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Novel multi-excitation Raman (ME-Raman) technologies for clinical identification of respiratory biofilms in ventilator associated pneumonia (VAP)

用于临床识别呼吸机相关性肺炎 (VAP) 呼吸生物膜的新型多激发拉曼 (ME-Raman) 技术

基本信息

批准号：
2893967
负责人：
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：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2893967
关键词：
Novel multi excitation Raman ME

项目摘要

In intensive care facilities ventilator associated pneumonia (VAP) is the most common lung disease, it significantly increase length of stay, patient morbidity and mortality. The rise in antimicrobial resistance means that current treatment with broad spectrum antibiotics is increasingly ineffective and a more targeted monitoring and treatment method is require. Current culture based methods for the detection of the bacteria and their associated biofilms are to slow to be highly effective. Recently, Raman spectroscopy has been shown to be able to identify and differentiate between different bacterial pathogens and biofilms. This project will further develop the Raman spectroscopy and machine learning approach that will allow a rapid and sensitive detection of bacteria and biofilms in VAP cases. A novel fibre based portable Raman device will also be developed for directly collecting data from endotracheal tubes, which will allow a rapid in situ analysis for the characterisation of the infection alongwith an analytical toolbox/GUI for such a potential device. This combined technology has the potential to be vital importance in the identification and treatment of VAP cases especially with antimicrobial resistant pathogens, increasing positive patient outcomes. This is an interdisciplinary project combining leading research in bacteria and biofilms and state-of-the-art Raman spectroscopy and machine learning tools.

呼吸机相关性肺炎（VAP）是重症监护病房最常见的肺部疾病，它显著增加了患者的住院时间、发病率和死亡率。抗生素耐药性的上升意味着目前使用广谱抗生素的治疗越来越无效，需要更有针对性的监测和治疗方法。目前用于检测细菌及其相关生物膜的基于培养物的方法是缓慢的，以高度有效。最近，拉曼光谱已被证明能够识别和区分不同的细菌病原体和生物膜。该项目将进一步开发拉曼光谱和机器学习方法，以快速灵敏地检测VAP病例中的细菌和生物膜。还将开发一种新型的基于光纤的便携式拉曼设备，用于直接从气管内导管收集数据，这将允许快速原位分析感染的特征以及用于这种潜在设备的分析工具箱/GUI。这种组合技术有可能在VAP病例的识别和治疗中发挥至关重要的作用，特别是在耐药性病原体的情况下，增加患者的积极结果。这是一个跨学科的项目，结合了细菌和生物膜的领先研究以及最先进的拉曼光谱和机器学习工具。