A Multiscale Model to Predict Neuronal Cell Deformation with Varying Extracellular Matrix Stiffness and Topography

Predicting ultrasound wave stimulated bone growth in bioinspired scaffolds using machine learning

For conditions like osteoporosis, changes in bone pore geometry even when porosity is constant have been shown to correlate to increased fracture risk using techniques such as dual-energy x-ray absorptiometry (DXA) and computed tomography (CT). Additionally, studies have found that bone pore geometry can be characterized by ultrasound to determine fracture risk, since certain pore geometries can cause stress concentration which in turn will be a source for fracture. However, it is not yet fully understood if changes in pore geometry can be detected by ultrasound when the porosity is constant. Therefore, this study develops an unsupervised machine learning model classifying pore geometry between bioinspired and quadrilateral pore scaffolds with constant porosity using experimental ultrasound wave transmission data. Our results demonstrate that differences in pore geometry can be detected by ultrasound, even at constant porosity, and that these differences can be distinguished in an unsupervised manner with machine learning. For traumatic bone injuries and late-stage osteoporosis where fracture occurs, tissue scaffolds are used to aid the healing of fractures or bone loss. The scaffold design is optimized to match material properties closely with bone, and healing can be enhanced with ultrasound stimulation. In this study we predict the combined effects of ultrasound parameters, such as wave frequency and mode of displacement, and scaffold material properties on bone tissue growth. We therefore develop an unsupervised machine learning clustering model of bone tissue growth in the scaffolds using finite element analysis and bone growth algorithms evaluating effects of pore geometry, scaffold materials, ultrasound wave type and frequency, and mesenchymal stem cell distribution on bone tissue growth. The computational predictions of tissue growth agreed within 10% of comparable experimental studies. The data corresponding to pore geometry, mesenchymal stem cell distribution, and scaffold material demonstrate distinct clusters of total bone formation, while ultrasound frequency and mesenchymal stem cell distribution show distinct clusters in bone growth rate. These variables can be tuned to tailor the scaffold design and optimize the required amount and rate of bone growth to meet a patient's specific needs.

Cardiac tissue engineering: an emerging approach to the treatment of heart failure

Heart failure is a major health problem in which the heart is unable to pump enough blood to meet the body's needs. It is a progressive disease that becomes more severe over time and can be caused by a variety of factors, including heart attack, cardiomyopathy and heart valve disease. There are various methods to cure this disease, which has many complications and risks. The advancement of knowledge and technology has proposed new methods for many diseases. One of the promising new treatments for heart failure is tissue engineering. Tissue engineering is a field of research that aims to create living tissues and organs to replace damaged or diseased tissue. The goal of tissue engineering in heart failure is to improve cardiac function and reduce the need for heart transplantation. This can be done using the three important principles of cells, biomaterials and signals to improve function or replace heart tissue. The techniques for using cells and biomaterials such as electrospinning, hydrogel synthesis, decellularization, etc. are diverse. Treating heart failure through tissue engineering is still under development and research, but it is hoped that there will be no transplants or invasive surgeries in the near future. In this study, based on the most important research in recent years, we will examine the power of tissue engineering in the treatment of heart failure.

Predicting neurite extension for varying extracellular matrix stiffness and topography

Neurite extension is a dynamic process and is dependent on the microenvironment. The mechanical properties of the extracellular matrix (ECM), such as stiffness and topography influence the microenvironment and affects neurite extension; however, the mechanistic basis for this dynamic response of neurite extension remains elusive. In this study, we develop a computational model that predicts neurite extension dynamics process as the stiffness and patterned topography of ECM changes. The model includes the contribution of receptors integrin and neural cellular adhesion molecule toward the growth of neurite tip. We use non-linear finite element analysis (FEA) to model the neuronal cell, neurite, and the ECM, which is then coupled to the force-deformation receptor properties obtained from molecular dynamics simulations. Using an empirical relation, we develop a neurite extension algorithm that simulates the dynamic process of growth cone induced by growth cone extension, receptor density, and rupture. We investigate the dependence of neurite extension on ECM stiffness using three distinct materials, the effect of width and spacing of continuous (cylindrical) and discontinuous (pillar) patterned topography, as well as the topography steepness and stiffness gradient. We find that an increasing stiffness and width of patterned topography results in increased neurite extension, but the magnitude of the increase differs depending on the growth cone extension and receptor density between them. These findings will aid in vitro studies in determining an ECM with appropriate mechanical properties, such as stiffness and topography that will improve neurite extension, thus resulting in the formation of functional neurons.

Local tissue heterogeneity may modulate neuronal responses via altered axon strain fields: insights about innervated joint capsules from a computational model

In innervated collagenous tissues, tissue scale loading may contribute to joint pain by transmitting force through collagen fibers to the embedded mechanosensitive axons. However, the highly heterogeneous collagen structures of native tissues make understanding this relationship challenging. Recently, collagen gels with embedded axons were stretched and the resulting axon signals were measured, but these experiments were unable to measure the local axon strain fields. Computational discrete fiber network models can directly determine axon strain fields due to tissue scale loading. Therefore, this study used a discrete fiber network model to identify how heterogeneous collagen networks (networks with multiple collagen fiber densities) change axon strain due to tissue scale loading. In this model, a composite cylinder (axon) was embedded in a Delaunay network (collagen). Homogeneous networks with a single collagen volume fraction and two types of heterogeneous networks with either a sparse center or dense center were created. Measurements of fiber forces show higher magnitude forces in sparse regions of heterogeneous networks and uniform force distributions in homogeneous networks. The average axon strain in the sparse center networks decreases when compared to homogeneous networks with similar collagen volume fractions. In dense center networks, the average axon strain increases compared to homogeneous networks. The top 1% of axon strains are unaffected by network heterogeneity. Based on these results, the interaction of tissue scale loading, collagen network heterogeneity, and axon strains in native musculoskeletal tissues should be considered when investigating the source of joint pain.

Long-range mechanical signaling in biological systems

Cells can respond to signals generated by other cells that are remarkably far away. Studies from at least the 1920's showed that cells move toward each other when the distance between them is on the order of a millimeter, which is many times the cell diameter. Chemical signals generated by molecules diffusing from the cell surface would move too slowly and dissipate too fast to account for these effects, suggesting that they might be physical rather than biochemical. The non-linear elastic responses of sparsely connected networks of stiff or semiflexible filament such as those that form the extracellular matrix (ECM) and the cytoskeleton have unusual properties that suggest multiple mechanisms for long-range signaling in biological tissues. These include not only direct force transmission, but also highly non-uniform local deformations, and force-generated changes in fiber alignment and density. Defining how fibrous networks respond to cell-generated forces can help design new methods to characterize abnormal tissues and can guide development of improved biomimetic materials.