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In the present work, we introduce a scheme for efficient mechanobiological research on the dedifferentiation of chondrocytes (CHs) using a deep neural network (DNN) model that does not require sacrificing the cells and thus, saving resources. Cells are a part of active biological systems subjected to physical stimuli such as mechanical loading. Such loading affects cellular processes including proliferation, differentiation, and the interplay with the surrounding environment. CHs are mechanosensitive cells that produce and maintain the cartilaginous matrix that allows cartilage to bear and distribute mechanical loads in joints; their role in cartilage regeneration and treatment of osteoarthritis (OA) has been the focus of research projects. Nevertheless, it remains an open challenge to fully understand the effect of mechanical stimuli on CHs. One of the unresolved mechanobiological behaviors of CHs is dedifferentiation. Dedifferentiation of CHs is the phenomenon where isolated CHs alter their phenotype when cultured in a 2D in vitro environment over passages. While intact, cobblestone‐shaped CHs mainly produce collagen II, dedifferentiated CHs with elongated shapes produce fibroblastic collagen I. This limits the scalability of a promising treatment for OA, called ‘autologous chondrocyte implantation (ACI)’. To overcome this limit, research is being carried out to understand the dedifferentiation mechanism in detail. This proposed scheme is composed of three parts: (i) the cell‐seeded specimens are loaded in a bioreactor system, (ii) an optical microscope is used to obtain the phase‐contrast images of living cells, and (iii) the images are analyzed using a DNN. This scheme provides an efficient tool to analyze the ratio of intact to dedifferentiated CHs while preserving cells on our way to elucidate the effects of mechanical loading on the dedifferentiation of CHs.
In the present work, we introduce a scheme for efficient mechanobiological research on the dedifferentiation of chondrocytes (CHs) using a deep neural network (DNN) model that does not require sacrificing the cells and thus, saving resources. Cells are a part of active biological systems subjected to physical stimuli such as mechanical loading. Such loading affects cellular processes including proliferation, differentiation, and the interplay with the surrounding environment. CHs are mechanosensitive cells that produce and maintain the cartilaginous matrix that allows cartilage to bear and distribute mechanical loads in joints; their role in cartilage regeneration and treatment of osteoarthritis (OA) has been the focus of research projects. Nevertheless, it remains an open challenge to fully understand the effect of mechanical stimuli on CHs. One of the unresolved mechanobiological behaviors of CHs is dedifferentiation. Dedifferentiation of CHs is the phenomenon where isolated CHs alter their phenotype when cultured in a 2D in vitro environment over passages. While intact, cobblestone‐shaped CHs mainly produce collagen II, dedifferentiated CHs with elongated shapes produce fibroblastic collagen I. This limits the scalability of a promising treatment for OA, called ‘autologous chondrocyte implantation (ACI)’. To overcome this limit, research is being carried out to understand the dedifferentiation mechanism in detail. This proposed scheme is composed of three parts: (i) the cell‐seeded specimens are loaded in a bioreactor system, (ii) an optical microscope is used to obtain the phase‐contrast images of living cells, and (iii) the images are analyzed using a DNN. This scheme provides an efficient tool to analyze the ratio of intact to dedifferentiated CHs while preserving cells on our way to elucidate the effects of mechanical loading on the dedifferentiation of CHs.
In this article, we apply the sensitivity analysis method to capture the influence of various parameters on the inflation pressure, axial force, and the deformation for an inflated and axially stretched cylinder. The material consists of an isotropic ground substance material reinforced with fibers that undergo a continuous and mechano-sensitive remodeling process. The input parameters of the mechanical system are assumed to be distributed according to the uniform probability distribution function. In the sensitivity analysis, we apply the Sobol method to determine how the variations of input parameters affect the inflation as well as the axial force in the cylinder. Special attention is given to the fiber remodeling process associated with a homeostatic balance between the constant fiber creation process and the strain-stabilized fiber dissolution. The results may help to understand the importance of the effect of material parameter changes, for example, due to remodeling processes in the context of diseases or recovering processes, on the overall tissue behavior.
In this article, we apply sensitivity analysis (SA) to study the pressure–inflation relation and axial force in a pressurized and extended cylindrical tube. The material consists of an isotropic ground substance that is reinforced in the azimuthal direction with one family of fibers which are taken to be dispersed about that (mean) direction. The natural configuration of the fibers may differ from that of the ground substance, either because the fibers are pre-stretched or because the bonding between the fibers and the ground substance is considered to be imperfect. The axial stretch of the cylindrical membrane is given by a constant value. The input parameters data of the mechanical system, namely, the azimuthal stretch of the cylinder, the fiber dispersion, and the fiber natural configurations, are assumed to be distributed according to three probability distribution functions. In the sensitivity analysis, we apply the Sobol method as well as the Fourier amplitude sensitivity test (FAST) method to determine the way in which variations of the input parameters affect the required inflation pressure and corresponding axial force (output variables). The implementation of the Sobol and FAST methods allows us to account for the interplay of different parameters as well as to identify the most influential parameters in both the pressure–inflation relation and the axial force. The analysis singles out all these aspects showing a rich variety of results.
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