PFI-TT: Point-of-Care Sensor Based on Electric Fields and Machine Learning for the Detection of Circulating MicroRNA to Identify Early Stage Pancreatic Cancer

该创新-技术转化合作伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是开发用于胰腺癌早期检测的护理点生物传感器设备。目前，早期胰腺癌很难发现，因此5年生存率不到5%。如果早期发现胰腺癌，5年生存率可超过90%。所提出的生物传感器旨在有效和准确地检测最近建议用于指示早期胰腺癌的循环血液microRNA生物标志物的表达水平。所提出的生物传感器是一种使用电场和荧光检测microRNA生物标志物的一次性装置。如果成功，这些传感器可以快速检测胰腺癌生物标志物。该项目的商业影响可能很高，因为该项目通过实用的技术解决方案解决了医疗保健的关键需求。拟议的创业培训将包括对学生进行知识产权程序、产品商业化潜力分析和创业发展方面的培训。外联方案旨在向该区域代表性不足的群体介绍创新和创业的基本概念。 拟议的项目将研究microRNA和DNA杂交，并使用基于机器学习的回归方法来估计microRNA生物标志物分子的浓度。目前，使用基于高温的microRNA和DNA杂交，但它需要时间来执行，并且容易产生假阳性。为了解决这些问题，将开发一种新的方法，以潜在地产生具有更少假阳性的microRNA和DNA杂交测定。在护理点需要自动检测microRNA，因为没有技术专业知识来执行手动microRNA检测。这些拟议的机器学习研究将研究回归方法，以准确估计microRNA生物标志物的浓度。最后，拟议中的microRNA检测将自动化，以在护理点应用中发挥作用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to develop a point of care biosensor device for early detection of pancreatic cancer. Currently, it is difficult to detect early-stage pancreatic cancers, therefore 5-year survival rates are less than 5%. If pancreatic cancer is detected early, 5-year survival rate can be over 90%. The proposed biosensor seeks to efficiently and accurately detect the expression levels of circulating blood microRNA biomarkers recently suggested to indicate early pancreatic cancers. The proposed biosensor is a disposable device that uses electric fields and fluorescence for detection of microRNA biomarkers. If successful, these sensors may detect pancreatic cancer biomarkers rapidly. The commercial impact of the project is potentially high as this project addresses a critical need in healthcare with a practical technological solution. The proposed training on entrepreneurship will involve training students on intellectual property procedures, the analysis of the commercialization potential of a product, and start-up development. The outreach program is aimed at introducing the basic concepts of innovation and entrepreneurship to underrepresented groups in the region. The proposed project will study microRNA and DNA hybridization, and use machine learning based regression methods for the estimation of concentrations of microRNA biomarker molecules. Currently, high temperature-based microRNA and DNA hybridization is used but it takes time to perform and is prone to produce false positives. To address these issues, a novel approach will be developed to potentially produce microRNA and DNA hybridization assays with fewer false positives. Automated detection of microRNA is needed in point of care as there is no technical expertise to perform manual microRNA detection. These proposed studies in machine learning will investigate regression methods for accurately estimating the concentrations of microRNA biomarkers. Finally, proposed microRNA detection will be automated to be useful in point of care applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.