我们建议开发一个基于微芯片的平台，可以用来 利用脑源性循环中发现的RNA货物表征脑损伤及其恢复 胞外小泡(EV)，包括外体。与以前的工作不同，以前的工作主要集中在 单个生物标记物，我们的方法测量一组循环的肠道病毒miRNA标记物 利用机器学习算法，更全面地捕捉伤者的状态和 恢复中的大脑。我们的方案结合了表面标记特定的纳米磁隔离 来自各种细胞类型的脑源性EVS，使用RNA测序发现生物标记物，以及 EV miRNA货物损伤和恢复状态的机器学习处理 TBI。

In the United States, 2.8 million people every year seek medical attention for traumatic brain injury (TBI), of which 80% are considered to have a mild TBI (i.e. concussion). Even in those with mild injuries, 10-20% of individuals will suffer long-term disability including seizures and emotional and behavioral issues. One of the primary challenges in TBI care is appropriately classifying this heterogeneous injury and identifying patients at risk for these chronic impairments. Conventional imaging studies, including magnetic resonance imaging (MRI) and computed tomography (CT), are commonly used to classify TBI, but do not reliably capture the full extent of the injury, particularly in those patients with mild injuries. Currently, there are few molecular markers to assist in the assessment of an individual's unique injury and subsequent recovery and biomarkers are desperately needed in the field that correlate with these varied endophenotypes, track the progress of the disease, and predict clinical outcomes. To address this challenge, we propose to develop a microchip-based platform that can be used to characterize TBI and its recovery using the RNA cargo found in brain-derived circulating extracellular vesicles (EVs), including exosomes. Unlike prior work that has mainly focused on single biomarkers, our approach measures a panel of circulating EV miRNA markers processed with machine learning algorithms, to more comprehensively capture the state of the injured and recovering brain. Our proposal combines surface marker-specific nanomagnetic isolation of brain-derived EVs from a variety of cell types, biomarker discovery using RNA sequencing, and machine learning processing of EV miRNA cargo to measure the state of injury and recovery in TBI.