Rheological Analysis and Evaluation of Measurement Techniques for Curing Poly(Methyl Methacrylate) Bone Cement in Vertebroplasty.

Vertebroplasty is a minimally invasive surgical procedure used to treat vertebral fractures, which conventionally involves injecting poly(methyl methacrylate) (PMMA) bone cement into the fractured vertebra. A common risk associated with vertebroplasty is cement leaking out of the vertebra during the injection, which may occur due to a lack of understanding of the complex flow behavior. Therefore, experiments to quantify the cement's flow properties are necessary for understanding and proper handling of the bone cement. In this study, we aimed to characterize the behavior of PMMA bone cement in its curing stages to obtain parameters that govern the flow behavior during injection. We used rotational and oscillatory rheometry for our measurements, as well as a custom-made injector setup that replicated a typical vertebroplasty setting. Our results showed that the complex viscoelastic behavior of bone cement is significantly affected by deformations and temperature. We found that the results from rotational tests, often used for characterizing the bone cement, are susceptible to measurement artifacts caused by wall slip and "ridge"-like formations in the test sample. We also found the Cox-Merz rule to be conditionally valid, which affects the use of oscillatory tests to obtain the shear-thinning characteristics of bone cement. Our findings identify important differences in the measured flow behavior of PMMA bone cement when assessed by different rheological methods, an understanding that is crucial for its risk-free usage in downstream medical applications.

Quantifying fat zonation in liver lobules: an integrated multiscale in silico model combining disturbed microperfusion and fat metabolism via a continuum biomechanical bi-scale, tri-phasic approach.

Metabolic zonation refers to the spatial separation of metabolic functions along the sinusoidal axes of the liver. This phenomenon forms the foundation for adjusting hepatic metabolism to physiological requirements in health and disease (e.g., metabolic dysfunction-associated steatotic liver disease/MASLD). Zonated metabolic functions are influenced by zonal morphological abnormalities in the liver, such as periportal fibrosis and pericentral steatosis. We aim to analyze the interplay between microperfusion, oxygen gradient, fat metabolism and resulting zonated fat accumulation in a liver lobule. Therefore we developed a continuum biomechanical, tri-phasic, bi-scale, and multicomponent in silico model, which allows to numerically simulate coupled perfusion-function-growth interactions two-dimensionally in liver lobules. The developed homogenized model has the following specifications: (i) thermodynamically consistent, (ii) tri-phase model (tissue, fat, blood), (iii) penta-substances (glycogen, glucose, lactate, FFA, and oxygen), and (iv) bi-scale approach (lobule, cell). Our presented in silico model accounts for the mutual coupling between spatial and time-dependent liver perfusion, metabolic pathways and fat accumulation. The model thus allows the prediction of fat development in the liver lobule, depending on perfusion, oxygen and plasma concentration of free fatty acids (FFA), oxidative processes, the synthesis and the secretion of triglycerides (TGs). The use of a bi-scale approach allows in addition to focus on scale bridging processes. Thus, we will investigate how changes at the cellular scale affect perfusion at the lobular scale and vice versa. This allows to predict the zonation of fat distribution (periportal or pericentral) depending on initial conditions, as well as external and internal boundary value conditions.

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.

A continuum mechanical porous media model for vertebroplasty: Numerical simulations and experimental validation.

The outcome of vertebroplasty is hard to predict due to its dependence on complex factors like bone cement and marrow rheologies. Cement leakage could occur if the procedure is done incorrectly, potentially causing adverse complications. A reliable simulation could predict the patient-specific outcome preoperatively and avoid the risk of cement leakage. Therefore, the aim of this work was to introduce a computationally feasible and experimentally validated model for simulating vertebroplasty. The developed model is a multiphase continuum-mechanical macro-scale model based on the Theory of Porous Media. The related governing equations were discretized using a combined finite element-finite volume approach by the so-called Box discretization. Three different rheological upscaling methods were used to compare and determine the most suitable approach for this application. For validation, a benchmark experiment was set up and simulated using the model. The influence of bone marrow and parameters like permeability, porosity, etc., was investigated to study the effect of varying conditions on vertebroplasty. The presented model could realistically simulate the injection of bone cement in porous materials when used with the correct rheological upscaling models, of which the semi-analytical averaging of the viscosity gave the best results. The marrow viscosity is identified as the crucial reference to categorize bone cements as 'high- 'or 'low-' viscosity in the context of vertebroplasty. It is confirmed that a cement with higher viscosity than the marrow ensures stable development of the injection and a proper cement interdigitation inside the vertebra.

The effect of Caspian Sea water on mechanical properties and durability of concrete containing rice husk ash, nano [Formula: see text], and nano [Formula: see text].

Various studies have been recently conducted aiming at developing more sustainable cementitious systems so that concrete structures may not have a negative effect on the environment and are decomposed. It has been attempted to build sustainable binders by substituting silica fume, cement with fly ash, nano-silica, nano-alumina, and rice husk ash. In this paper, a series of experiments on concrete with different contents of rice husk ash (10%, 15%, and 20%), nano[Formula: see text] (1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%), and nano[Formula: see text] (1%, 2%, 3%, 4%) are performed to analyze the durability and mechanical properties of samples under the curing condition of Caspian seawater. The workability, density, water penetration, chloride ion penetration, and compressive strength (at 7, 14, 28, and 90 day) of the samples were determined. The experimental results showed that workability decreased gradually with increasing additives content, while the compressive gradually increased. Among the additives, adding 8% of the nano[Formula: see text] had the most significant effect on the improvement of compressive strength. Adding 8% nano[Formula: see text] and 4% nano[Formula: see text] reduced the depth of water permeability by 53% and 30%, respectively. Furthermore, adding 8% nano[Formula: see text] reduced chloride ion penetration by 85%.

Fractures and skin lesions in pediatric abusive head trauma: a forensic multi-center study.

Abusive head trauma (AHT) and its most common variant, the shaken baby syndrome (SBS), are predominantly characterized by central nervous system-associated lesions. Relatively little data are available on the value of skeletal and skin injuries for the diagnosis of SBS or AHT. Thus, the present study retrospectively investigated 72 cases of living children diagnosed with the explicit diagnosis of SBS during medico-legal examinations at three German university institutes of legal medicine. The risk of circular reasoning was reduced by the presence of 15 cases with confession by perpetrators. Accordingly, the comparison with the 57 non-confession cases yielded no significant differences. Skeletal survey by conventional projection radiography, often incomplete, was found to be performed in 78% of the cases only. Fractures were found in 32% of the cases. The skull (43%) and ribs (48%) were affected most frequently; only 8% of the cases showed classic metaphyseal lesions. In 48% of the cases, healing fractures were present. Skin lesions (hematomas and abrasions) were found in 53% of the cases with the face (76%), scalp (26%), and trunk (50%) being the major sites. In 48% of the cases, healing skin lesions were observed. Nearly 80% of the cases with fractures also showed skin lesions. The data prove that SBS is frequently accompanied by other forms of physical abuse. Therefore, skeletal survey is indispensable and should always be done completely and according to existing imaging guidelines if child abuse is suspected.

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function.

Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.

Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review.

MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.

On incorporating osmotic prestretch/prestress in image-driven finite element simulations of cartilage.

Medical imaging performed in vivo captures geometries under Donnan osmotic loading, even when the articulating joint is otherwise mechanically unloaded. Hence patient-specific finite element (FE) models constructed from such medical images of cartilage represent osmotically induced prestretched/prestressed states. When applying classical modeling approaches to patient-specific simulations of cartilage a theoretical inconsistency arises: the in-vivo imaged geometry (used to construct the model) is not an unloaded, stress-free reference configuration. Furthermore when fitting nonlinear constitutive models that include osmotic swelling (to obtain material parameters), if one assumes that experimental data-generated from osmotically loaded cartilage-begin from a stress-free reference configuration the fitted stress-stretch relationship (parameters) obtained will actually describe a different behavior. In this study we: (1) establish a practical computational method to include osmotically induced prestretch/prestress in image-driven simulations of cartilage; and (2) investigate the influence of considering the prestretched/prestressed state both when fitting fiber-reinforced, biphasic constitutive models of cartilage that include osmotic swelling and when simulating cartilage responses. Our results highlight the importance of determining the prestretched/prestressed state within cartilage induced by osmotic loading in the imaged configuration prior to solving boundary value problems of interest. With our new constitutive model and modeling methods, we aim to improve the fidelity of FE-based, patient-specific biomechanical simulations of joints and cartilage. Improved simulations can provide medical researchers with new information often unavailable in a clinical setting, information that may contribute to better insight into the pathophysiology of cartilage diseases.

Computational Modeling in Liver Surgery.

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

Characterization of methane oxidation in a simulated landfill cover system by comparing molecular and stable isotope mass balances.

Biological methane oxidation may be regarded as a method of aftercare treatment for landfills to reduce climate relevant methane emissions. It is of social and economic interest to estimate the behavior of bacterial methane oxidation in aged landfill covers due to an adequate long-term treatment of the gas emissions. Different approaches assessing methane oxidation in laboratory column studies have been investigated by other authors recently. However, this work represents the first study in which three independent approaches, ((i) mass balance, (ii) stable isotope analysis, and (iii) stoichiometric balance of product (CO2) and reactant (CH4) by CO2/CH4-ratio) have been compared for the estimation of the biodegradation by a robust statistical validation on a rectangular, wide soil column. Additionally, an evaluation by thermal imaging as a potential technique for the localization of the active zone of bacterial methane oxidation has been addressed in connection with stable isotope analysis and CO2/CH4-ratios. Although landfills can be considered as open systems the results for stable isotope analysis based on a closed system correlated better with the mass balance than calculations based on an open system. CO2/CH4-ratios were also in good agreement with mass balance. In general, highest values for biodegradation were determined from mass balance, followed by CO2/CH4-ratio, and stable isotope analysis. The investigated topsoil proved to be very suitable as a potential cover layer by removing up to 99% of methane for CH4 loads of 35-65gm-2d-1 that are typical in the aftercare phase of landfills. Finally, data from stable isotope analysis and the CO2/CH4-ratios were used to trace microbial activity within the reactor system. It was shown that methane consumption and temperature increase, as a cause of high microbial activity, correlated very well.

A microstructurally based continuum model of cartilage viscoelasticity and permeability incorporating measured statistical fiber orientations.

The remarkable mechanical properties of cartilage derive from an interplay of isotropically distributed, densely packed and negatively charged proteoglycans; a highly anisotropic and inhomogeneously oriented fiber network of collagens; and an interstitial electrolytic fluid. We propose a new 3D finite strain constitutive model capable of simultaneously addressing both solid (reinforcement) and fluid (permeability) dependence of the tissue's mechanical response on the patient-specific collagen fiber network. To represent fiber reinforcement, we integrate the strain energies of single collagen fibers-weighted by an orientation distribution function (ODF) defined over a unit sphere-over the distributed fiber orientations in 3D. We define the anisotropic intrinsic permeability of the tissue with a structure tensor based again on the integration of the local ODF over all spatial fiber orientations. By design, our modeling formulation accepts structural data on patient-specific collagen fiber networks as determined via diffusion tensor MRI. We implement our new model in 3D large strain finite elements and study the distributions of interstitial fluid pressure, fluid pressure load support and shear stress within a cartilage sample under indentation. Results show that the fiber network dramatically increases interstitial fluid pressure and focuses it near the surface. Inhomogeneity in the tissue's composition also increases fluid pressure and reduces shear stress in the solid. Finally, a biphasic neo-Hookean material model, as is available in commercial finite element codes, does not capture important features of the intra-tissue response, e.g., distributions of interstitial fluid pressure and principal shear stress.

A hyperelastic biphasic fibre-reinforced model of articular cartilage considering distributed collagen fibre orientations: continuum basis, computational aspects and applications.

Cartilage is a multi-phase material composed of fluid and electrolytes (68-85% by wet weight), proteoglycans (5-10% by wet weight), chondrocytes, collagen fibres and other glycoproteins. The solid phase constitutes an isotropic proteoglycan gel and a fibre network of predominantly type II collagen, which provides tensile strength and mechanical stiffness. The same two components control diffusion of the fluid phase, e.g. as visualised by diffusion tensor MRI: (i) the proteoglycan gel (giving a baseline isotropic diffusivity) and (ii) the highly anisotropic collagenous fibre network. We propose a new constitutive model and finite element implementation that focus on the essential load-bearing morphology: an incompressible, poroelastic solid matrix reinforced by an inhomogeneous, dispersed fibre fabric, which is saturated with an incompressible fluid residing in strain-dependent pores of the collagen-proteoglycan solid matrix. The inhomogeneous, dispersed fibre fabric of the solid further influences the fluid permeability, as well as an intrafibrillar portion that cannot be 'squeezed out' from the tissue. Using representative numerical examples on the mechanical response of cartilage, we reproduce several features that have been demonstrated experimentally in the cartilage mechanics literature.

Structural analysis of articular cartilage using multiphoton microscopy: input for biomechanical modeling.

The 3-D morphology of chicken articular cartilage was quantified using multiphoton microscopy (MPM) for use in continuum-mechanical modeling. To motivate this morphological study we propose aspects of a new, 3-D finite strain constitutive model for articular cartilage focusing on the essential load-bearing morphology: an inhomogeneous, poro-(visco)elastic solid matrix reinforced by an anisotropic, (visco)elastic dispersed fiber fabric which is saturated by an incompressible fluid residing in strain-dependent pores. Samples of fresh chicken cartilage were sectioned in three orthogonal planes and imaged using MPM, specifically imaging the collagen fibers using second harmonic generation. Employing image analysis techniques based on Fourier analysis, we derived the principal directionality and dispersion of the collagen fiber fabric in the superficial layer. In the middle layer, objective thresholding techniques were used to extract the volume fraction occupied by extracellular collagen matrix. In conjunction with information available in the literature, or additional experimental testing, we show how this data can be used to derive a 3-D map of the initial solid volume fraction and Darcy permeability.

Multigenerational interstitial growth of biological tissues.

This study formulates a theory for multigenerational interstitial growth of biological tissues whereby each generation has a distinct reference configuration determined at the time of its deposition. In this model, the solid matrix of a growing tissue consists of a multiplicity of intermingled porous permeable bodies, each of which represents a generation, all of which are constrained to move together in the current configuration. Each generation's reference configuration has a one-to-one mapping with the master reference configuration, which is typically that of the first generation. This mapping is postulated based on a constitutive assumption with regard to that generations' state of stress at the time of its deposition. For example, the newly deposited generation may be assumed to be in a stress-free state, even though the underlying tissue is in a loaded configuration. The mass content of each generation may vary over time as a result of growth or degradation, thereby altering the material properties of the tissue. A finite element implementation of this framework is used to provide several illustrative examples, including interstitial growth by cell division followed by matrix turnover.

A biphasic model for sinusoidal liver perfusion remodeling after outflow obstruction.

Liver resection can lead to focal outflow obstruction due to transection of hepatic veins. Outflow obstruction may cause additional damage to the small remnant liver. Drainage of the obstructed territories is reestablished via dilatation of sinusoids. Subsequently, sinusoidal canals are formed draining the blood from the obstructed territory to the neighboring unobstructed territories. We raised the phenomenological hypothesis that the blood pressure gradient is the main driving force for the formation of sinusoidal vascular canals. We generated a biphasic mechanical model to describe this vascular remodeling process in relation to the variable pressure gradient. Therefore, we introduced a transverse isotropic permeability relation as well as an evolutional optimization rule to describe the relationship between pressure gradient and the direction of the sinusoidal blood flow in the fluid phase. As a next step, we developed a framework for the calculation concept including the representation of the governing weak formulations. Then, we examined a representative numerical example with simulation of the blood flow under both conditions, the physiological situation as well as after outflow obstruction. Doing so, we were able to reproduce numerically the experimentally observed process of reestablishing hepatic venous drainage via redirection of blood flow and formation of new vascular structures in respect to the fluid flow. The calculated results support the hypothesis that the reorientation of blood flow mainly depends on the pressure gradient. Further investigations are needed to determine the micromechanical influences on the reorientation of the sinusoids.

Estimation of landfill emission lifespan using process oriented modeling.

Depending on the particular pollutants emitted, landfills may require service activities lasting from hundreds to thousands of years. Flexible tools allowing long-term predictions of emissions are of key importance to determine the nature and expected duration of maintenance and post-closure activities. A highly capable option represents predictions based on models and verified by experiments that are fast, flexible and allow for the comparison of various possible operation scenarios in order to find the most appropriate one. The intention of the presented work was to develop a experimentally verified multi-dimensional predictive model capable of quantifying and estimating processes taking place in landfill sites where coupled process description allows precise time and space resolution. This constitutive 2-dimensional model is based on the macromechanical theory of porous media (TPM) for a saturated thermo-elastic porous body. The model was used to simulate simultaneously occurring processes: organic phase transition, gas emissions, heat transport, and settlement behavior on a long time scale for municipal solid waste deposited in a landfill. The relationships between the properties (composition, pore structure) of a landfill and the conversion and multi-phase transport phenomena inside it were experimentally determined. In this paper, we present both the theoretical background of the model and the results of the simulations at one single point as well as in a vertical landfill cross section.