Non-invasive real-time modelling of biophysical and metabolic changes in airways disease

气道疾病生物物理和代谢变化的无创实时建模

• 批准号: 2899496
• 金额: --
• 依托单位: University of Manchester
• 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-2899496
• 关键词: Non; invasive; real; time; modelling

Sampling breath can uncover vast quantities of information about the underlying metabolic state of the body occurring in an individual in real-time. Tools to measure breath metabolites therefore have the potential for monitoring of a wide range of airways diseases. Despite increasingly large-scale studies being conducted in the last ten years, a clinically relevant breath test based on exhaled volatile organic compounds VOCs) test has yet to be validated. One reason for this is the number of confounding factors that must be accounted for during breath collection which makes reproducing published results difficult. Recent work on breath holding, oral versus nasal breathing (Sukul et al. 2014, 2017) and time of day (Wilkinson et al. 2019) has highlighted the need for standardised sampling protocols. Fractional exhaled nitric oxide (FeNO) levels, which are routinely used in asthma diagnosis and monitoring, are highly affected by these physiological parameters and it is therefore likely that the level of breath VOCs is similarly impacted. This combination of complex biological and physiological factors makes it difficult to interpret measurements or to develop analysis techniques that account for these factors. Therefore, this project will use a combination of controlled experiments with human volunteers and patients with airway disease (e.g. asthma) at the North West Lung Centre (Manchester University Hospitals) as well computational modelling of the lung to create new sampling protocols and analysis tools. This will build upon similar approaches that have been used previously to account for flow-rate and diffusion on FeNO measurement in the breath (Conderelli et al. 2007) or multiple breath washout measurements (Whitfield et al. 2022).The student will collaborate with our industry partner Imspex Diagnostic Ltd. to measure exhaled breath online using ion mobility spectrometry and mass spectrometry. The student will then build on sampling expertise within the group to assess the impact of changes in lung physiology on exhaled metabolites and investigate the short- and long-term variation observed in an individual's breath profile. These results will contribute to the optimisation of an existing lung function model that uniquely incorporates realistic rendering of lung physiology and gas washout in order to determine the factors governing exhaled VOC concentrations in health and disease.Although not essential, it would be advantageous for the prospective

呼吸采样可以实时揭示有关个体体内潜在代谢状态的大量信息。因此，测量呼吸代谢物的工具具有监测多种气道疾病的潜力。尽管在过去十年中进行了越来越大规模的研究，但基于呼出的挥发性有机化合物（VOC）测试的临床相关呼气测试尚未得到验证。造成这种情况的原因之一是在呼吸收集过程中必须考虑的混杂因素的数量，这使得再现已发表的结果变得困难。最近关于屏气、口呼吸与鼻呼吸（Sukul 等人，2014 年、2017 年）和一天中的时间（Wilkinson 等人，2019 年）的研究强调了标准化采样方案的必要性。通常用于哮喘诊断和监测的呼出一氧化氮 (FeNO) 浓度受到这些生理参数的高度影响，因此呼吸 VOC 水平也可能受到类似影响。这种复杂的生物和生理因素的结合使得解释测量结果或开发解释这些因素的分析技术变得困难。因此，该项目将结合西北肺部中心（曼彻斯特大学医院）的人类志愿者和气道疾病（例如哮喘）患者的对照实验以及肺部计算模型来创建新的采样方案和分析工具。这将建立在以前用于解释呼吸中 FeNO 测量的流速和扩散（Conderelli 等人，2007 年）或多次呼吸冲洗测量（Whitfield 等人，2022 年）的类似方法的基础上。学生将与我们的行业合作伙伴 Imspex Diagnostic Ltd. 使用离子淌度光谱法和质谱法在线测量呼出气。然后，学生将利用小组内的采样专业知识来评估肺部生理学变化对呼出代谢物的影响，并研究在个人呼吸曲线中观察到的短期和长期变化。这些结果将有助于优化现有的肺功能模型，该模型独特地结合了肺生理学和气体冲刷的真实渲染，以确定控制健康和疾病中呼出的 VOC 浓度的因素。虽然不是必需的，但这对于未来的研究是有利的