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      Computational modeling of the obstructive lung diseases asthma and COPD

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      1 , , 1 , 2
      Journal of Translational Medicine
      BioMed Central

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          Abstract

          Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow limitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling.

          There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods.

          Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the current state-of-the-art in techniques developed for pulmonary image analysis, development of structural models of the respiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models to investigate structure-function relationships in asthma and COPD.

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          Most cited references47

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          Chronic obstructive pulmonary disease

          Summary Chronic obstructive pulmonary disease (COPD) is characterised by progressive airflow obstruction that is only partly reversible, inflammation in the airways, and systemic effects or comorbities. The main cause is smoking tobacco, but other factors have been identified. Several pathobiological processes interact on a complex background of genetic determinants, lung growth, and environmental stimuli. The disease is further aggravated by exacerbations, particularly in patients with severe disease, up to 78% of which are due to bacterial infections, viral infections, or both. Comorbidities include ischaemic heart disease, diabetes, and lung cancer. Bronchodilators constitute the mainstay of treatment: β2 agonists and long-acting anticholinergic agents are frequently used (the former often with inhaled corticosteroids). Besides improving symptoms, these treatments are also thought to lead to some degree of disease modification. Future research should be directed towards the development of agents that notably affect the course of disease.
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            Self-organized patchiness in asthma as a prelude to catastrophic shifts.

            Asthma is a common disease affecting an increasing number of children throughout the world. In asthma, pulmonary airways narrow in response to contraction of surrounding smooth muscle. The precise nature of functional changes during an acute asthma attack is unclear. The tree structure of the pulmonary airways has been linked to complex behaviour in sudden airway narrowing and avalanche-like reopening. Here we present experimental evidence that bronchoconstriction leads to patchiness in lung ventilation, as well as a computational model that provides interpretation of the experimental data. Using positron emission tomography, we observe that bronchoconstricted asthmatics develop regions of poorly ventilated lung. Using the computational model we show that, even for uniform smooth muscle activation of a symmetric bronchial tree, the presence of minimal heterogeneity breaks the symmetry and leads to large clusters of poorly ventilated lung units. These clusters are generated by interaction of short- and long-range feedback mechanisms, which lead to catastrophic shifts similar to those linked to self-organized patchiness in nature. This work might have implications for the treatment of asthma, and might provide a model for studying diseases of other distributed organs.
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              Diagnosis and definition of severe refractory asthma: an international consensus statement from the Innovative Medicine Initiative (IMI).

              Patients with severe refractory asthma pose a major healthcare problem. Over the last decade it has become increasingly clear that, for the development of new targeted therapies, there is an urgent need for further characterisation and classification of these patients. The Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) consortium is a pan-European public-private collaboration funded by the European Commission Innovative Medicines Initiative of the European Union. U-BIOPRED aims to subphenotype patients with severe refractory asthma by using an innovative systems biology approach. This paper presents the U-BIOPRED international consensus on the definition and diagnosis of severe asthma, aligning the latest concepts in adults as well as in children. The consensus is based on existing recommendations up to 2010 and will be used for the selection of patients for the upcoming U-BIOPRED study. It includes the differentiation between 'problematic', 'difficult' and 'severe refractory' asthma, and provides a systematic algorithmic approach to the evaluation of patients presenting with chronic severe asthma symptoms for use in clinical research and specialised care.
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                Author and article information

                Contributors
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central
                1479-5876
                2014
                28 November 2014
                : 12
                : Suppl 2
                : S5
                Affiliations
                [1 ]Department of Computer Science, University of Oxford, Oxford, UK
                [2 ]Institute for Lung Health, Department of Infection, Immunity and Inflammation, University Hospitals of Leicester, Leicester, UK
                Article
                1479-5876-12-S2-S5
                10.1186/1479-5876-12-S2-S5
                4255909
                25471125
                a9927715-fbdd-4145-90d8-68c2e1993272
                Copyright © 2014 Burrowes et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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