数字风险评分能灵敏地识别是否存在α-突触核蛋白聚集或多巴胺能缺陷

Ann-Kathrin Schalkamp, Kathryn J Peall, Neil A Harrison, Valentina Escott-Price, Payam Barnaghi, Cynthia Sandor
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引用次数: 0

摘要

背景 使用数字传感器来长期被动地收集数据,为我们筛查普通人群疾病早期征兆的能力提供了一个进步。智能手表数据已被证明可在临床诊断前几年识别帕金森病(PD),但尚未在高危人群中与多巴胺能成像(DaTscan)或脑脊液(CSF)α-突触核蛋白种子扩增试验(SAA)等生物和病理标记物进行比较评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital risk score sensitively identifies presence of α-synuclein aggregation or dopaminergic deficit
Background Use of digital sensors to passively collect long-term offers a step change in our ability to screen for early signs of disease in the general population. Smartwatch data has been shown to identify Parkinson’s disease (PD) several years before the clinical diagnosis, however, has not been evaluated in comparison to biological and pathological markers such as dopaminergic imaging (DaTscan) or cerebrospinal fluid (CSF) alpha-synuclein seed amplification assay (SAA) in an at-risk cohort.
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