Defining therapeutic protocols in asthma and monitoring patient response require a more in-depth knowledge on the disease severity and treatment outcome based on quantitative indicators. This paper aims at grading severity in asthma based on objective morphological measurements obtained in automated fashion from 3-D multi-slice computed tomography (MSCT) image datasets. These measures attempt to capture and quantify the airway remodeling process involved in asthma, both at the level of the airway wall thickness and airway lumen. Two morphological changes are thus targeted here, (1) the airway wall thickening measured as a global index characterizing the increase of wall thickness above a normal value of wall-to-lumen-radius ratio, and (2) the bronchoarterial ratio index assessed globally from numerous locations in the lungs. The combination of these indices provides a grading of the severity of the remodeling process in asthma which correlates with the known phenotype of the patients investigated. Preliminary application to assess the patient response in thermoplasty trials is also considered from the point of view of the defined indices.
We develop a lung ventilation model based on a continuum poroelastic representation of lung parenchyma that is strongly coupled to a pipe network representation of the airway tree. The continuous system of equations is discretized using a low-order stabilised finite element method. The framework is applied to a realistic lung anatomical model derived from computed tomography data and an artificially generated airway tree to model the conducting airway region. Numerical simulations produce physiologically realistic solutions and demonstrate the effect of airway constriction and reduced tissue elasticity on ventilation, tissue stress and alveolar pressure distribution. The key advantage of the model is the ability to provide insight into the mutual dependence between ventilation and deformation. This is essential when studying lung diseases, such as chronic obstructive pulmonary disease and pulmonary fibrosis. Thus the model can be used to form a better understanding of integrated lung mechanics in both the healthy and diseased states
Complex flow patterns exist within the asymmetric branching airway network in the lungs. These flow patterns are known to become increasingly heterogeneous during disease as a result of various mechanisms such as bronchoconstriction or alterations in lung tissue compliance. Here, we present a coupled model of tissue deformation and network airflow enabling predictions of dynamic flow properties, including temporal flow rate, pressure distribution, and the occurrence of reverse flows. We created two patient-specific airway geometries, one for a healthy subject and one for a severe asthmatic subject, derived using a combination of high-resolution CT data and a volume-filling branching algorithm. In addition, we created virtually constricted airway geometry by reducing the airway radii of the healthy subject model.
As part of the AirPROM project aimed at advancing new individually tailored therapies for asthma and COPD, the work presented here is focused on enabling investigation of the typical flow modifications seen in these diseases to improve understanding of the underlying mechanisms.
The burden of oxidative stress is increased in chronic obstructive pulmonary disease (COPD). However, whether the intra-cellular mechanisms controlling the oxidant/anti-oxidant balance in structural airway cells such as airway smooth muscle in COPD is altered is unclear. We sought to determine whether the expression of the NADPH oxidase (NOX)-4 is increased in airway smooth muscle in COPD both in vivo and primary cells in vitro and its role in hydrogen peroxide-induced reactive oxygen species generation. We found that in vivo NOX4 expression was up-regulated in the airway smooth muscle bundle in COPD (n = 9) and healthy controls with >20 pack year history (n = 4) compared to control subjects without a significant smoking history (n = 6). In vitro NOX4 expression was increased in airway smooth muscle cells from subjects with COPD (n = 5) compared to asthma (n = 7) and upregulated following TNF-α stimulation. Hydrogen peroxide-induced reactive oxygen species generation by airway smooth muscle cells in COPD (n = 5) was comparable to healthy controls (n = 9) but lower than asthma (n = 5); and was markedly attenuated by NOX4 inhibition. Our findings demonstrate that NOX4 expression is increased in vivo and in vitro in COPD and although we did not observe an intrinsic increase in oxidant-induced reactive oxygen species generation in COPD, it was reduced markedly by NOX4 inhibition supporting a potential therapeutic role for NOX4 in COPD.
We sought to determine, in terms of their sputum cellular and mediator profiles, the extent to which they represent distinct or overlapping conditions supporting either the “British” or “Dutch” hypotheses of airway disease pathogenesis.
Airflow limitation is seen in both COPD and asthma.
The multiple-breath inert gas washout parameter acinar ventilation heterogeneity (Sacin) is thought to be a marker of acinar airway involvement but has not been validated by using quantitative imaging techniques in asthmatic patients. We aimed to use hyperpolarized 3He diffusion magnetic resonance at multiple diffusion timescales and quantitative computed tomographic (CT) densitometry to determine the nature of acinar airway involvement in asthmatic patients.
The forced expiratory volume in one second (FEV1) is often used to monitor asthma control, and as an outcome measure in clinical trials. However, FEV1 is thought to be insensitive to small airway obstruction (SAO), and therefore a number of putative markers of SAO have been suggested.
Personalized medicine may be defined in general as a customization of medical approach to an individual patient, involving diagnosis, treatment and other medical procedures. The EU-founded AirPROM project is a prime example of joint cooperation that aims to develop a more personalized treatment in the area of respiratory medicine. In particular, project partners develop models and software tools that help to predict the progression of asthma and COPD (chronic obstructive pulmonary disease) as well as response to treatment for individual patients. However, such development would not be possible without computer science, its methods and technologies. The large amount of data produced for each patient, e.g. lung and airway models, together with complex simulations and effective data/results sharing are just selected challenges that need to be addressed. Therefore, a lot of effort has been spent to integrate the specific software tools used in the project with cloud-based infrastructure that allows scalable storage and computing. The computational automation achieved in the project translates into more time that may be spent on direct patient care. The story presented in this paper proves that personalized medicine is not only a matter for the future but a reality that is already happening
To determine the extent to which severe asthma and COPD represent distinct or overlapping conditions in terms of their sputum cellular and mediator profiles.
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