Real-time, large-area microbial mapping to prevent the spread of healthcare-associated infections

摘要摘要 医疗保健相关感染(HAI)是对患者安全的威胁。阻止人类免疫缺陷病毒的传播需要预防， 监测和疫情调查。许多医院正在采用增强的清洁方案，如紫外线消毒， 他们的标准清洁方案，这些方案如果得到适当的实施，已被证明可以减少禽流感的传播。 实施增强型清洁方案的医疗保健提供者面临的一个重大挑战是验证其有效性 这些干预措施。使用常规微生物计数方法进行采样，如培养和菌落计数。 可以使用计数方法、聚合酶链式反应方法和免疫分析方法，但这些方法 劳动强度大，采样区域有限，不能提供实时结果。光谱和光谱成像 由于最少的样品制备和快速的数据采集，这些技术变得流行和有吸引力。荧光 光谱学使用紫外线光源激发微生物分子中的电子，并测量可见光 发出的光。不同的微生物会根据它们的组成分子产生不同的荧光信号， 例如蛋白质、维生素和辅酶。基于传统光谱仪的荧光光谱分析工具有 目前用于对制药和食品生产应用中的生物污染物进行量化。然而，这些系统基于 在传统的光谱仪上，采样区域有限。将荧光光谱学应用于大范围需要 高光谱成像仪，测量图像中每个像素的空间和光谱信息。在实验室环境中， 这种高光谱成像仪通常是扫描图像光谱仪，体积大，价格昂贵，需要样品和 传感器是静止的，可能需要几分钟才能捕捉到一张图像。NanoHmics已经开发出一种固态芯片规模 提供实时全帧数据采集和空间配准光谱数据的高光谱成像仪。 NanoHmics建议开发一种手持、大面积、实时荧光成像检测器(HEART-FID)来 在医疗保健环境中启用生物边界的映射。心脏-FID系统的关键组件是一个定制的 利用光谱的嵌入式图像采集和处理控制激发源的荧光成像仪 指纹和机器学习来区分细菌、真菌和其他有机材料。在第一阶段 计划中，该团队将演示该系统可以测量不同浓度的目标微生物 并将这些结果与已建立的微生物监测方法进行比较。 第二阶段计划的目标将是设计、优化和演示心脏-FID系统 可以纳入既定的消毒程序。原型将在以下过程中升级到TRL 5-6 第二阶段的程序能够在不到10秒的时间内成像100厘米x 100厘米的图像，并区分细菌， 真菌和无害的有机物质。该系统将为医疗保健提供者提供新的数据，使他们能够 轻松评估他们的清洁程序，并快速跟踪和预防潜在的疫情。

Summary Abstract Healthcare-associated infections (HAIs) are a threat to patient safety. Stopping the spread of HAIs requires prevention, surveillance, and outbreak investigations. Many hospitals are employing enhanced cleaning protocols, such UV disinfection, to their standard cleaning protocols, which when properly implemented have been shown to decrease the spread of HAIs. A significant challenged faced by healthcare providers implementing enhanced cleaning protocols is verifying their efficacy of these interventions. Sampling using conventional microorganism counting methods, such as culturing and colony- counting methods, polymerase chain reaction methods, and immunoassay approaches, can be used, but these methods are labor-intensive, sample only limited areas, and do not provide real-time results. Spectroscopic and spectral imaging techniques have become popular and attractive due to minimal sample preparation and rapid data acquisition. Fluorescence spectroscopy uses an ultraviolet light source to excite electrons in molecules in microorganisms and measures the visible light emitted. Different microorganisms will produce different fluorescence signatures based on their constituent molecules, such as proteins, vitamins, and coenzymes. Fluorescence spectroscopy tools based on conventional spectrometers are currently used to quantify the bioburden in pharmaceutical and food production applications. However, these systems based on conventional spectrometers have a limited sampling area. Applying fluorescence spectroscopy to large areas requires a hyperspectral imager that measures both spatial and spectral information for each pixel in the image. In laboratory settings, this hyperspectral imager is typically a scanning image spectrometer, which is bulky, expensive, requires the sample and sensor to be still, and can take minutes to capture a single image. Nanohmics has developed a solid-state chip-scale hyperspectral imager that provides real-time full-frame data collection and spatially registered spectral data. Nanohmics proposes to develop a handheld, extensive-area, real-time fluorescence imaging detector (HEART-FID) to enable mapping of the bioburden in healthcare settings. The key components of the HEART-FID system are a custom fluorescence imager with excitation sources controlled by an embedded image acquisition and processing that uses spectral fingerprints and machine learning to differentiate between bacteria, fungi, and other organic materials. In the Phase I program, the team will demonstrate that the system can measure different concentrations of target microorganisms on relevant surfaces typically found in hospitals and compare these results to established methods of microbial monitoring. The goal of the Phase II program will be the design, optimization, and performance demonstration of a HEART-FID system that can incorporated into established disinfection routines. The prototype will be advanced to TRL 5-6 over the course of the Phase II program with the ability image a 100cm x 100cm in less than 10 seconds and distinguish between bacteria, fungi, and nonharmful organic materials. The system will provide healthcare providers with new data that will allow them to easily evaluate their cleaning procedures and quickly track and prevent potential outbreaks.