Biomarkers for prognosis of closed-mechanism nerve injuries

闭合性神经损伤预后的生物标志物

• 批准号: 10742745
• 负责人: MARK MAHAN
• 金额: $ 44.06万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10742745
• 关键词: Address; Apraxias; Area Under Curve; Biological Assay; Biological Markers; Biology; Biomechanics; Blood; Categories; Cicatrix; Clinical; Clinical Management; Clinical Research; Complex; Crush Injury; Data; Data Set; Diagnosis; Diagnostic; Diagnostic tests; Early Diagnosis; Early Intervention; Elasticity; Esthesia; Evaluation; Evidence based intervention; Expert Opinion; Failure; Functional disorder; Future; Goals; Growth; Health Care Costs; Histology; Hospitalization; Hour; Human; Individual; Injury; Laboratory Study; Lead; Limb structure; Machine Learning; Methodology; Methods; Modeling; Movement; Multiple Trauma; Muscular Atrophy; Natural regeneration; Nerve; Nerve Regeneration; Nerve Tissue; Nervous System Trauma; Neurological outcome; Neuroma; Operative Surgical Procedures; Outcome; Pain; Pathway interactions; Patients; Pattern; Performance; Peripheral nerve injury; Physicians; Plasma; Poverty; Prognosis; Property; Proteins; Proteomics; Qualifying; RNA; Recommendation; Recovery; Research; Rodent; Rupture; Severities; Specificity; Stretching; Techniques; Testing; Time; Tissues; Validation; Work; axon injury; biomarker identification; biomarker signature; biomarker validation; blood-based biomarker; clinically relevant; design; diagnostic accuracy; diagnostic tool; diagnostic value; evidence base; extracellular; functional loss; healing; improved; large datasets; loss of function; mRNA Expression; machine learning algorithm; molecular marker; nerve injury; novel; peripheral blood; pointed protein; productivity loss; prognosis biomarker; prognostic; prognostic value; programs; protein biomarkers; reconstruction; regenerative; reinnervation; repaired; research clinical testing; response; restoration; senescence; specific biomarkers; spinal cord and brain injury; success; surgery outcome; tool; transcriptome sequencing; transcriptomics

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.

摘要 /项目摘要 封闭力学的外周神经损伤是最具破坏性的神经系统损伤，通常 肢体的功能使用完全丧失。神经再生，即在 受伤，通常在快速拉伸伤害中失败。相反，神经瘤形式 - 丰富的疤痕组织替代 神经再生的正常途径。不幸的是，在封闭力学损伤中，诊断很少 识别神经瘤何时会形成的线索 - 因此，临床管理是基于等待直到失败为止的 显然 - 因此，由于再生感应，手术结局是贫穷的。 该项目的目的是测试外周血中有生物标志物的假设 预后和指导封闭机制神经损伤的管理。神经学显然是 与1）神经损伤的严重程度以及2）愈合反应有关，无论是再生还是神经瘤形成。 我们已经建立了一个快速拉伸的神经损伤模型，该模型在临床上模仿伴侣损伤。 相关的封闭力学神经损伤。我们的模型与在 人神经损伤。我们建议同时使用循环蛋白和RNA分子，以保持公正 为未来选择最佳诊断工具。机器学习算法将应用于大数据集 提高诊断准确性。 如果成功，该项目将提供初步数据，用于设计旨在评估神经的未来人体试验 受伤（R61/R33）。特别是，我们需要知道蛋白质组学或转录组学还是组合 将提供更高的诊断准确性，并为机器学习算法开发工作流程。 拟议的评估可能会在预后造成的代理租赁损伤中提供明显的临床效用 由于检测到再生所需的时间延长，目前恢复目前很差。