Research Supplements to Promote Diversity

牙周致病菌的芯片复合检测 通过单细胞水平的拉曼成像 项目概要： 牙周炎是由牙齿周围组织中复杂的细菌相互作用引起的。 牙周病病原体在牙周袋中积累并引起牙周破坏， 合作的方式，导致牙龈炎症，软组织破坏，和骨 杀伤性这些病原体可以作为生物标志物来告知牙周状况。表面- 增强的拉曼光谱（SERS）已经作为一种强大的无标记检测工具出现， 由于细菌独特分子的拉曼特征， 混合物.为了促进SERS在临床应用中的实用性，必须开发一个平台， 这保证了简单和通用的样品制备方法，具有最小的技术要求。 在这项研究中，我们通过在所有纳米颗粒上预加载银纳米颗粒（Ag-NPs）来解决这一关键需求。 在一锅反应中将样品中的细菌细胞分离出来。反应迅速，无种属特异性 标签是必须的。根据父母格兰特的微生物区室化概念， 病原体将传播到通过平行连接的微室中， 微通道通过设计，每个微室容纳一个细胞。Ag-NP使 在单细胞水平上收集良好分辨的拉曼光谱（目标1）。机器学习 与快照高光谱成像方法相结合， 以空间分辨率识别和计数病原体种类以促进芯片上 多重检测（Aim 2）。这是一种通用的检测方法，可以集成在 便携式传感器设备中提出的父母补助金。 结合父母补助金的其他组成部分，这项研究有助于 唾液传感器原型的开发，将提供详细的医疗数据， 渐进式监测，它将是未来个性化医疗的有效工具， 有望显著改善患者的整体健康状况。除了技术培训外， 博士前学生将通过从事跨学科合作获得经验， 为他提供了对跨学科项目中工作流程复杂性的见解， 每个专业科目在支持不同领域的研究方面将发挥关键作用， 使他能够在未来的职业生涯中应对多方面的挑战。

On-chip multiplex detection of periodontal pathogens by Raman imaging at single cell level Project Summary: Periodontitis is initiated by complex bacterial interaction in tissues surrounding the teeth. Periodontal pathogens accumulate in periodontal pockets and induce periodontal destruction in a cooperative manner, leading to gingival inflammation, soft tissue destruction, and bone destruction. These pathogens can serve as biomarkers to inform periodontal status. Surface- enhanced Raman spectroscopy (SERS) has emerged as a powerful label-free detection tool to discriminate bacterial phenotypes due to the Raman signatures of their unique molecular composition. To facilitate SERS utility in clinical applications, it is imperative to develop a platform that warrants simple and universal sample preparation method with minimal technical demand. We address this critical need in this study by pre-loading Ag nanoparticles (Ag-NPs) on all bacterial cells in a sample in a one-pot reaction. The reaction is quick and no species-specific label is needed. Following the parent grant's microbial compartmentalization concept, the pathogens will be disseminated to microchambers that are interconnected by parallel microchannels. By design, each microchamber accommodates one cell. Ag-NP enables the collection of well-resolved Raman spectrum at a single-cell level (Aim 1). Machine learning in combination with the snap-shot hyperspectral imaging approach will enable confident identification and enumeration of pathogen species with spatial resolution to facilitate on-chip multiplex detection (Aim 2). This is a versatile detection method that can be integrated on a portable sensor device as proposed in the parent grant. Integrating with other components of the parent grant, this research contributes to the development of a prototype salivary sensor that will offer detailed medical data from frequent and progressive monitoring, and it will be an effective tool for future personalized medicine and is expected to dramatically improve the patient's overall health. In addition to technical training, the predoctoral student will gain experience through engaging in interdisciplinary collaborations, providing him with insights into the intricacies of workflow within an interdisciplinary project and the pivotal role that each specialty subject would play in supporting research in diverse fields, preparing him to become capable of tackling multifaceted challenges in his future career.