Psi Beta, Nurse Scientist, National Institute of Nursing Research, Division of Intramural Research, National Institutes of Health, Bethesda, MD, USA.
J Nurs Scholarsh. 2019 Jan;51(1):17-25. doi: 10.1111/jnu.12445. Epub 2018 Oct 30.
To describe the collaborative framework used by National Institute of Nursing Research (NINR) investigators to advance symptom science and to provide a research exemplar.
The National Institutes of Health (NIH) Symptom Science Model (SSM) was developed to guide symptom science researchers to understand the molecular underpinnings of symptoms using innovative "omics" approaches. The process begins with a review of the literature to understand the state of the science of the symptoms of interest and is followed by cross-sectional, case-controlled, or longitudinal studies to identify potential biological correlates of these symptoms. The final steps include validation of these potential symptom biomarkers using multidisciplinary, collaborative, preclinical experiments, and proof-of-concept clinical trials.
Using the NIH SSM as a guide, the identification of biologic correlates of symptoms using omics and bioinformatic strategies depends on determining the distinct phenotype of the symptoms of interest. The identified biologic correlates of these symptoms are then validated for their functional relevance using in vitro and ex vivo experiments, or through proof-of-concept clinical trials. NINR investigators observed that activation of specific receptors in neural networks can trigger inflammation-related fatigue development and predispose patients to develop chronicity of symptoms. Specifically targeting these neural receptors can reduce symptom intensity.
Through using the NIH SSM as a guide, NINR investigators quickly generate data and discoveries that significantly advance symptom science by simultaneously collaborating with multiple experts and core laboratories to identify more correlates and validate their functional relevance in order to further understand the biological underpinnings of the symptoms of interest.
The collaborative framework used by NINR investigators as guided by the NIH SSM identifies functionally relevant clinical markers that can explain the biological underpinnings of symptoms and can be targeted to optimize symptom management.
描述国立卫生研究院（NIH）护理研究所（NINR）研究人员用于推进症状科学的协作框架，并提供一个研究范例。
NIH 症状科学模型（SSM）旨在指导症状科学研究人员使用创新的“组学”方法了解症状的分子基础。该过程首先是对文献进行审查，以了解感兴趣的症状的科学现状，然后进行横断面、病例对照或纵向研究，以确定这些症状的潜在生物学相关性。最后包括使用多学科、协作的临床前实验和概念验证临床试验验证这些潜在症状生物标志物。
使用 NIH SSM 作为指南，使用组学和生物信息学策略识别症状的生物学相关性取决于确定感兴趣症状的独特表型。然后，使用体外和离体实验或通过概念验证临床试验验证这些症状的生物学相关性的功能相关性。NINR 研究人员观察到，神经网络中特定受体的激活可以引发与炎症相关的疲劳发展，并使患者容易出现症状的慢性化。专门针对这些神经受体可以减轻症状强度。
通过使用 NIH SSM 作为指南，NINR 研究人员通过同时与多个专家和核心实验室合作，快速生成数据和发现，这些数据和发现极大地推进了症状科学，以识别更多相关性并验证其功能相关性，从而进一步了解感兴趣症状的生物学基础。
NINR 研究人员在 NIH SSM 指导下使用的协作框架确定了功能相关的临床标志物，这些标志物可以解释症状的生物学基础，并可以针对这些标志物进行优化以改善症状管理。
