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(MRC DTP) - Detecting and modelling the progression of early cystic fibrosis lung disease from multiple breath washin and washout

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  • Full or part time
    Prof O Jensen
    Dr A Horsley
    Prof P Robbins
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

This project will use mathematical modelling to help improve a clinical lung-function test for cystic fibrosis (CF), an inherited condition that results in progressive lung damage from birth and an early death. The project relates to a major NIHR-funded study (LCI-SEARCH), involving a sensitive method of tracking physiological changes in the lungs of more than 100 adults and children with CF, using multiple-breath wash-in of an inert tracer gas (SF6) followed by wash-out during tidal breathing. The methodology incorporates a novel closed-circuit (rebreathing) wash-in method, using a portable device with a quick wash-in time. Although conventionally discarded during analysis, the wash-in part of the test is affected by the same disease processes assessed during wash-out. The comprehensive longitudinal dataset generated in LCI-SEARCH, the novel method of wash-in and the mechanistic mathematical models to be developed in this project together offer a unique opportunity to develop new indices of gas mixing efficiency, offering improved interpretation and
prediction of airway function.

The information provided by wash-in may offer greater sensitivity than wash-out, and will offer considerable advantages in terms of improved test time. In addition, the system measures other expired gas species (such as CO2) that will allow us to explore the differential effects of disease and age-related changes on measures such as the alveolar slopes of expired gases. Measures related to alveolar slope progression currently provide greater sensitivity than summary measures of wash-out efficiency, such as lung clearance index.
However these indices are complex to generate and not reproducible enough for clinical interpretation. We plan to exploit observed differences between CO2 and SF6 slopes to generate wash-out indices of greater sensitivity and to use these data to inform mathematical models of lung ventilation that will allow us to infer the processes underlying the gas-mixing abnormalities.

Building on an existing methodology, mathematical models will be based on principles of fluid mechanics and gas transport in complex geometrical domains, aiming to provide a tractable representation of measured data. The objectives are to generate measures of lung physiology that offer both enhanced sensitivity and reduced test time compared to those currently available, which could offer substantial advantage to the clinical scalability of the technology and methodology currently in use. The student will gain clinical exposure at the
Manchester Adult CF Centre and will benefit from additional external supervision from Prof Peter Robbins, Head of Dept. Physiology, University of Oxford.

Funding Notes

This project is to be funded under the MRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on our website

Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.


1) Horsley AR, O'Neill K, Downey DG, Elborn JS, Bell NJ, Smith J, Owers-Bradley J. Closed circuit rebreathing to achieve inert gas wash-in for multiple breath wash-out. ERJ Open Res 2016; 2: 00042-2015

2) Hiorns JE, Jensen OE & Brook BS (2016). Static and dynamic stress heterogeneity in a multiscale model of the asthmatic airway wall. J. Appl. Physiol. 121:233-247 DOI: 10.1152/japplphysiol.00715.2015

3) Jensen OE & Stewart PS (2015). Patterns of recruitment and injury in a heterogeneous airway network model. J. Roy. Soc. Interface, 12:20150523. DOI:10.1098/rsif.2015.0523.

4) Rowan SA, Bradley JM, Bradbury I, Lawson J, Lynch T, Gustafsson P, Horsley A, O'Neill K, Ennis M, Elborn JS. Lung clearance index is a repeatable and sensitive indicator of radiological changes in bronchiectasis. Am J Respir Crit Care Med. 2014 Mar 1;189(5):586-92. doi: 10.1164/rccm.201310-1747O

5) Ciaffoni L, O’Neill DP, Couper JH, Ritchie GAD, Hancock G, Robbins PA. In-airway molecular flow sensing: A new technology for continuous, noninvasive monitoring of oxygen consumption in critical care. Sci. Adv. 2016; 2 : e1600560

How good is research at University of Manchester in Mathematical Sciences?

FTE Category A staff submitted: 54.40

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