A versatile hybrid agent-based, particle and partial differential equations method to analyze vascular adaptation

Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system

The lymph node (LN) is a vital organ of the lymphatic and immune system that enables timely detection, response, and clearance of harmful substances from the body. Each LN comprises of distinct substructures, which host a plethora of immune cell types working in tandem to coordinate complex innate and adaptive immune responses. An improved understanding of LN biology could facilitate treatment in LN-associated pathologies and immunotherapeutic interventions, yet at present, animal models, which often have poor physiological relevance, are the most popular experimental platforms. Emerging biomaterial engineering offers powerful alternatives, with the potential to circumvent limitations of animal models, for in-depth characterization and engineering of the lymphatic and adaptive immune system. In addition, mathematical and computational approaches, particularly in the current age of big data research, are reliable tools to verify and complement biomaterial works. In this review, we first discuss the importance of lymph node in immunity protection followed by recent advances using biomaterials to create in vitro/vivo LN-mimicking models to recreate the lymphoid tissue microstructure and microenvironment, as well as to describe the related immuno-functionality for biological investigation. We also explore the great potential of mathematical and computational models to serve as in silico supports. Furthermore, we suggest how both in vitro/vivo and in silico approaches can be integrated to strengthen basic patho-biological research, translational drug screening and clinical personalized therapies. We hope that this review will promote synergistic collaborations to accelerate progress of LN-mimicking systems to enhance understanding of immuno-complexity.

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

HYBRID NANOFLUID FLOWS THROUGH A VERTICAL DISEASED CORONARY ARTERY WITH HEAT TRANSFER

Gold nanoparticles (AuNPs) are now widely used because of their synthesis compatibility and less toxicity in several biomedical applications such as cancer treatment. From the fluid mechanics point of view, we examine the behavior of a mixture of gold and Titanium Oxide nanoparticles, which suspended in the blood as a base fluid in the diseased coronary artery. The main goal of this paper is to examine and shed light on the hybrid nanofluid flows through a vertical diseased artery in the presence of the catheter tube with heat transfer. The mathematical model is established and then solved with the Laplace and the finite Hankel transforms. The inverse of the transformed functions has been calculated numerically. The velocity, the pressure, the impedance and the heat transfer are discussed graphically. It is noteworthy to mention that the mixture of the nanoparticles dispersed in the blood needs high pressure to push it. The impedance of blood is proportional to the overall volume concentration of the nanoparticles and Reynolds number.

[Review of studies on the biomechanical modelling of the coupling effect between stent degradation and blood vessel remodeling]

The dynamic coupling of stent degradation and vessel remodeling can influence not only the structural morphology and material property of stent and vessel, but also the development of in-stent restenosis. The research achievements of biomechanical modelling and analysis of stent degradation and vessel remodeling were reviewed; several noteworthy research perspectives were addressed, a stent-vessel coupling model was developed based on stent damage function and vessel growth function, and then concepts of matching ratio and risk factor were established so as to evaluate the treatment effect of stent intervention, which may lay the scientific foundation for the structure design, mechanical analysis and clinical application of biodegradable stent.

Computational modeling for cardiovascular tissue engineering: the importance of including cell behavior in growth and remodeling algorithms

Understanding cardiovascular growth and remodeling (G&R) is fundamental for designing robust cardiovascular tissue engineering strategies, which enable synthetic or biological scaffolds to transform into healthy living tissues after implantation. Computational modeling, particularly when integrated with experimental research, is key for advancing our understanding, predicting the in vivo evolution of engineered tissues, and efficiently optimizing scaffold designs. As cells are ultimately the drivers of G&R and known to change their behavior in response to mechanical cues, increasing efforts are currently undertaken to capture (mechano-mediated) cell behavior in computational models. In this selective review, we highlight some recent examples that are relevant in the context of cardiovascular tissue engineering and discuss the current and future biological and computational challenges for modeling cell-mediated G&R.

A critical review on the three-dimensional finite element modelling of the compression therapy for chronic venous insufficiency

Compression therapy is an adjuvant physical intervention providing the benefits of calibrated compression and controlled stretch and consequently is increasingly applied for the treatment of chronic venous insufficiency. However, the mechanism of the compression therapy for chronic venous insufficiency is still unclear. To elaborate the mechanism of compression therapy, in recent years, the computational modelling technique, especially the finite element modelling method, has been widely used. However, there are still many unclear issues regarding the finite element modelling of compression therapy, for example, the selection of appropriate material models, the validation of the finite element predictions, the post-processing of the results. To shed light on these unclear issues, this study provides a state-of-the-art review on the application of finite element modelling technique in the compression therapy for chronic venous insufficiency. The aims of the present study are as follows: (1) to provide guidance on the application of the finite element technique in healthcare and relevant fields, (2) to enhance the understanding of the mechanism of compression therapy and (3) to foster the collaborations among different disciplines. To achieve these aims, the following parts are reviewed: (1) the background on chronic venous insufficiency and the computational modelling approach, (2) the acquisition of medical images and the procedure for generating the finite element model, (3) the definition of material models in the finite element model, (4) the methods for validating the finite element predictions, (5) the post-processing of the finite element results and (6) future challenges in the finite element modelling of compression therapy.

A Twofold Usage of an Agent-Based Model of Vascular Adaptation to Design Clinical Experiments

Several computational models of Vein Graft Bypass (VGB) adaptation have been developed in order to improve the surgical outcome and they all share a common property: their accuracy relies on a winning choice of their driving coefficients which are best to be retrieved from experimental data. Since experiments are time-consuming and resources-demanding, the golden standard is to know in advance which measures need to be retrieved on the experimental table and out of how many samples. Accordingly, our goal is to build a computational framework able to pre-design an effective experimental structure to optimize the computational models setup. Our hypothesis is that an Agent-Based Model (ABM) developed by our group is comparable enough to a true set of experiments to be used to generate reliable virtual experimental data. Thanks to a twofold usage of our ABM, we created a filter to be posed before the real experiment in order to drive its optimal design. This work is the natural continuation of a previous study from our group [1], where the attention was posed on simple single-cellular events models. With this new version we focused on more complex models with the purpose of verifying that the complexity of the experimental setup grows proportionally with the accuracy of the model itself.