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

对呼吸进行采样可以实时揭示关于个体中发生的身体的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各种气道疾病的潜力。尽管在过去十年中进行了越来越大规模的研究，但基于呼出挥发性有机化合物（VOC）测试的临床相关呼吸测试尚未得到验证。其中一个原因是在呼吸采集期间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 et al. 2014，2017）和一天中的时间（威尔金森et al. 2019）的研究强调了标准化采样方案的必要性。常规用于哮喘诊断和监测的呼出气一氧化氮（FeNO）分数水平受这些生理参数的影响很大，因此呼吸VOC的水平可能受到类似的影响。这种复杂的生物和生理因素的组合使得难以解释测量结果或开发解释这些因素的分析技术。因此，该项目将在西北肺中心（曼彻斯特大学医院）对人类志愿者和气道疾病（如哮喘）患者进行对照实验，并对肺部进行计算建模，以创建新的采样协议和分析工具。这将建立在类似的方法，以前已经使用到占流速和呼吸中的FeNO测量（Conderelli等人2007）或多次呼吸冲洗测量（Whitfield等人2022）的扩散。学生将与我们的行业合作伙伴Imspex诊断有限公司合作，使用离子迁移谱和质谱在线测量呼出气。然后，学生将在小组内建立采样专业知识，以评估肺部生理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洗的逼真渲染，以确定健康和疾病中控制呼出VOC浓度的因素。

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