I-Corps: Portable System for Pathogenic Bacteria Characterization

I-Corps项目更广泛的影响/商业潜力是开发一种便携式诊断系统来识别致病菌。目前，大多数诊断检测方法包括体外培养结合抗原或聚合酶链反应（PCR）为基础的检测分析。然而，这些方法往往是缓慢和无效的，特别是呼吸道细菌感染。该技术是基于光学散射数据，结合机器学习模型和优化。这是一种便携式、自动化的方法，旨在快速检测呼吸道病原体/细菌，减少分类时间。拟议的技术可能会对医疗保健行业和患者的结果产生广泛的影响，特别是儿童和老年人的呼吸道感染。此外，这项技术有可能加速致病菌临床诊断的进展。这个I-Corps项目的基础是开发一种用于病原菌检测和鉴定的便携式诊断系统。该技术将光学散射数据与机器学习模型和优化相结合，以实现细菌模式和特征的评估和表征。该系统是利用致病菌颗粒的光散射来设计的，目的是为致病菌的检测和表征提供一个快速、实时、便携式的系统。四种不同细菌的初步结果表明，获得了光学特征，可以区分不同的细菌。所提出的系统对于临床医生和研究人员快速检测特定细菌感染的模式可能有价值。该奖项反映了美国国家科学基金会的法定使命，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 I-Corps project is the development of a portable diagnostic system to identify pathogenic bacteria. Currently, most diagnostic detection methods include growing cultures in vitro coupled with antigen-based or polymerase chain reaction (PCR)-based detection assays. However, these methods are often slow and ineffective, especially for respiratory bacterial infections. The proposed technology is based on based on optical scatter data combined with machine learning models and optimization. It is a portable, automated approach that is designed to achieve rapid detection of respiratory pathogens/bacteria with reduced classification times. The proposed technology may have a broad impact on the health care industry and patient outcomes, particularly respiratory infections in children and the elderly. In addition, this technology has the potential to accelerate advances in clinical diagnosis of pathogenic bacteria.This I-Corps project is based on the development of a portable diagnostic system for pathogenic bacteria detection and identification. The proposed technology combines optical scatter data with machine learning models and optimization to enable the evaluation and characterization of bacteria patterns and signatures. The system is designed using light scattering of particles in the pathogenic bacteria with the goal of providing a rapid, real-time, portable system for detection and characterization of pathogenic bacteria. Preliminary results with four different bacteria showed that optical signatures were obtained and that different bacteria can be distinguished. The proposed system may be valuable to clinicians and researchers for rapidly detecting the patterns of specific bacterial infections.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.