Predicting patient outcome using machine learning technique

使用机器学习技术预测患者结果

• 批准号: 549560-2020
• 负责人: Chan, WarrenWCW
• 金额: $ 11.76万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746155
• 关键词: Predicting; patient; outcome; using; machine

Chemical probes are key components of thebioimaging toolbox, as they label biomolecules in cells andtissues. The new challenge in bioimaging is to design chemicalprobes for three-dimensional (3D) tissue imaging. In thiswork, we discovered that light scattering of metal nanoparticlescan provide 3D imaging contrast in intact and transparenttissues. The nanoparticles can act as a template for thechemical growth of a metal layer to further enhance thescattering signal. The use of chemically grown nanoparticles inwhole tissues can amplify the scattering to produce a 1.4million-fold greater photon yield than obtained using common?uorophores. These probes are non-photobleaching and canbe used alongside ?uorophores without interference. Wedemonstrated three distinct biomedical applications: (a) molecular imaging of blood vessels, (b) tracking of nanodrug carriers intumors, and (c) mapping of lesions and immune cells in a multiple sclerosis mouse model. Our strategy establishes a distinct yetcomplementary set of imaging probes for understanding disease mechanisms in three dimensions.

化学探针是生物成像工具箱的关键组件，因为它们标记细胞和组织中的生物分子。生物成像的新挑战是设计用于三维(3D)组织成像的化学探针。在这项工作中，我们发现金属纳米颗粒的光散射可以在完整和透明的组织中提供3D成像对比度。纳米颗粒可以作为金属层化学生长的模板，进一步增强散射信号。在整个组织中使用化学生长的纳米粒子可以放大散射，产生的光子产额比使用普通生物质获得的光子产额高140万倍。这些探头是非光漂白的，可以在不受干扰的情况下与浮子一起使用。我们展示了三种不同的生物医学应用：(A)血管的分子成像，(B)纳米载体肿瘤的跟踪，以及(C)多发性硬化症小鼠模型中的病变和免疫细胞的映射。我们的策略建立了一套截然不同但互补的成像探针，用于从三维角度了解疾病机制。