Ballooning and rupture-induced oscillations in fast reactor fuel cladding: Experimental insights and computational modeling

A computational elbow joint model was developed with a main goal of providing complimentary data to experimental results. The computational model was developed and validated using an experimental elbow joint phantom consisting of a linked total joint replacement. An established in-vitro motion simulator was used to actively flex/extend the experimental elbow in multiple orientations. Muscle forces predicted by the computational model were similar to the experimental model in 4 out of the 5 orientations with errors less than 7.5 N. Valgus angle kinematics were in agreement with differences less than 2.3°. In addition, changes in radial head length, a clinically relevant condition following elbow reconstruction, were simulated in both models and compared. Both lengthening and shortening of the radial head prosthesis altered muscle forces by less than 3.5 N in both models, and valgus angles agreed within 1°. The computational model proved valuable in cross validation with the experimental model, elucidating important limitations in the in-vitro motion simulator's controller. With continued development, the computational model can be a complimentary tool to experimental studies by providing additional noninvasive outcome measurements.

Experimental validation of finite elements model in hip fracture and its clinical applicability

Validación experimental de un modelo de análisis de elementos finitos en fractura de cadera y su aplicabilidad clínica

Event Abstract Back to Event Mapping convective and diffusive transport in 3D printed vascularized tissues Samantha Paulsen1, Bagrat Grigoryan1 and Jordan Miller1 1 Rice University, Bioengineering, United States Introduction: 3D printing provides a promising method to establish complex, customizable vascular networks within engineered tissues[1,][2]. Though computational modeling offers unique understanding of essential parameters within complex tissues, few research groups first verify their computational models in vitro or in vivo, a necessary step for using computational models to aid in the design and production of 3D printed tissues[2]. In this work, we begin to verify computational models of 3D printed vascularized tissues to understand the role of vascular architecture in altering the cellular environment. Methods: Using an open-source stereolithography printer developed in our lab, we printed PEG-DA gels containing branched micro-channel networks. To assess convective transport through the channels, we tracked the flow of fluorescent beads using an epifluorescent microscope and PIVlab software[3]. To map diffusion rates of different sized molecules into the gels, we assessed the transport of fluorescently labeled dextrans (10 kDa and 3 kDa) along with methacrylated rhodamine into the gels. Finally, to assess cell viability following the printing process, we printed gels containing 293T cells transduced to express Luc2P. We then measured the luminescent output of the gels in the presence of luciferin after 5 days in culture. To develop parallel computational models for these gels, we used a 3D model of our printed channel networks to develop computational models for flow and diffusion through the gels (Fig 1). Results and Discussion: Results from bead tracking data for 5 printed gels indicate that average flow rates varied noticeably between channels, with the first and last channels having the highest flow rates (Fig 1). After normalizing the inflow rate of our computational model to that of our printed gels, the computational models corresponded strongly in vitro data (Fig 1). These results show the capability of computational modeling to demonstrate non-obvious flow patterns in 3D printed tissues along with the necessity for in vitro verification. For initial diffusion data, trends show that small molecules diffused rapidly (within 10 min) to distances of 200 μm, while larger dextrans took upwards of 60 min (Fig 2). These results demonstrate the need for improved transport mechanisms to deliver proteins and larger molecules to interstitial cells, possibly through the use of superphysiological flow rates or the addition of capillary networks. Finally, after 5 days in culture, a strong luminescent output was still detected from printed cells, suggesting that cells can survive the initial printing process and may be viable in long term culture. For future work we will compare computational and experimental models for diffusion in printed gels, and will develop computational models for cell viability. With this groundwork, computational models are an invaluable resource in making high throughput optimizations for the design of 3D printed tissues. Palvasha Deme; Alex Zaita

PurposeThis research aims to provide a innovative class of shape-memory-responsive cellulosic composites (RCC) for 4D printing, enabling self-activated, reversible shape morphing. By integrating experimental, theoretical, and computational modeling, the study optimizes material behavior, offering precise curvature predictions for advanced biomedical and pharmaceutical applications.Design/methodology/approachThis study presents an innovative class of shape–memory–responsive cellulosic composites (RCC), with a unique combination of starch and AffnisolTM. RCC-mediated filaments were used to print single-layer strips using fused deposition modeling 3D printing technology. The printed single-layer strip exhibited reversible, contactless and self-activated shape morphing in response to swelling and heat. The programming stage involves the swelling and heating of the composite strip and subsequent shape recovery through heating. The shape deformation during the self-activated programming stage was both estimated and predicted using simple experimental, theoretical and computational tools. The study was conducted at different thicknesses (1.5, 2.0 and 2.5 mm) and temperatures (25°C and 37°C) to validate the performance of the developed model in predicting bending curvature.FindingsThe developed model showed less than a 13.96 % difference in curvature predicted using theoretical and experimental modeling at studied temperatures. At lower thicknesses, the model can predict the bending curvature with less than a 2.0 % difference in curvature. These RCC materials exhibited potential reversible 4D printing capacity and satisfied the adopted approaches and modeling to forecast the bending curvature for reversible 4D printing.Originality/valueThis study introduces a new class of composite materials for potential 4D applications and provides simple predictive models to forecast bending curvature in reversible 4D printing.

To devise experimental and computational models to predict aqueous drug solubility. A simple and reliable modification of the shake flask method to a small-scale format was devised, and the intrinsic solubilities of 17 structurally diverse drugs were determined. The experimental solubility data were used to investigate the accuracy of commonly used theoretical and semiexperimental models for prediction of aqueous drug solubility. Computational models for prediction of intrinsic solubility, based on lipophilicity and molecular surface areas, were developed. The intrinsic solubilities ranged from 0.7 ng/mL to 6.0 mg/ mL, covering a range of almost seven log10 units, and the values determined with the new small-scale shake flask method agreed well with published solubility data. Solubility data computed with established theoretical models agreed poorly with the experimentally determined solubilities, but the correlations improved when experimentally determined melting points were included in the models. A new, fast computational model based on lipophilicity and partitioned molecular surface areas, which predicted intrinsic drug solubility with a good accuracy (R2 of 0.91 and RMSEtr of 0.61) was devised. A small-scale shake flask method for determination of intrinsic drug solubility was developed, and a promising alternative computational model for the theoretical prediction of aqueous drug solubility was proposed.

The aim of this study was to devise experimental protocols and computational models for the prediction of intestinal drug permeability. Both the required experimental and computational effort and the accuracy and quality of the resulting predictions were considered. In vitro intestinal Caco-2 cell monolayer permeabilities were determined both in a highly accurate experimental setting (Pc) and in a faster, but less accurate, mode (Papp). Computational models were built using four different principles for generation of molecular descriptors (atom counts, molecular mechanics calculations, fragmental, and quantum mechanics approaches) and were evaluated for their ability to predict intestinal membrane permeability. A theoretical deconvolution of the polar molecular surface area (PSA) was also performed to facilitate the interpretation of this composite descriptor and allow the calculation of PSA in a simplified and fast mode. The results indicate that it is possible to predict intestinal drug permeability from rather simple models with little or no loss of accuracy. A new, fast computational model, based on partitioned molecular surface areas, that predicts intestinal drug permeability with an accuracy comparable to that of time-consuming quantum mechanics calculations is presented.

Transient flow in a pipe was studied using both experimental and computer models. In the present study, three different numerical models: The method of characteristics model, the axisymmetrical model, and the implicit scheme model are utilized and compared. Experiments for transient flow in a simple pipeline have been conducted to verify the results from the computer models. It was found that head loss coefficient for the 1D models, such as the method of characteristics model and the implicit scheme model, should be much bigger than the Darcy-Weisbach frictional coefficient. Experiments for transient flow with the backflow preventer in a pipe were conducted. Results show that backflow preventer serves as a strong damper to the water hammer generated by the hydraulic transients. Numerical investigation simulating a backflow preventer in transient flow has been performed in this study. It was found that different values of head loss coefficient should be applied for the upstream and downstream of backflow preventer. All of the numerical models were compared with the experiments. The results of different computer models developed in the present study agree well with the experimental data.

Urinary flow is governed by the principles of fluid mechanics. Urodynamic studies have revealed the fundamental kinematics and dynamics of urinary flow in various physiological and pathological conditions, which are cornerstones for future development of diagnostic knowledge and innovative devices. There are three primary approaches to study the fluid mechanical characteristics of urinary flow: reduced order, computational, and experimental methods. Reduced-order methods exploit the disparate length scales inherent in the system to reveal the key dominant physics. Computational models can simulate fully three-dimensional, time-dependent flows in physiologically-inspired anatomical domains. Finally, experimental models provide an excellent counterpart to reduced and computational models by providing physical tests under various physiological and pathological conditions. While the interdisciplinary approaches to date have provided a wealth of insight into the fluid mechanical properties of the stented ureter, the next challenge is to develop new theoretical, computational and experimental models to capture the complex interplay between the fluid dynamics in stented ureters and biofilm/encrustation growth. Such studies will (1) enable identification of clinically relevant scenarios to improve patients’ treatment, and (2) provide physical guidelines for next-generation stent design.

In osteoarthritis, articular cartilage degenerates and eventually wears out, resulting in pain and disability. It is a major challenge in health care to prevent osteoarthritis or slow down the progression of the disease. The onset of osteoarthritis may result from an altered stress and strain state in cartilage, for example, due to an injury of ligament, cartilage, or meniscus. However, the disease progression is patient specific and can hardly be predicted. In order to assess the articular cartilage function and possible failure sites in joints, and to evaluate the onset and progression of osteoarthritis patient-specifically, computational models may become useful tools. Although there has been significant progress over the last years, these models need to be further developed before they may eventually become of direct clinical value. For clinical, patient-specific application of a computer model, a realistic description of joint geometry, joint kinematics, and tissue loading is ultimately required. Geometrical data can be obtained from imaging modalities, such as MRI, which become increasingly more detailed. Still, meshing of such information and running computational simulations of such meshes is challenging. Simulation analysis of joint kinematics is computationally expensive and requires knowledge of the kinematic constraints as a consequence of muscles and ligaments, including, for instance, pre stresses and attachment sites of ligaments. In many cases, 3D and/or kinematic simulations are still more feasible in combination with simpler material models for cartilage. More advanced material descriptions are required for a more detailed investigation of mechanical conditions inside cartilage. However, these are essential to move forward in our understanding of the relationship between mechanical loading, cartilage damage, and osteoarthritis. A wide variety of cartilage models have been developed and employed to evaluate the mechanical behavior at the cell, tissue, and joint level. They have been used to evaluate static and dynamic tissue behavior, to explore effects of mechanical and biochemical loading, and even to predict tissue adaptation at the long time scale. The expectation is that such models may be taken to the next level, where patient-specific characteristics are incorporated in 3D kinematic joint models. Given the composite structure of cartilage with the collagen fibril network, proteoglycan matrix, and fluid, this special issue has a focus on the development and application of fibril-reinforced models at different scales. A review of the fibril-reinforced tissue models and their application in the cell level is presented (P. Julkunen et al.). Practical considerations in modeling applications and model limitations are further discussed. A thorough review of recent advances in computational modeling of human knee joints is also presented (M. Kazemi et al.). Creation of knee joint models starting from imaging data (such as MRI) is summarized, clinical applications of the models are discussed, and examples are given for patient-specific evaluation of knee joint mechanics. Furthermore, influence of the altered depth-dependent properties of cartilage in osteoarthritis on fluid pressurization in a knee joint is demonstrated (Y. Dabiri and L. P. Li). Since generation of computational finite element models of knee joints is time-consuming and model simulation times can be relatively long, a semiautomated method is presented to study tibiofemoral contact stresses of adult subjects (D. D. Anderson et al.). Combination of experimental measurements and modeling is reviewed and model validation in cell, tissue, and joint level is discussed (P. Julkunen et al. and M. Kazemi et al.). Specifically, experimental results on the depth-dependent microstructural response of cartilage under directly loaded and unloaded regions are presented (A. Thambyah and N. Broom). Loading-related changes in chondron aspect ratios at different zones are also shown. These results can be used for the validation and development of computational models incorporating higher degrees of structural realism. Further, experimentally measured chondrocyte deformation behavior under mechanical loading of the tissue is presented for normal and osteoarthritic cartilage (P. Tanska et al.). Computational multiscale modeling is applied to explain differences in the cell behavior between the aforementioned groups. Finally, potential future directions in the application of multiscale models for the evaluation of cell responses and disease progression in knee joints are addressed (P. Julkunen et al.). Recent advances in the development of computational models of articular cartilage, as partially demonstrated in this special issue, and models of joints with realistic patient-specific material descriptions and joint kinematics have brought clinical application closer. More advanced adaptation models, likely combinations of models addressing different length scales, should ultimately lead to the prediction of altered tissue properties and wear, which strongly correlate to the development of osteoarthritis. Ultimately, computational modeling may become a clinical tool for identifying or optimizing patient-specific treatment strategies, such as rehabilitation and surgical interventions. Rami K. Korhonen Petro Julkunen LePing Li Corrinus C. van Donkelaar

Knowledge in cardiac development, heart diseaseand drug-induced toxicity has steadily progressed for centuries, but the most recent decades have seen an explosion in technological advancements that have benefited cardiac research. In particular, the development of induced pluripotent stem cells (iPSCs) derived from accessible human adult tissues, as well as lineage-specific cell cultures differentiated from these iPSCs, has led to the rapid growth of the iPSC-derived cardiomyocyte (iPSC-CM) as a promising in vitro model. However, major differences in iPSC-CM phenotype have been observed across studies. This variability maybe attributed to differences in cardiomyocyte differentiation protocols, maturation efficiency, or iPSC donor genetic background. While phenotypic heterogeneity is an important aspect of modelling a population as diverse as humans, it can also confound research study interpretation and reproducibility. Computational models of iPSC-CM physiology provide a potential avenue for assessing the mechanisms behind varied phenotypes and responses without sacrificing the valuable information this heterogeneity provides. Recently, new developments in the calibration of mechanistic models have aided in the generation of patient- or cell line-specific computational models, which hold potential in benchmarking iPSC-CM preparations. In this review, we summarize recent literature on iPSC-CM heterogeneity and computational model calibration, and we emphasize the utility of integrating computational ('dry lab') models with information from experimental ('wet lab') datasets in future iPSC-CM studies.

Bioprosthetic aortic valve diameter and thickness are directly related to leaflet fluttering: Results from a combined experimental and computational modeling study

Passive oral drug absorption can be predicted more reliably by experimental than computational models—Fact or myth

During angiogenesis, endothelial cells expand the vasculature by migrating from existing blood vessels, proliferating and collectively organizing into new capillaries. In vitro and in vivo experimentation is instrumental for identifying the molecular players and cell behaviour that regulate angiogenesis. Alongside experimental work, computational and mathematical models of endothelial cell network formation have helped to analyse if the current molecular and cellular understanding of endothelial cell behaviour is sufficient to explain the formation of endothelial cell networks. As input, the models take (a subset of) the current knowledge or hypotheses of single cell behaviour and capture it into a dynamical, mathematical description. As output, they predict the multicellular behaviour following from the actions of many individual cells, i.e., formation of a vascular-like network. Paradoxically, computational modelling based on different assumptions, i.e., completely different, sometimes non-intersecting sets of observed single cell behaviour, can reproduce the same angiogenesis-like multicellular behaviour, making it practically impossible to decide which, if any, of these models is correct. Here we present dynamical analyses of time-lapses of in vitro endothelial cell network formation experiments and compare these with dynamic analyses of three mathematical models: (1) the cell elongation model; (2) the contact-inhibited chemotaxis model; and (3) the mechanical cell-cell communication model. We extract a variety of dynamical characteristics of endothelial cell network formation using a custom time-lapse video analysis pipeline in ImageJ. We compare the dynamical network characteristics of the in vitro experiments to those of the cellular networks produced by the computational models. We test the response of the in silico dynamical cell network characteristics to changes in cell density and make related changes in the in vitro experiments. Of the three computational models that we have considered, the cell elongation model best captures the remodelling phase of in vitro endothelial cell network formation. Furthermore, in the in vitro model, the final size and number of lacunae in the network are independent of the initial cell density. This observation is also reproduced in the cell elongation model, but not in the other two models that we have considered. Altogether, we present an approach to model validation based on comparisons of time-resolved data and variations of model conditions.

This study investigated current distribution in the female abdomen using experimental and computer models of magnetic stimulation for treatment of urinary incontinence. Four experimental models and two computer models were used to simulate current distributions. In the performed computations of eddy current distribution, 18 cross-sectional images (interslice gap, 10 mm; slice numbers 750-920) of a female pelvic region from the NPAC/OLDA Visible Human Viewer were used. The influence of tissue inhomogeneity on current distribution were investigated using experimental models. In the region of pelvic floor muscles at depths of 20 mm and 50 mm from the experimental model, analysis of eddy current distribution could not be performed as a homogeneous conductor due to large differences in angles and summation of currents between Models I and IV. Within the region of the bladder and other organs at a depth of 100 mm from the experimental model, the pelvis and other tissues exerted little influence on current distribution.