A theoretical model of inflammation- and mechanotransduction- driven asthmatic airway remodelling

Inflammation, airway hyper-responsiveness and airway remodelling are well-established hallmarks of asthma, but their inter-relationships remain elusive. In order to obtain a better understanding of their inter-dependence, we develop a mechanochemical morphoelastic model of the airway wall accounting for local volume changes in airway smooth muscle (ASM) and extracellular matrix in response to transient inflammatory or contractile agonist challenges. We use constrained mixture theory, together with a multiplicative decomposition of growth from the elastic deformation, to model the airway wall as a nonlinear fibre-reinforced elastic cylinder. Local contractile agonist drives ASM cell contraction, generating mechanical stresses in the tissue that drive further release of mitogenic mediators and contractile agonists via underlying mechanotransductive signalling pathways. Our model predictions are consistent with previously described inflammation-induced remodelling within an axisymmetric airway geometry. Additionally, our simulations reveal novel mechanotransductive feedback by which hyper-responsive airways exhibit increased remodelling, for example, via stress-induced release of pro-mitogenic and procontractile cytokines. Simulation results also reveal emergence of a persistent contractile tone observed in asthmatics, via either a pathological mechanotransductive feedback loop, a failure to clear agonists from the tissue, or a combination of both. Furthermore, we identify various parameter combinations that may contribute to the existence of different asthma phenotypes, and we illustrate a combination of factors which may predispose severe asthmatics to fatal bronchospasms

Functional lung imaging with synchrotron radiation : Methods and preclinical applications

Many lung disease processes are characterized by structural and functional heterogeneity that is not directly appreciable with traditional physiological measurements. Experimental methods and lung function modeling to study regional lung function are crucial for better understanding of disease mechanisms and for targeting treatment. Synchrotron radiation offers useful properties to this end: coherence, utilized in phase-contrast imaging, and high flux and a wide energy spectrum which allow the selection of very narrow energy bands of radiation, thus allowing imaging at very specific energies. K-edge subtraction imaging (KES) has thus been developed at synchrotrons for both human and small animal imaging. The unique properties of synchrotron radiation extend X-ray computed tomography (CT) capabilities to quantitatively assess lung morphology, and also to map regional lung ventilation, perfusion, inflammation and biomechanical properties, with microscopic spatial resolution. Four-dimensional imaging, allows the investigation of the dynamics of regional lung functional parameters simultaneously with structural deformation of the lung as a function of time. This review summarizes synchrotron radiation imaging methods and overviews examples of its application in the study of disease mechanisms in preclinical animal models, as well as the potential for clinical translation both through the knowledge gained using these techniques and transfer of imaging technology to laboratory X-ray sources.Peer reviewe

Elastocapillary network model of inhalation

The seemingly simple process of inhalation relies on a complex interplay between muscular contraction in the thorax, elasto-capillary interactions in individual lung branches, propagation of air between different connected branches, and overall air flow into the lungs. These processes occur over considerably different length and time scales; consequently, linking them to the biomechanical properties of the lungs, and quantifying how they together control the spatiotemporal features of inhalation, remains a challenge. We address this challenge by developing a computational model of the lungs as a hierarchical, branched network of connected liquid-lined flexible cylinders coupled to a viscoelastic thoracic cavity. Each branch opens at a rate and a pressure that is determined by input biomechanical parameters, enabling us to test the influence of changes in the mechanical properties of lung tissues and secretions on inhalation dynamics. By summing the dynamics of all the branches, we quantify the evolution of overall lung pressure and volume during inhalation, reproducing the shape of measured breathing curves. Using this model, we demonstrate how changes in lung muscle contraction, mucus viscosity and surface tension, and airway wall stiffness---characteristic of many respiratory diseases, including those arising from COVID-19, cystic fibrosis, chronic obstructive pulmonary disease, asthma, and emphysema---drastically alter inhaled lung capacity and breathing duration. Our work therefore helps to identify the key factors that control breathing dynamics, and provides a way to quantify how disease-induced changes in these factors lead to respiratory distress.Comment: In pres

Modeling Recruitment/Derecruitment

Recruitment and derecruitment (R/D) of airways is known to significantly influence mechanical properties of the respiratory system during artificial ventilation, particularly in states of lung injury. The prevailing view of this phenomenon treats airway R/D as a static function of pressure. Recent experimental and clinical data suggests that this is not the case, but rather that R/D is an inherently dynamic process. In order to quantitatively assess the dynamics of lung recruitment during mechanical ventilation we extended a mathematical model by Bates and Irvin (9) for the purpose of fitting experimental data. The model of the lung consists of a parallel network of flow pathways with identical resistive and elastic elements. Each pathway is allowed to be either open, whereby it accumulates flow and decreases overall lung stiffness, or closed, increasing lung elastance and not participating in ventilation. The pathways are characterized by unique critical closing and opening pressures, and opening and closing velocities, each chosen from probability distribution functions. The rate of transition between an open and closed state depends on the magnitude difference between the pressure in the respiratory system and each unit’s critical pressure times the airway’s opening or closing velocity constant. Since the exact form of the pressure dependence governing recruitment and derecruitment remains unknown we explored four model variants to predict how opening or closing behavior is altered in injury. The lung model was coupled with a computational model of a mechanical ventilator in order to simulate elastance changes following deep inflation (DI) at three levels of Positive End Expiratory Pressure (PEEP). Elastance measurements came from healthy or lung injured mice at 4, 14, 24 or 48 hours following intratracheal instillation of saline (control) or hydrochloric acid (injury). The Nelder and Mead simplex optimization method was used to minimize error between model variants and average experimental elastance for each condition. By comparing the residual error of the fits for each model, we have demonstrated that only one variant was able to recreate both the transient response to deep inflations and the response to static PEEP. In fitting the best model to data from individual mice we obtained estimates for parameters governing opening and closing behavior. Statistics and model sensitivity were determined for each parameter in every experimental condition. Comparison of parameter values between groups revealed a significant increase in closing and opening pressures from health to injury, which worsened with increasing injury severity. The progressive increase in critical pressures as injury worsens implicates surfactant deactivation as the likely cause of increased propensity for airway closing during acute lung injury

The Virtual Physiological Human: Ten Years After

Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype–phenotype interaction and by a “systemic” nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible—the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done

Multiscale Modeling of Airway Inflammation Induced by Mechanical Ventilation

Mechanical ventilation (MV) is a system that partially or fully assists patients whose respiratory system fails to achieve a gas exchange function. However, MV can cause a ventilator-associated lung injury (VALI) or even contribute to a multiple organ dysfunction syndrome (MODS) in acute respiratory distress syndrome (ARDS) patients. Despite advances in today technologies, mortality rates for ARDS patient are still high. A better understanding of the interactions between airflow from mechanical ventilator and the airway could provide useful information used to develop a better strategy to ventilate patients. The mechanisms, which mechanical ventilation induces airway inflammation, are complex processes and cover a wide range of spatial scales. The multiscale model of the airway have been developed combining the computational models at organ, tissue, and cellular levels. A model at the organ level was used to study behaviors of the airway during mechanical ventilation. Strain distributions in each layer of the airway were investigated using a model at the tissue level. The cellular inflammatory responses during mechanical ventilation were investigated through the cellular automata (CA) model incorporating all biophysical processes during inflammatory responses. The multiscale modeling framework started by obtaining airway displacements from the organ-level model. They were then transferred to the tissue-level model for determining the strain distributions in each airway layer. The strain levels in each layer were then transferred to the cellular-level model for inflammatory responses due to strain levels. The ratio of the number of damage cells to healthy cells was obtained through the cellular-level model. This ratio, in turn, modulated changes in the Young’s modulus of elasticity at the tissue and organ levels. The simulation results showed that high tidal volume (1400 cc) during mechanical ventilation can cause tissue injury due to high concentration of activated immune cells and low tidal volume during mechanical ventilation (700 cc) can prevent tissue injury during mechanical ventilation and can mitigate tissue injury from the high tidal volume ventilation. The multiscale model developed in this research could provide useful information about how mechanical ventilation contributes to airway inflammation so that a better strategy to ventilate patients can be developed

Static and dynamic stress heterogeneity in a multiscale model of the asthmatic airway wall

Airway hyperresponsiveness (AHR) is a key characteristic of asthma that remains poorly understood. Tidal breathing and deep inspiration ordinarily cause rapid relaxation of airway smooth muscle (ASM) (as demonstrated via application of length fluctuations to tissue strips) and are therefore implicated in modulation of AHR, but in some cases (such as application of transmural pressure oscillations to isolated intact airways) this mechanism fails. Here we use a multiscale biomechanical model for intact airways that incorporates strain stiffening due to collagen recruitment and dynamic force generation by ASM cells to show that the geometry of the airway, together with interplay between dynamic active and passive forces, gives rise to large stress and compliance heterogeneities across the airway wall that are absent in tissue strips. We show further that these stress heterogeneities result in auxotonic loading conditions that are currently not replicated in tissue-strip experiments; stresses in the strip are similar to hoop stress only at the outer airway wall and are under- or overestimates of stresses at the lumen. Taken together these results suggest that a previously underappreciated factor, stress heterogeneities within the airway wall and consequent ASM cellular response to this micromechanical environment, could contribute to AHR and should be explored further both theoretically and experimentally

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 129, June 1974

This special bibliography lists 280 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1974

Novel strategies and multiscale modeling in respiratory mechanics

Despite tremendous technological and scientific advancements in the 20th century, clinically feasible assessment of detailed macroscopic respiratory mechanics is still limited. Additionally, a comprehensive understanding of macroscopic behavior in terms of microscopic description of alveolar mechanics and extracellular matrix properties is also absent. Combined together, these two limitations may explain why there has been a slow progress in optimizing mechanical ventilation for patients with lung disease. Addressing these two limitations, and, more importantly, linking macroscopic emergent phenomena to microscopic behavior could provide improved understanding and better health care. To this end, (1) a 3-D printed flow sensor was designed and evaluated to continuously measure airway opening flow and pressure in mice. (2) Using this sensor, we introduced a novel technique (ZVV) which provides continuous monitoring of the respiratory system’s physiological condition through evaluating cycle-by-cycle respiratory impedance (ZRS) during variable ventilation (VV). The feasibility and accuracy of ZVV was demonstrated by applying it in mice before and after inducing lung injury mimicking acute respiratory distress syndrome (ARDS), as well as in a computational study. Furthermore, when ZVV was applied to previously collected data, the analysis demonstrated for the first time that VV improved lung mechanics in human patients with ARDS. Additionally, two analytical models were developed to relate macroscopic to microscopic mechanical behavior of the lung parenchyma. (3) The first alveolar-unit model related alveolar septal wall properties (i.e., thickness) and constituents (i.e., fibers) to alveolar pressure-volume relationship providing insight into the importance of calculating true stress, the role of the collagen waviness and elastic modulus in alveolar stability and protection from over distension, as well as the multiscale relation between fiber stresses and macroscopic pressures. (4) The second intermediate tissue-level model described the mechanics of alveolar wall alignment under uniaxial stretching and estimated alveolar wall stiffness and stress demonstrating its ability to extract fiber-level properties from tissue strip stress-strain relations. When applied to pressure-volume and stress-strain data from lungs of old subjects, both models predicted alveolar wall and collagen fiber stiffening in aging. In summary, this study, presented a novel technique which can assess respiratory mechanics in clinical settings and multiscale models to enhance our understanding of how macroscopic behavior is related to alveolar constituent properties.2020-06-04T00:00:00

Multiscale modelling methods in biomechanics

More and more frequently, computational biomechanics deals with problems where the portion of physical reality to be modelled spans over such a large range of spatial and temporal dimensions, that it is impossible to represent it as a single space-time continuum. We are forced to consider multiple space-time continua, each representing the phenomenon of interest at a characteristic space-time scale. Multiscale models describe a complex process across multiple scales, and account for how quantities transform as we move from one scale to another. This review offers a set of definitions for this emerging field, and provides a brief summary of the most recent developments on multiscale modelling in biomechanics. Of all possible perspectives, we chose that of the modelling intent, which vastly affect the nature and the structure of each research activity. To the purpose we organised all papers reviewed in three categories: さcausal confirmationざ, where multiscale models are used as materialisations of the causation theories; さpredictive accuracyざ, where multiscale modelling is aimed to improve the predictive accuracy; and さdetermination of effectざ, where multiscale modelling is used to model how a change at one scale manifest in an effect at another, radically different space-time scale. Consistently with the how the volume of computational biomechanics research is distributed across application targets, we extensively reviewed papers targeting the musculoskeletal and the cardiovascular system, and covered only a few exemplary papers targeting other organ systems. The review shows a research sub-domain still in its infancy, where causal confirmation papers remain the most common