A poroelastic model coupled to a fluid network with applications in lung modelling.

Int J Numer Method Biomed Eng

Department of Computer Science, University of Oxford, Wolfson Building Parks, Road, OX1 3QD, Oxford, UK.

Published: January 2016

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. Copyright © 2015 John Wiley & Sons, Ltd.

Download full-text PDF

Source
http://dx.doi.org/10.1002/cnm.2731DOI Listing

Publication Analysis

Top Keywords

airway tree
8
lung
6
model
5
poroelastic model
4
model coupled
4
coupled fluid
4
fluid network
4
network applications
4
applications lung
4
lung modelling
4

Similar Publications

Background: Idiopathic pulmonary fibrosis (IPF) is a fibrosing interstitial pneumonia with restrictive ventilation. Recently, the structural and functional defects of small airways have received attention in the early pathogenesis of IPF. This study aimed to elucidate the characteristics of small airway epithelial dysfunction in patients with IPF and explore novel therapeutic interventions to impede IPF progression by targeting the dysfunctional small airways.

View Article and Find Full Text PDF

The small airways comprise generations 8 to 23 of the bronchial tree, consist of airways with an internal diameter <2mm, and are classically difficult to assess and treat in persistent asthma. Small airways dysfunction (SAD) is integral to the asthma management paradigm as it is associated with poorer symptom control, greater levels of type 2 inflammation, and has been proposed as a potential treatable asthma trait. Although identification of SAD by oscillometry has been found to be clinically useful in managing asthma, very few physicians, including specialists, use this technique as part of standard or adjunct evaluation of lung function to diagnose asthma, grade severity of airway obstruction, ascertain disease control or the risk for future exacerbations or to make management decisions.

View Article and Find Full Text PDF

Airway multiciliated cells (MCs) maintain respiratory health by clearing mucus and trapped particles through the beating of motile cilia. While it is known that ciliary lengths decrease along the proximal-distal (P-D) axis of the tracheobronchial tree, how this is regulated is unclear. Here, we demonstrate that canonical Notch signaling in MCs plays a critical role in stabilizing ciliary length.

View Article and Find Full Text PDF
Article Synopsis
  • Early lung function deficits can begin in childhood and are linked to developmental issues, leading to long-term risks for diseases like asthma and COPD.
  • Suboptimal fetal development, marked by low birth weight and intrauterine growth restriction, increases the likelihood of obstructive diseases later in life.
  • Prenatal exposures affecting growth can cause various structural and physiological abnormalities, highlighting the importance of early interventions, such as nutrition or antioxidant therapy, to support healthy lung development.
View Article and Find Full Text PDF

Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.

BMC Anesthesiol

December 2024

Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, 1th floor, No 21, Darband St., Tajrish sq., Tehran, Iran.

Background: To protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequently surpassing more established techniques. This study aims to utilize machine learning techniques on predictive parameters for challenging airway management.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!