Nat BME :神经退行性疾病的多元诊断的定义和预测

2020-08-03 MedSci原创 MedSci原创

神经退行性疾病影响着700多万美国人。每年医疗费用超过5000亿美元。这个公共卫生问题预计会更加恶化。

病理蛋白聚集是神经变性的一个主要疾病过程。随着时间的推移,这些蛋白质聚集物可能沿着大的白质纤维传播,在遥远的区域造成功能障碍。它们的毒性被认为部分是由炎症系统介导的。不同的神经退行性综合征的特征是特定蛋白质的聚集,典型的阿尔茨海默病包括淀粉样蛋白-β和相关蛋白τ。帕金森氏病涉及α-合成核苷,额颞叶痴呆症可能涉及跨活性反应dna结合蛋白43 kDa(tdp-43)。 大量的体外和动物研究表明,这些蛋白质相互作用,产生独特的伴随功能障碍。分子病理学导致细胞功能障碍的机制有多种。(也就是说,血管病变、胶质增生、神经细胞死亡或信号功能障碍)和细胞功能障碍可能比单纯斑块负担更能反映认知功能障碍。计算硬件和高级计算方法(如机器学习)的的成本不断降低,为通过定义新的数据算法的疾病亚类型来解析异构性提供了工具。

方法:所有数据都是通过综合神经退行性疾病数据库获得的,由宾夕法尼亚大学医院神经退行性疾病研究中心主办。一组专家神经病理学家评估了1659例患者尸检过程中6种分子病理特征(淀粉样蛋白-β、神经性斑块、τ、α-syNucin、tdp-43和泛素)和3种细胞病理特征(血管病变、神经元丢失和胶质变)的程度。所有患者都根据宾夕法尼亚大学机构审查委员会批准的协议给予知情同意。所有患者在尸检前都由其医生进行临床诊断。用Luminex法测定脑脊液中的分析物水平。传统的神经退行性疾病定义通常只占一到两种蛋白质的集合。在这里,作者寻求以一种无监督的方式将病人分组。在由N × p矩阵Pp是98个可用的病理特征,N是我们样本中的895个实验对象。为了对顺序半定量病理评分进行聚类,构造了一个矩阵。R其元素Rij等于多弦相关列间ijP,对应于病人的病理评分向量。为了证明多变量生物标志物模型在临床上的实际应用,使用了体内可用的数据来预测患者是否符合特定的神经退行性疾病的标准,或者被归类为特定的数据算法的疾病集群。

结果:无监督学习可应用于基于与病理生理模式重叠的疾病相关的任何一组特征的相似性矩阵,例如在癫痫、血管疾病或癌症等多因素疾病中。对这一进程至关重要的是汇编大型多模式和多地点数据集,这些数据集涵盖广泛的诊断、表型和基因型。除了基于生物标志物的组织病理学综合征预测的潜在临床应用外,本工作还可作为使用无监督方法在任何领域识别数据驱动的跨诊断性疾病亚型的模式。

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    2021-01-17 liye789132251
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    2020-08-04 124c32bfm02暂无昵称

    学习一下

    0

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    2020-08-04 医鸣惊人

    学习了

    0

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