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

• 批准号: 2749522
• 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

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.

癌症仍然是全球最普遍的疾病之一。由于大多数癌症在晚期被诊断出，癌症与非常高的死亡率（10年生存率）有关。尽管所采用的测定仍然是实验性的，而且敏感性和特异性可能会遭受较差的症状，但在早期检测中取得了最新进展。另一方面，IR中IR光谱已证明在检测血液和其他体液中的癌症特异性分析物方面具有鲁棒性。但是，在低分析物浓度下，基于FTIR的常规测量值（包括使用ATR晶体的使用）对此类诊断不足。在这个项目中，我们将使用信号增强的ATR芯片和波导芯片来利用高度敏感的evanevanscent波中IR光谱，并将使用机器学习方法进行快速的早期癌症诊断以及治疗监测。在特定的情况下，该项目将涉及针对癌症（乳腺和卵巢）生物标志物的高度敏感的一次性ATR和波导芯片的设计和制造。制造的芯片将用于从剑桥大学英国癌症研究者那里获得的血浆样品中的中IR光谱，并将使用机器学习和深度学习算法进行分析结果。