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Early cancer diagnostics using evanescent wave Mid-IR spectroscopy

使用倏逝波中红外光谱进行早期癌症诊断

基本信息

批准号：
2749522
负责人：
金额：
--
依托单位：
University of Southampton
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2749522
关键词：
Early cancer diagnostics using evanescent

项目摘要

Cancer continues to be one of the most prevalent diseases worldwide. Cancer is associated with a very high mortality rate (50% survival at 10 years) as most cancers are diagnosed at a late stage. Recent advances have been made in early detection, though the assays employed are still experimental, highly expensive and can suffer from poor sensitivity and specificity. On the other hand, Mid-IR spectroscopy have shown to be robust in detecting cancer-specific analytes within the blood and other bodily fluids. However, conventional FTIR based measurements (including the use of ATR crystals) are insufficiently sensitive for such diagnostics at low analyte concentrations. In this project, we will exploit highly sensitive evanescent wave Mid-IR spectroscopy using signal enhanced ATR chips and waveguide chips and will use machine learning approach for rapid early cancer diagnostics as well as therapy monitoring. In specific this project will involve the design and fabrication of highly sensitive disposable ATR and waveguide chips optimised for cancer (breast and ovarian) biomarker sensing. The fabricated chips will be utilised for Mid-IR spectroscopy of blood plasma samples obtained from our collaborators from Cancer Research UK at Cambridge University and the results will be analysed using machine learning and deep learning algorithms specifically developed for this purpose.

癌症仍然是世界上最普遍的疾病之一。癌症的死亡率非常高（10年存活率为50%），因为大多数癌症在晚期才被诊断出来。最近在早期检测方面取得了进展，尽管所采用的检测方法仍处于实验阶段，价格昂贵，灵敏度和特异性较差。另一方面，中红外光谱已被证明在检测血液和其他体液中的癌症特异性分析物方面是可靠的。然而，传统的基于FTIR的测量（包括使用ATR晶体）在低分析物浓度下对这种诊断不够敏感。在这个项目中，我们将利用信号增强ATR芯片和波导芯片开发高灵敏度的倏逝波中红外光谱，并将使用机器学习方法进行快速早期癌症诊断和治疗监测。具体来说，该项目将涉及设计和制造高灵敏度的一次性ATR和波导芯片，优化用于癌症（乳腺癌和卵巢癌）生物标志物传感。制造的芯片将用于从剑桥大学英国癌症研究中心获得的血浆样本的中红外光谱分析，结果将使用专门为此目的开发的机器学习和深度学习算法进行分析。