Biomarkers for prognosis of closed-mechanism nerve injuries

摘要/项目摘要 闭合性周围神经损伤是最具破坏性的神经损伤之一，通常 完全丧失肢体的功能。神经再生，即再生后的级联变化 损伤，在快速伸展损伤中通常失败。相反，会形成神经瘤--在那里，丰富的疤痕组织取代了 神经再生的正常途径。不幸的是，在闭合机制损伤中，几乎没有诊断 确定神经瘤何时形成的线索--因此，临床管理基于等待失败 显然--手术结果因再生衰老而变得贫乏。 该项目的目标是验证外周血液中存在生物标记物的假设 并指导闭合性机械神经损伤的治疗。神经系统的结果很明显 与1)神经损伤的严重程度和2)愈合反应有关，无论是再生还是神经瘤形成。 我们已经建立了一种快速拉伸的神经损伤模型，它与挤压伤一起模拟临床上的 相关闭合机制神经损伤。我们的模型符合损伤类型/等级和组织学 人类神经损伤。我们建议同时利用循环蛋白质和RNA分子，以保持对 为未来选择最佳的诊断工具。机器学习算法将应用于大数据集 以提高诊断的准确性。 如果成功，该项目将为设计未来旨在评估神经的人体试验提供初步数据 受伤(R61/R33)。具体地说，我们需要知道蛋白质组学、转录组学或两者的组合 将提供更高的诊断准确性，以及为机器学习算法开发工作流程。这个 拟议的评估可能会在近端神经损伤中提供重要的临床实用价值，在这些情况下，预后 由于检测再生失败所需的时间较长，目前恢复较差。

ABSTRACT / PROJECT SUMMARY Closed-mechanism peripheral nerve injuries are among the most devastating neurologic injuries, often with complete loss of functional use of a limb. Nerve regeneration, i.e., the cascade of regenerative changes after injury, commonly fails in rapid-stretch injuries. Instead, a neuroma forms – where abundant scar tissue replaces the normal pathway for nerve regeneration. Unfortunately, in closed-mechanism injuries, there are few diagnostic clues to identify when neuromas will form – and thus, clinical management is based on waiting until failure is manifest – and the surgical outcomes are, consequentially, impoverished due to regenerative senescence. The goal of this project is to test the hypotheses that there are biomarkers in peripheral blood to provide prognosis and guide the management of closed mechanism nerve injuries. Neurological outcomes are clearly related to the 1) severity of nerve injury and to 2) healing response, whether regeneration or neuroma formation. We have established a rapid-stretch nerve injury model that mimics, along with crush injuries, the clinically relevant closed-mechanism nerve injuries. Our model matches the injury types/grades and histology seen in human nerve injuries. We propose utilizing both circulating protein and RNA molecules, to remain unbiased in selecting the optimal diagnostic tool for the future. Machine learning algorithms will be applied to the large dataset to improve diagnostic accuracy. If successful, this project will provide preliminary data for designing future human trials aimed at evaluating nerve injuries (R61/R33). Specifically, we will need to know whether proteomics or transcriptomics or a combination will provide greater diagnostic accuracy, as well as developing workflow for machine learning algorithms. The proposed evaluation will likely provide significant clinical utility in proximal nerve injuries, where the prognosis for recovery is currently poor due to the prolonged time required to detect failed regeneration.