Mechanical stimulation is essential in tissue engineering and regenerative medicine for proper tissue maturation. However, conventional uniaxial platforms fail to reproduce the multiaxial loading experienced in vivo. In this study, we present a humanoid robotic bioreactor capable of delivering human-like shoulder motions to engineered tendon constructs, enabling controlled multiaxial stimulation with real-time strain monitoring. Human mesenchymal stem cells were cultured on decellularized tendon scaffolds and subjected to adduction-abduction loading at peak strains of approximately 3.5% and 9.5% under external forces of 25 and 50 N, respectively. Strain levels were directly quantified in situ using a flexible sensor integrated within the bioreactor. The transparent bioreactor membrane allowed noninvasive observation while simultaneously applying mechanical stimulation over 14 d, with continuous assessment of cellular morphology without fixation. Compared with static and traditional uniaxial controls, the robot motions enhance cell alignment and activation of mechanotransduction pathways while inducing notable gene and protein expression changes, particularly within the PI3K-Akt signaling pathway. Although dynamic loading resulted in a moderate reduction in cell viability, the transcriptional profile was consistent with mechanically driven phenotypic adaptation toward tenogenic-related programs rather than dominant signatures of acute cytotoxic damage. These findings demonstrate that replicating human-like multiaxial mechanics in vitro fundamentally alters cellular mechanosensing and may provide a mechanobiological foundation for the future development of more physiologically relevant tendon grafts.

Abstract For many modern design problems, e.g., in clinical device design, evaluating designs prior to experimental testing typically requires high-fidelity multiphysics models that may involve fluid–structure interaction (FSI), multiple spatial and temporal scales, and dependencies on open systems. Such simulations can take hours or days, making them impractical for incorporation into computational optimization protocols. Surrogate models that provide rapid assessments are therefore essential. Traditional data-driven surrogates are fast but offer limited interpretability, while reduced-order models capture physical mechanisms but depend on simplifying assumptions and prior knowledge. Mechanistic learning (MxL) addresses these limitations by combining machine learning with reduced-order modeling to create a surrogate that retains mechanistic insight while achieving high computational efficiency. Here, we propose to integrate MxL-based surrogates into a computational optimization protocol. We showcase this methodology in the design of hydrocephalus shunt systems. Starting from a 3D finite element FSI model, we motivate the use of a fast surrogate model in which fluid stresses on the FSI boundary are combined with machine learning techniques to predict solid deformation. The resulting MxL surrogate is integrated into a hybrid optimization algorithm, reducing evaluation time from 3 days to 10 min and enabling tractable optimization of a high-dimensional design space. The resulting designs are ultimately tested in the full FSI model to confirm their improved efficacy. This methodology is applicable to engineering design problems characterized by computationally expensive multiphysics models, particularly those involving dominant FSI effects.

A novel methodology, enhanced by artificial intelligence (AI), to achieve zero emission building (ZEB) designs is being developed within the European ZEBAI project. The methodology will integrate all interdependent analyses and partial decision-making processes into a holistic approach that simultaneously assesses the building’s energy performance, environmental impact, indoor environmental quality and economic costs. The results of a building energy simulation are significantly influenced by the accuracy of the heat and moisture transfer calculations of the individual components of the building envelope. Consequently, a comprehensive and up-to-date database of building material properties is required to obtain reliable simulation results. However, most databases on the market do not include parameters essential for accurate energy calculations, such as optical properties (solar and visible absorption, reflectance and transmittance), colour coordinates and thermal emittance of envelope materials. Furthermore, the behaviour of these properties with changes in temperature and humidity, as well as the coupled mechanical and thermal properties of building materials, are rarely considered. As a result, engineers/technicians are forced to adopt standardised values that may differ significantly from the true values, increasing the discrepancy between simulated and measured energy performance of buildings. This work presents an overview of the existing databases at European level containing information on the mechanical, optical and thermal properties of building materials. From their analysis, the requirements for the development of a new catalogue of materials suitable for AI-enhanced design based on machine learning and heuristic optimisation of ZEBs are established, i.e. a characterisation plan for materials and systems, the appropriate conditions for processing the characterisation data and collecting the results, and the proposed format for the final catalogue.

Effective non-invasive detection and tracking of disease progression remains a challenge in medical research. This is particularly critical for brain-related conditions such as Chiari malformations, hydrocephalus or neurodegenerative disorders, as an early detection can significantly improve patient outcomes. One promising approach looks to identify changes in the patterns of brain movement during a cardiac cycle, using aMRI or ultrasound, as a diagnostic proxy for a given condition. During the cardiac cycle, pressure variations in the blood vessels and cerebrospinal fluid (CSF) ventricular system cause motion and deformation of brain tissues. Microstructural changes in the brain due to malformations or disease progression can alter deformation patterns. In this work, we present a computational model to examine brain deformation during the cardiac cycle, with the goal of detecting the progression of underlying diseases from altered brain motion. Our approach uses finite element (FE) simulations to model brain motion in a biologically realistic head model, incorporating deformable brain tissue and CSF, and applying a pressure pulse through the arterial vasculature. We illustrate the use of our model in the context of a Chiari-type I malformation (CM-I), and demonstrate that our model can capture differences in the deformation patterns between healthy and CM-I brains, in agreement with recent aMRI data. While applied here to CM-I, our model can be used as a digital twin for a variety of brain disease evolution whereby changes in microstructure are expected to lead to different brain pulsation patterns.

Traditionally, clinical devices are designed, tested and improved through lengthy and expensive laboratory experiments and clinical trials [1]. More recently, computational methods have allowed for rapid testing, speeding up the design process and enabling far more complete searches of design space. While computational models cannot fully capture the complexities of biological systems, they provide valuable insights into crucial underlying mechanisms, such as the effects of fluid-structure interactions (FSIs). In this paper we present a modular, partitioned, computational FSI pipeline whereby 2D reduced order models guide the 3D design of the problem of interest. This framework is applied to the problem of hydrocephalus shunt occlusion. Hydrocephalus is a medical condition characterised by an excess of cerebrospinal fluid (CSF) in the brain, and is commonly treated with the insertion of a shunt system. This system includes a ventricular catheter component - a hollow tube with inlet holes arranged in the tube wall close to the closed tip - which is positioned in the lateral ventricles of the brain. Despite recent improvements in the catheter material, this treatment still has high failure rates, most often due to the blockage of the catheter by the Choroid Plexus (ChP) tissue. We use an idealised FSI model to compare existing catheter designs by considering the deformation of the ChP under CSF flow in the ventricle environment in an hydrocephalus scenario. To the best of our knowledge, this is the first computational framework to directly incorporate the deformation of the ChP to discriminate between catheter designs. The faster 2D model is used in a comprehensive parameter sweep of the catheter design domain, and motivates a new design, then confirmed to be an improvement when tested in the full 3D domain. This approach demonstrates the success of using reduced order methods to guide the design of a more complex problem.

Lattices consisting of slender elements lend themselves particularly well to the design of engineering metamaterials with tailored properties, in particular in the context of additive manufacturing. This paper explores the use of the Soft Finite Element Method (SoftFEM), a recently developed optimisation framework making use of finite element simulations, for the design of lattice-based metamaterials. As a general black-box optimisation framework for engineering applications, SoftFEM is a powerful tool for identifying optimum configurations, combining a population-based evolutionary global optimisation and a local search technique to simultaneously and automatically (i.e., without a priori knowledge) leverage exploration and exploitation, respectively. When applying SoftFEM to lattice-built structure optimisation, the optimised parameters are defined by the length scale characteristics of its slender elements, i.e., length and cross-sectional area, along with a corresponding threshold below which the slender element is removed from the lattice by being “deactivated”. During optimisation iterations, this threshold parameter is allowed to vary, in turn increasing the possibility for deactivated elements to be activated again, resulting in a more flexible and efficient exploration in search of the optimised configurations. The capability of the proposed framework is demonstrated here with design examples of lattices with tailored properties and its superiority is demonstrated against published results. Finally, a new method is proposed whereby the optimisation procedure is done on a smaller stackable lattice, allowing for large scale deployment at the macroscale.

Objective:The monitoring and diagnosis of sports-related mild traumatic brain injury (SR-mTBI) remains a challenge. This systematic review summarises the current monitoring tools used for on-the-day assessment and diagnosis of SR-mTBI and their performance. Design:Systematic review, using Quality Assessment of Diagnostic Accuracy Studies assessment. Data sources:Embase via Ovid, IEEEXplore, Medline via Ovid, Scopus and Web of Science were searched up to June 2024. Eligibility criteria:Peer-reviewed English-language journal articles which measured athletes using the index test within a day of injury and provided a performance measure for the method used. Studies of all designs were accepted, and no reference methods were required. Results:2534 unique records were retrieved, with 52 reports included in the review. Participants were 76% male, when reported, and the mean injury-to-measurement time was reported in 10% of reports. 46 different methods were investigated. 38 different reference methods were used, highlighting the lack of gold standard within the field. Area under the curve (AUC), sensitivity and specificity were the most frequent outcome metrics provided. The most frequent index test was the King-Devick (KD) test. However, there were large variations in accuracy metrics between reports for the KD test, for instance, the range of AUC: 0.51-0.92. Conclusion:Combinations of existing methods and the KD test were most accurate in assessing SR-mTBI, despite the inconsistent accuracy values related to the KD test. The absence of a gold-standard measurement hampers our ability to diagnose or monitor SR-mTBI. Further exploration of the mechanisms and time-dependent pathophysiology of SR-mTBI could result in more targeted diagnostic and monitoring techniques. The Podium Institute for Sports Medicine and Technology funded this work. PROSPERO registration number:CRD42022376560.

Police forensic investigations are not immune to our society's ubiquitous search for better predictive ability. In the particular and very topical case of Traumatic Brain Injury (TBI), police forensic investigations aim at evaluating whether a given impact or assault scenario led to the clinically observed TBI. This question is traditionally answered by means of forensic biomechanics and neurosurgical expertise which cannot provide a fully objective probabilistic measure. To this end, we propose here a numerical framework-based solution coupling biomechanical simulations of a variety of injurious impacts to machine learning training of police reports provided by the UK's Thames Valley Police and the National Crime Agency's National Injury Database. In this approach, the biomechanical predictions of mechanical metrics such as strain and stress distributions are interpreted by the machine learning model by additionally considering assault specific metadata to predict brain injury outcomes. The framework, only taking as input information typically available in police reports, reaches prediction accuracies exceeding 94% for skull fracture, 79% for loss of consciousness and intracranial haemorrhage, and is able to identify the best predictive features for each targeted injury. Overall, the proposed framework offers new avenues for the prediction, directly from police reports, of any TBI related symptom as required by forensic law enforcement investigations.

We recently proposed an efficient method facilitating the parametric study of a finite element mechanical simulation as a postprocessing step, that is, without the need to run multiple simulations: the a posteriori finite element method (APFEM). APFEM only requires the knowledge of the vertices of the parameter space and is able to predict accurately how the degrees of freedom of a simulation, i.e., nodal displacements, and other outputs of interests, for example, element stress tensors, evolve when simulation parameters vary within their predefined ranges. In our previous work, these parameters were restricted to material properties and loading conditions. Here, we extend the APFEM to additionally account for changes in the original geometry. This is achieved by defining an intermediary reference frame whose mapping is defined stochastically in the weak form. Subsequent deformation is then reached by correcting for this stochastic variation in the reference frame through multiplicative decomposition of the deformation gradient tensor. The resulting framework is shown here to provide accurate mechanical predictions for relevant applications of increasing complexity: (i) quantifying the stress concentration factor of a plate under uniaxial loading with one and two elliptical holes of varying eccentricities, and (ii) performing the stochastic homogenisation of a composite plate with uncertain mechanical properties and geometry inclusion. This extension of APFEM completes our original approach to account parametrically for geometrical alterations, in addition to boundary conditions and material properties. The advantages of this approach in our original work in terms of stochastic prediction, uncertainty quantification, structural and material optimisation and Bayesian inferences are all naturally conserved.

During vaginal delivery, the delivery requires the fetal head to mold to accommodate the geometric constraints of the birth canal. Excessive molding can produce brain injuries and long-term sequelae. Understanding the loading of the fetal brain during the second stage of labor (fully dilated cervix, active pushing, and expulsion of fetus) could thus help predict the safety of the newborn during vaginal delivery. To this end, this study proposes a finite element model of the fetal head and maternal canal environment that is capable of predicting the stresses experienced by the fetal brain at the onset of the second phase of labor. Both fetal and maternal models were adapted from existing studies to represent the geometry of full-term pregnancy. Two fetal positions were compared: left-occiput-anterior and left-occiput-posterior. The results demonstrate that left-occiput-anterior position reduces the maternal tissue deformation, at the cost of higher stress in the fetal brain. In both cases, stress is concentrated underneath the sutures, though the location varies depending on the presentation. In summary, this study provides a patient-specific simulation platform for the study of vaginal delivery and its effect on both the fetal brain and maternal anatomy. Finally, it is suggested that such an approach has the potential to be used by obstetricians to support their decision-making processes through the simulation of various delivery scenarios.

The soft finite element method (SoftFEM) was recently developed as an efficient black-box engineering optimization framework combining population-based evolutionary global optimization and a local search technique using a hybrid self-adaptive strategy. This framework simultaneously leverages exploration and exploitation without the need to know a priori which strategy is best fitted to the problem at hand. In this work, we first improve SoftFEM by replacing the original differential evolution by the so-called success-history-based adaptive differential evolution. The developed framework is then applied to optimize the aerodynamic performance of 1) an airfoil, 2) a wing, and 3) the fluid-structure interaction problem of an airfoil with a flexible trailing edge. In the first two cases, the open-source computational fluid dynamics code OpenFOAM is considered to evaluate the aerodynamic characteristics of the airfoil, while in the latter case, we combine OpenFOAM and MuPhiSim-our recently released open-source code for computational solids mechanics-to conduct fluid-structure interaction simulations. The results demonstrate that this approach can effectively find optimal solutions with improved aerodynamic performance in all three cases. The proposed framework has the potential to significantly impact the design and optimization of aerodynamic systems in complex design spaces. In particular, new systems involving soft components and soft-hard junctions would benefit from a seamless application of the developed framework to their optimization.

We propose a robust framework for quantitatively comparing model-predicted and experimentally measured strain fields in the human brain during harmonic skull motion. Traumatic brain injuries (TBIs) are typically caused by skull impact or acceleration, but how skull motion leads to brain deformation and consequent neural injury remains unclear and comparison of model predictions to experimental data remains limited. Magnetic resonance elastography (MRE) provides high-resolution, full-field measurements of dynamic brain deformation induced by harmonic skull motion. In the proposed framework, full-field strain measurements from human brain MRE in vivo are compared to simulated strain fields from models with similar harmonic loading. To enable comparison, the model geometry and subject anatomy, and subsequently, the predicted and measured strain fields are nonlinearly registered to the same standard brain atlas. Strain field correlations ( C_v ), both global (over the brain volume) and local (over smaller sub-volumes), are then computed from the inner product of the complex-valued strain tensors from model and experiment at each voxel. To demonstrate our approach, we compare strain fields from MRE in six human subjects to predictions from two previously developed models. Notably, global C_v values are higher when comparing strain fields from different subjects ( C_v 0.6–0.7) than when comparing strain fields from either of the two models to strain fields in any subject. The proposed framework provides a quantitative method to assess similarity (and to identify discrepancies) between model predictions and experimental measurements of brain deformation and thus can aid in the development and evaluation of improved models of brain biomechanics.

Stochastic methods have recently been the subject of increased attention in Computational Mechanics for their ability to account for the stochasticity of both material parameters and geometrical features in their predictions. Among them, the Galerkin Stochastic Finite Element Method (GSFEM) was shown to be particularly efficient and able to provide accurate output statistics, although at the cost of intrusive coding and additional theoretical algebraic efforts. In this method, distributions of the stochastic parameters are used as inputs for the solver, which in turn outputs nodal displacement distributions in one simulation. Here, we propose an extension of the GSFEM—termed the A posteriori Finite Element Method or APFEM—where uniform distributions are taken by default to allow for parametric studies of the inputs of interest as a postprocessing step after the simulation. Doing so, APFEM only requires the knowledge of the vertices of the parameter space. In particular, one key advantage of APFEM is its use in the context of Bayesian inferences, where the random evaluations required by the Bayesian setting (usually done through Monte Carlo) can be done exactly without the need for further simulations. Finally, we demonstrate the potential of APFEM by solving forward models with parametric boundary conditions in the context of (i) metamaterial design and (ii) pitchfork bifurcation of the buckling of a slender structure; and demonstrate the flexibility of its use for Bayesian inference by (iii) inferring friction coefficient of a half plane in a contact mechanics problem and (iv) inferring the stiffness of a brain region in the context of cancer surgical planning.

General anaesthetics are widely used for their analgesic, immobilising, and hypnotic effects. The mechanisms underlying these effects remain unclear, but likely arise from alterations to cell microstructure, and potentially mechanics. Here we investigate this hypothesis using a custom experimental setup combining calcium imaging and nanoindentation to quantify the firing activity and mechanical properties of dorsal root ganglion-derived neurons exposed to a clinical concentration of 1% isoflurane gas, a halogenated ether commonly used in general anaesthesia. We found that cell viscoelasticity and functional activity are simultaneously and dynamically altered by isoflurane at different stages of exposure. Particularly, cell firing count correlated linearly with the neuronal loss tangent, the ratio of mechanical energy dissipation and storage by the cell. Our results demonstrate that anaesthetics affect cells as a whole, reconciling seemingly contradictory theories of how anaesthetics operate, and highlight the importance of considering cell mechanics in neuronal functions, anaesthesia, and clinical neuroscience in general.

The Finite Element Method (FEM) suffers from important drawbacks in problems involving excessive deformation of elements despite being universally applied to a wide range of engineering applications. While dynamic remeshing is often offered as the ideal solution, its computational cost, numerical noise and mathematical limitations in complex geometries are impeding its widespread use. Meshless methods (MM), however, by not relying on mesh connectivity, circumvent some of these limitations, while remaining computationally more expensive than the classic FEM. These problems in MM can be improved by coupling with FEM in a FEM-MM scheme, in which MM is used within sensitive regions that undergo large deformations while retaining the more efficient FEM for other less distorted regions. Here, we present a numerical framework combining the benefits of FEM and MM to study large deformation scenarios without heavily compromising on computational efficiency. In particular, the latter is maintained through two mechanisms: (1) coupling of FEM and MM discretisation schemes within one problem, which limits MM discretisation to domains that cannot be accurately modelled in FEM, and (2) a simplified MM parallelisation approach which allows for highly efficient speed-up. The proposed approach treats the problem as a quadrature point driven problem, thus making the treatment of the constitutive models, and thus the matrix and vector assembly fully method-agnostic. The MM scheme considers the maximum entropy (max-ent) approximation, in which its weak Kronecker delta property is leveraged in parallel calculations by convexifying the subdomains, and by refining meshes at the boundary in such a way that the higher density of nodes is mainly concentrated within the bulk of the domain. The latter ensures obtaining the Kronecker delta property at the boundary of the MM domain. The results, demonstrated by means of a few applications, show an excellent scalability and a good balance between accuracy and computational cost.

Background: The consideration of neurons as coupled mechanical-electrophysiological systems is supported by a growing body of experimental evidence, including observations that cell membranes mechanically deform during the propagation of an action potential. However, the short-term (seconds to minutes) influence of membrane voltage on the mechanical properties of a neuron at the single-cell level remains unknown.Materials and Methods: Here, we use microscale dynamic mechanical analysis to demonstrate that changes in membrane potential induce changes in the mechanical properties of individual neurons. We simultaneously measured the membrane potential and mechanical properties of individual neurons through a multiphysics single-cell setup. Membrane voltage of a single neuron was measured through whole-cell patch clamp. The mechanical properties of the same neuron were measured through a nanoindenter, which applied a dynamic indentation to the neuron at different frequencies.Results: Neuronal storage and loss moduli were lower for positive voltages than negative voltages.Conclusion: The observed effects of membrane voltage on neuron mechanics could be due to piezoelectric or flexoelectric effects and altered ion distributions under the applied voltage. Such effects could change cell mechanics by changing the intermolecular interactions between ions and the various biomolecules within the membrane and cytoskeleton.

Several animal and human studies have now established the potential of low intensity, low frequency transcranial ultrasound (TUS) for non-invasive neuromodulation. Paradoxically, the underlying mechanisms through which TUS neuromodulation operates are still unclear, and a consensus on the identification of optimal sonication parameters still remains elusive. One emerging hypothesis based on thermodynamical considerations attributes the acoustic-induced nerve activity alterations to the mechanical energy and/or entropy conversions occurring during TUS action. Here, we propose a multiscale modelling framework to examine the energy states of neuromodulation under TUS. First, macroscopic tissue-level acoustic simulations of the sonication of a whole monkey brain are conducted under different sonication protocols. For each one of them, mechanical loading conditions of the received waves in the anterior cingulate cortex region are recorded and exported into a microscopic cell-level 3D viscoelastic finite element model of a neuronal axon embedded in extracellular medium. Pulse-averaged elastically stored and viscously dissipated energy rate densities during axon deformation are finally computed under different sonication incident angles and are mapped against distinct combinations of sonication parameters of the TUS. The proposed multiscale framework allows for the analysis of vibrational patterns of the axons and its comparison against the spectrograms of stimulating ultrasound. The results are in agreement with literature data on neuromodulation, demonstrating the potential of this framework to identify optimised acoustic parameters in TUS neuromodulation. The proposed approach is finally discussed in the context of multiphysics energetic considerations, argued here to be a promising avenue towards a scalable framework for TUS in silico predictions. STATEMENT OF SIGNIFICANCE: Low-intensity transcranial ultrasound (TUS) is poised to become a leading neuromodulation technique for the treatment of neurological disorders. Paradoxically, how it operates at the cellular scale remains unknown, hampering progress in personalised treatment. To this end, models of the multiphysics of neurons able to upscale results to the organ scale are required. We propose here to achieve this by considering an axon submitted to an ultrasound wave extracted from a simulation at the organ scale. Doing so, information pertaining to both stored and dissipated axonal energies can be extracted for a given head/brain morphology. This two-scale multiphysics energetic approach is a promising scalable framework for in silico predictions in the context of personalised TUS treatment.

The Monte Carlo method is widely used for the estimation of uncertainties in mechanical engineering design. However, while flexible, this method remains impractical in terms of computational time and scalability. To bypass these limitations, other more efficient approaches such as the Galerkin stochastic finite element method (GSFEM) or the collocation method have been proposed. GSFEM provides accurate output statistics, has the advantage of being sampling independent and can be modular in terms of operations, albeit code intrusive. While linear elasticity has been extensively covered in the literature, the application of GSFEM to nonlinear mechanical behaviour remains relatively unexplored, in part due to the difficulty to capture nonlinear effects with the traditional GSFEM interpolants. To this end, we propose a seamless and efficient modular framework avoiding the need to know a priori the material law. In particular, the method makes use of a wavelet based formulation able to capture simultaneously continuous and discontinuous behaviours, and stochastic operators are proposed to straightforwardly adapt any material model. Finally, the flexibility of this approach is illustrated with two problems: (i) a 3D hyperelastic example with nonlinear behaviour arising from buckling uncertainty, and (ii) the evaluation of the displacement of a point within a structure based on how much of the external accessible structural deformation is known.

Low-intensity transcranial ultrasound stimulation (TUS) is poised to become one of the most promising treatments for neurological disorders. However, while recent animal model experiments have successfully quantified the alterations of the functional activity coupling between a sonicated target cortical region and other cortical regions of interest (ROIs), the varying degree of alteration between these different connections remains unexplained. We hypothesise here that the incidental sonication of the tracts leaving the target region towards the different ROIs could participate in explaining these differences. To this end, we propose a tissue level phenomenological numerical model of the coupling between the ultrasound waves and the white matter electrical activity. The model is then used to reproduce in silico the sonication of the anterior cingulate cortex (ACC) of a macaque monkey and measure the neuromodulation power within the white matter tracts leaving the ACC for five cortical ROIs. The results show that the more induced power a white matter tract proximal to the ACC and connected to a secondary ROI receives, the more altered the connectivity fingerprint of the ACC to this region will be after sonication. These results point towards the need to isolate the sonication to the cortical region and minimise the spillage on the neighbouring tracts when aiming at modulating the target region without losing the functional connectivity with other ROIs. Those results further emphasise the potential role of the white matter in TUS and the need to account for white matter topology when designing TUS protocols.