Metasurface enhanced and machine learning aided spectrochemical liquid biopsy

项目总结 液体活组织检查方法可以从生物液中非侵入性地检测与疾病相关的生物标记物，从而能够 癌症早期检测和患者监测对提高存活率的影响。但是，当前 这些方法尚未达到关键的灵敏度和准确性，无法被批准用于人口筛查计划。 新的光谱化学液体活检方法，如拉曼和红外光谱，与机器相结合 学习模式正在成为下一代诊断模式。然而，基本的物理限制 使用传统光学装置的光-物质相互作用阻碍了分子的分析性能 光谱学技术。在这里，我们建议使用新型的电磁亚表面，它可以 红外吸收光谱的分析灵敏度和化学选择性使其能够在现实世界中使用 在生物医学领域的应用。此外，我们创新的基于激光的光谱成像方法可以实现 片上光谱仪-无化学指纹检索，消除了临床不相容、复杂和笨重 仪器要求。 该项目的长期目标是开发一种快速、无标记、便携和非侵入性的癌症检测方法 基于灵敏准确的化学计量液体活检和机器学习辅助判别的平台 医疗模式。本申请的总体目标是(I)确定一个有效的变形表面设计，该设计可以 从复杂的生物样本矩阵中稳健地提取化学指纹信息，(Ii)确定优化设计 基于光谱成像的芯片指纹检索参数(III)建立测量协议和 数据处理流水线(四)确定机器学习模型，通过该模型敏感和准确的样本 歧视是可以实现的。在短期内，我们将追求两个具体目标：1)开发新型工程技术 用于敏感和特定光谱化学生物流体分析和演示无需光谱仪的超表面 芯片上的化学指纹2)使用卵巢癌患者的生物液测试和验证平台 队列和非癌症对照。我们提出的方法是创新的，因为它催化了最先进的 基于激光的红外光谱成像技术，加上强大的纳米光子工具，使其能够在 生物医学诊断和解决未得到满足的医疗需求。此外，拟议的跨学科项目 具有重要意义，因为预计它将开发一种非侵入性和可访问的健康筛查平台，可以 最终影响癌症的临床治疗和不同人群的生存结果 人口。

PROJECT SUMMARY Liquid biopsy modalities that can non-invasively detect disease-associated biomarkers from biofluids can enable early cancer detection and patient monitoring with implications for improved survival rates. However, current methods have not achieved critical sensitivity and accuracy to be approved for population screening programs. New spectrochemical liquid biopsy methods, such as Raman and infrared spectroscopy, coupled with machine learning models are emerging as next-generation diagnostic modalities. Yet, fundamental physical limitations of light-matter interactions using conventional optical setups hinder the analytical performance of molecular spectroscopy techniques. Here, we propose to employ novel electromagnetic metasurfaces that can advance the analytical sensitivity and chemical selectivity of infrared absorption spectroscopy enabling its real-world applications in the biomedical field. Moreover, our innovative laser-based spectral imaging approach can achieve on-chip spectrometer-less chemical fingerprint retrieval eliminating clinically incompatible, complex, and bulky instrumentation requirements. The long-term goal of this project is to develop a rapid, label-free, portable, and non-invasive cancer detection platform based on sensitive and accurate chemometric liquid biopsy and machine learning-aided discrimination modalities. The overall objectives in this application are to (i) determine a potent metasurface design that can robustly extract chemical fingerprint information from a complex biosample matrix, (ii) identify optimized design parameters for spectral imaging-based on-chip fingerprint retrieval (iii) establish measurement protocols and data processing pipeline (iv) identify a machine learning model by which sensitive and accurate sample discrimination can be achieved. In the short term, we will pursue two specific aims: 1) develop novel engineered metasurfaces for sensitive and specific spectrochemical biofluid analysis and demonstrate spectrometer-less on-chip chemical fingerprinting 2) Test and validate the platform using biofluids from an ovarian cancer patient cohort and non-cancer controls. Our proposed approach is innovative because it catalyzes the state-of-the-art laser-based infrared spectral imaging technology with powerful nanophotonic tools to enable its impact in biomedical diagnostics and address an unmet medical need. In addition, the proposed interdisciplinary project is significant because it is expected to develop a non-invasive and accessible health screening platform that can ultimately impact the clinical management of cancer and the survival outcomes equitably among diverse populations.