Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Computer Methods and Programs in Biomedicine: Computational methods used to investigate atherosclerosis progression in coronary arteries: structural FEA, CFD or FSI
Vittorio Lissoni et al
Abstract
In recent years, computational simulations have emerged as valuable tools for the evaluation of atherosclerosis progression in coronary anatomies, although only a few studies have utilized more realistic Fluid-Structure Interaction (FSI) simulations. This work aims to compare the results of Computational Fluid Dynamics (CFD), Structural Finite Element Analysis (structural FEA) and FSI simulations in order to assess differences in plaque progression indices estimation.
We performed structural FEA, CFD and FSI on five patient-specific epicardial coronary anatomies using the commercial software LS-Dyna. To account for the vessel pre-stress, the zero-pressure configuration was calculated for each anatomy with an inverse elastostatic algorithm. CFD, structural FEA and FSI simulations were performed applying boundary conditions based on physiological values.
The comparison between structural FEA and FSI showed similar stress distribution and vessel expansions, with differences found only in the distal parts of the coronaries, where pressure reduction due to pressure loss affects the vessel walls. The elastic walls of the coronaries impact blood flow, resulting in a more disturbed flow. However, time averaged wall shear stress (TAWSS) and oscillatory shear index (OSI) distributions are similar across each coronary between CFD and FSI; TAWSS is slightly higher in CFD while OSI peaks are higher in FSI.
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Journal of Biomechanical Engineering: In Silico Modelling of Pulmonary Surfactant Dynamics from Alveolus to Whole Lung
Ruobing Li et al
Abstract
Surfactant plays a crucial role in maintaining lung mechanics by reducing alveolar surface tension and preventing alveolar collapse. Deficiencies or alterations in surfactant properties can lead to significant changes in lung mechanics and impairments in lung function. However, understanding how changes in surfactant concentration and properties impact on lung function remains challenging. In this study, we integrated a previously published model of alveolar surfactant dynamics [Otis et al., 1994] into a computational model that links acinar mechanics with ventilation of the full conducting airway tree. This approach allowed us to explore the regional and global effects of surfactant on lung function under different ventilation conditions. Simulations mimicking saline filled, lavaged, and air-filled lungs demonstrated the well-known effect of surfactant on reducing surface tension at the air-liquid interface and establishing the hysteresis observed during inhalation and exhalation. Increased hysteresis was observed during ventilation with higher tidal volumes, while increasing breathing frequency led to increased heterogeneity in surfactant distribution and acinar compliance. These findings demonstrate that reductions in surfactant concentration impair alveolar expansion and ventilation efficiency, influencing lung function under varying mechanical ventilation strategies. By integrating surfactant dynamics with acinar mechanics, this computational model has the potential to predict how surfactant depletion, as seen in neonatal respiratory distress syndrome and acute lung injury, leads to alveolar instability and ventilation heterogeneity. The framework provides a tool to assess surfactant-related lung dysfunction and optimize mechanical ventilation strategies to improve alveolar recruitment and gas exchange in patients with surfactant deficiencies.
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Review Diabetes Technology Theory: Metabolic Models, in Silico Trials, and Algorithms
Ali Cinar et al
Abstract
Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.
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Journal of The Royal Society Interface: Personalizing computational models to construct medical digital twins
Adam Knapp et al
Abstract
Digital twin technology, originally developed for engineering, is being adapted to biomedicine and healthcare. A key challenge in this process is dynamically calibrating computational models to individual patients using data collected over time. This calibration is vital for improving model-based predictions and enabling personalized medicine. Biomedical models are often complex, incorporating multiple scales of biology and both stochastic and spatially heterogeneous elements. Agent-based models, which simulate autonomous agents, such as cells, are commonly used to capture how local interactions affect system-level behaviour. However, no standard personalization methods exist for these models. The main challenge is bridging the gap between clinically measurable macrostates (e.g. blood pressure and heart rate) and the detailed microstate data (e.g. cellular processes) needed to run the model. In this article, we propose an algorithm that applies the ensemble Kalman filter, a classic data-assimilation technique, at the macrostate level. We then link the Kalman update at the macrostate to corresponding updates at the microstate level, ensuring that the resulting microstates are compatible with the desired macrostates and consistent with the model's dynamics. This approach improves the personalization of complex biomedical models and enhances model-based forecasts for individual patients.
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Computers in Biology and Medicine: A comparative analysis of metamodels for 0D cardiovascular models, and pipeline for sensitivity analysis, parameter estimation, and uncertainty quantification
John M Hanna et al
Abstract
Zero-dimensional (0D) cardiovascular models are reduced-order models aimed at studying the global dynamics of the whole circulation system or transport within it. They are employed to obtain estimates of important biomarkers for surgery planning and assessment applications (such as pressures, volumes, flow rates, and concentrations in the circulation) and can provide boundary conditions for high-fidelity three-dimensional models. Despite their low computational cost, tasks such as parameter estimation or uncertainty quantification require a large number of model evaluations, which is still a computationally expensive task. This motivates the building of metamodels in an offline stage, which can be evaluated significantly faster than 0D models. In this work, a pipeline going from 0D cardiovascular models to the building of metamodels and showcasing their use for tasks such as sensitivity analysis, parameter estimation, or uncertainty quantification is proposed. Three different strategies are assessed to build metamodels for 0D cardiovascular models, namely Neural Networks, Polynomial Chaos Expansion, and Gaussian Processes. The metamodels are assessed for three different 0D models. The first is a lumped model aimed at predicting the pressure in the portal vein after surgery. Due to the strong interaction between local liver hemodynamics and global circulation, the full circulation is modeled. The second one is simulating the whole-body circulation under the conditions of pulmonary arterial hypertension before and after shunt insertion. The final model is aimed at assessing the blood perfusion of an organ after a revascularization surgery. The transport of a contrast agent is modeled on top of a simplified 0D hemodynamics model. This model is chosen due to the different nature of the output which is a signal (concentration of the contrast agent over time), which requires a different treatment from the metamodeling point of view. The metamodels are trained and tested on synthetic data generated from the 0D models. It was found that neural networks offer the most convenient way of building metamodels in terms of the quality of the results, computational time, and practical ease of performing parameter estimation, sensitivity analysis, or uncertainty quantification tasks. Finally, we demonstrate a full pipeline of sensitivity analysis, inverse problem and (patient-specific) UQ, with a neural network as emulator.
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NPJ Aging: Walking into aging: real-world mobility patterns and digital benchmarks from the InCHIANTI Study
Jose Albites-Sanabria et al
Abstract
Mobility is a cornerstone of health and quality of life, particularly in older adults. Digital mobility outcomes (DMOs) from real-world walking data offer crucial insights into the functional status and early markers of mobility decline. This study provides reference values for walking activity, pace, rhythm, and gait bout-to-bout variability in community-dwelling older adults and evaluates the effects of age, sex, height, and weight on these parameters. Using data from 200 older adults (aged 65-94 years) from the InCHIANTI Study and applying the Mobilise-D computational pipeline, we analyzed real-world walking over a week. Significant differences by sex and age were found, with males showing higher walking activity in younger age groups (65-74 and 75-84 years) but not in the oldest group (85-94 years). Additionally, we observed non-linear trends in mobility metrics with age, indicating an accelerated reduction in mobility at certain age ranges. These findings underscore the importance of monitoring real-world walking data to pinpoint critical periods of mobility decline and guide targeted interventions. This work offers valuable benchmarks for clinical assessments and future research.
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