Theoretical open-loop model of respiratory mechanics in the extremely preterm infant

Non-invasive ventilation is increasingly used for respiratory support in preterm infants, and is associated with a lower risk of chronic lung disease. However, this mode is often not successful in the extremely preterm infant in part due to their markedly increased chest wall compliance that does not provide enough structure against which the forces of inhalation can generate sufficient pressure. To address the continued challenge of studying treatments in this fragile population, we developed a nonlinear lumped-parameter model of respiratory system mechanics of the extremely preterm infant that incorporates nonlinear lung and chest wall compliances and lung volume parameters tuned to this population. In particular we developed a novel empirical representation of progressive volume loss based on compensatory alveolar pressure increase resulting from collapsed alveoli. The model demonstrates increased rate of volume loss related to high chest wall compliance, and simulates laryngeal braking for elevation of end-expiratory lung volume and constant positive airway pressure (CPAP). The model predicts that low chest wall compliance (chest stiffening) in addition to laryngeal braking and CPAP enhance breathing and delay lung volume loss. These results motivate future data collection strategies and investigation into treatments for chest wall stiffening.Comment: 22 pages, 5 figure

Improving Student Knowledge Transfer between Mathematics and Engineering Courses through Structured Cross-disciplinary Collaboration: A SUMMIT-P Initiative

Student learning across STEM disciplines has been shown to increase with greater integration of applications in mathematics courses. One challenge of this effort is that identical constructs are often presented differently in the partner disciplines than in the mathematics courses. This leads to student confusion and an inability to transfer critical knowledge in their disciplinary courses, even for students who have mastered the mathematical paradigms. An interdisciplinary team at VCU consisting of mathematics and engineering faculty has worked to improve the knowledge transfer required for the integration of applications in the Differential Equations curriculum. This work is part of the multi-institutional SUMMIT-P initiative which aims to transform first- and second-year mathematics through collaboration with partner disciplines. The collaborative efforts have uncovered a variety of differently presented but identical constructs in categories ranging from notation up through higher-level interpretation. We provide some specific examples and analyses of these constructs and the implications for knowledge transfer and pedagogical concerns. Conversations around mathematics and disciplinary imperatives served to create a holistic view of the role mathematics and partner discipline professors have in improving learning outcomes

Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries

Computational fluid dynamics (CFD) models are emerging as tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation has made subject-specific modelling of the cardiovascular system a feasible task, which is particularly important in the case of pulmonary hypertension (PH), which requires a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation can easily propagate to CFD model predictions, making uncertainty quantification crucial for subject-specific models. This study quantifies the variability of one-dimensional (1D) CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of an image of an excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii, and network connectivity for each segmented pulmonary network. We quantify uncertainty in geometric features by constructing probability densities for vessel radius and length, and then sample from these distributions and propagate uncertainties of haemodynamic predictions using a 1D CFD model. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length

Lung, chest wall, and total respiratory system compliance curves for high <i>C</i>_<i>w</i> (left) and low <i>C</i>_<i>w</i> (right).

<p>Curves are described by Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198425#pone.0198425.e016" target="_blank">(9)</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198425#pone.0198425.e017" target="_blank">(10)</a> and parameterized using the procedures described in <b>Parameterization</b>. Tidal breathing loops with normal <i>R</i>_<i>u</i> (grey) and increased <i>R</i>_<i>u</i> (black) are superimposed for each condition over the lung compliance curve and larger in each inset to display hysteresis.</p

Initial conditions.

<p>Initial conditions.</p