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

总结抽象

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.