A mathematical model of infection, inflammation and immune response in an idealized bronchial tree is developed. This work is based on a model from the literature that is extended to account for the propagation dynamics of an infection between the airways. The inflammation affects the size of the airways, the air flows distribution in the airway tree, and, consequently, the oxygen transfers to blood. We test different infections outcomes and propagation speed. In the hypotheses of our model, the inflammation can reduce notably and sometimes drastically the oxygen flow to blood. Our model predicts how the air flows and oxygen exchanges reorganize in the tree during an infection. Our results highlight the links between the localization of the infection and the amplitude of the loss of oxygen flow to blood. We show that a compensation phenomena due to the reorganization of the flow exists, but that it remains marginal unless the power produced the ventilation muscles is increased. Our model forms a first step towards a better understanding of the dynamics of bronchial infections.
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http://dx.doi.org/10.1016/j.jtbi.2023.111405 | DOI Listing |
EBioMedicine
January 2025
Department of Respiratory and Clinical Care Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China. Electronic address:
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 PDFChest
December 2024
Children's Hospital of Orange County, University California, Irvine, Orange County, Calif.
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 PDFAirway 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 PDFCompr Physiol
December 2024
School of Human Sciences, The University of Western Australia, Crawley, Western Australia, Australia.
From the results of well-performed population health studies, we now have excellent data demonstrating that deficits in adult lung function may be present early in life, possibly as a result of developmental disorders, incurring a lifelong risk of obstructive airway diseases such as asthma and chronic obstructive pulmonary disease. Suboptimal fetal development results in intrauterine growth restriction and low birth weight at term (an outcome distinct from preterm complications), which are associated with subsequent obstructive disease. Numerous prenatal exposures and disorders compromise fetal development and these are summarized herein.
View Article and Find Full Text PDFBMC 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.
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