Magnetic Resonance Imaging-based biomechanical simulation of cartilage: A systematic review

Hippo cell signaling and HS-proteoglycans regulate tissue form and function, age-dependent maturation, extracellular matrix remodeling, and repair

This review examined how Hippo cell signaling and heparan sulfate (HS)-proteoglycans (HSPGs) regulate tissue form and function. Despite being a nonweight-bearing tissue, the brain is regulated by Hippo mechanoresponsive cell signaling pathways during embryonic development. HS-proteoglycans interact with growth factors, morphogens, and extracellular matrix components to regulate development and pathology. Pikachurin and Eyes shut (Eys) interact with dystroglycan to stabilize the photoreceptor axoneme primary cilium and ribbon synapse facilitating phototransduction and neurotransduction with bipolar retinal neuronal networks in ocular vision, the primary human sense. Another HSPG, Neurexin interacts with structural and adaptor proteins to stabilize synapses and ensure specificity of neural interactions, and aids in synaptic potentiation and plasticity in neurotransduction. HSPGs also stabilize the blood-brain barrier and motor neuron basal structures in the neuromuscular junction. Agrin and perlecan localize acetylcholinesterase and its receptors in the neuromuscular junction essential for neuromuscular control. The primary cilium is a mechanosensory hub on neurons, utilized by YES associated protein (YAP)-transcriptional coactivator with PDZ-binding motif (TAZ) Hippo, Hh, Wnt, transforming growth factor (TGF)-β/bone matrix protein (BMP) receptor tyrosine kinase cell signaling. Members of the glypican HSPG proteoglycan family interact with Smoothened and Patched G-protein coupled receptors on the cilium to regulate Hh and Wnt signaling during neuronal development. Control of glycosyl sulfotransferases and endogenous protease expression by Hippo TAZ YAP represents a mechanism whereby the fine structure of HS-proteoglycans can be potentially modulated spatiotemporally to regulate tissue morphogenesis in a similar manner to how Hippo signaling controls sialyltransferase expression and mediation of cell-cell recognition, dysfunctional sialic acid expression is a feature of many tumors.

Simulating impaired left ventricular-arterial coupling in aging and disease: a systematic review

Aortic stenosis, hypertension, and left ventricular hypertrophy often coexist in the elderly, causing a detrimental mismatch in coupling between the heart and vasculature known as ventricular-vascular (VA) coupling. Impaired left VA coupling, a critical aspect of cardiovascular dysfunction in aging and disease, poses significant challenges for optimal cardiovascular performance. This systematic review aims to assess the impact of simulating and studying this coupling through computational models. By conducting a comprehensive analysis of 34 relevant articles obtained from esteemed databases such as Web of Science, Scopus, and PubMed until July 14, 2022, we explore various modeling techniques and simulation approaches employed to unravel the complex mechanisms underlying this impairment. Our review highlights the essential role of computational models in providing detailed insights beyond clinical observations, enabling a deeper understanding of the cardiovascular system. By elucidating the existing models of the heart (3D, 2D, and 0D), cardiac valves, and blood vessels (3D, 1D, and 0D), as well as discussing mechanical boundary conditions, model parameterization and validation, coupling approaches, computer resources and diverse applications, we establish a comprehensive overview of the field. The descriptions as well as the pros and cons on the choices of different dimensionality in heart, valve, and circulation are provided. Crucially, we emphasize the significance of evaluating heart-vessel interaction in pathological conditions and propose future research directions, such as the development of fully coupled personalized multidimensional models, integration of deep learning techniques, and comprehensive assessment of confounding effects on biomarkers.

Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem

Physics-informed neural networks (PINNs) leverage data and knowledge about a problem. They provide a nonnumerical pathway to solving partial differential equations by expressing the field solution as an artificial neural network. This approach has been applied successfully to various types of differential equations. A major area of research on PINNs is the application to coupled partial differential equations in particular, and a general breakthrough is still lacking. In coupled equations, the optimization operates in a critical conflict between boundary conditions and the underlying equations, which often requires either many iterations or complex schemes to avoid trivial solutions and to achieve convergence. We provide empirical evidence for the mitigation of bad initial conditioning in PINNs for solving one-dimensional consolidation problems of porous media through the introduction of affine transformations after the classical output layer of artificial neural network architectures, effectively accelerating the training process. These affine physics-informed neural networks (AfPINNs) then produce nontrivial and accurate field solutions even in parameter spaces with diverging orders of magnitude. On average, AfPINNs show the ability to improve the [Formula: see text] relative error by [Formula: see text] after 25,000 epochs for a one-dimensional consolidation problem based on Biot's theory, and an average improvement by [Formula: see text] with a transfer approach to the theory of porous media.