In this study, coupled hemodynamic–acoustic simulations are employed to study the generation and propagation of murmurs associated with aortic stenoses where the aorta with a stenosed aortic valve is modeled as a curved pipe with a constriction near the inlet. The hemodynamics of the poststenotic flow is investigated in detail in our previous numerical study (Zhu et al., 2018, “Computational Modelling and Analysis of Haemodynamics in a Simple Model of Aortic Stenosis,” J. Fluid Mech., 851 , pp. 23–49). The temporal history of the pressure on the aortic lumen is recorded during the hemodynamic study and used as the murmur source in the acoustic simulations. The thorax is modeled as an elliptic cylinder and the thoracic tissue is assumed to be homogeneous, linear and viscoelastic. A previously developed high-order numerical method that is capable of dealing with immersed bodies is applied in the acoustic simulations. To mimic the clinical practice of auscultation, the sound signals from the epidermal surface are collected. The simulations show that the source of the aortic stenosis murmur is located at the proximal end of the aortic arch and that the sound intensity pattern on the epidermal surface can predict the source location of the murmurs reasonably well. Spectral analysis of the murmur reveals the disconnect between the break frequency obtained from the flow and from the murmur signal. Finally, it is also demonstrated that the source locations can also be predicted by solving an inverse problem using the free-space Green's function. The implications of these results for cardiac auscultation are discussed.

This study aims to estimate the control law employed by the central nervous system (CNS) to keep a person in balance after a sudden disturbance. For this aim, several experiments were carried out, in which the subjects were perturbed sagittally by using a single-axis tilt-platform and their motions were recorded with appropriate sensors. The analysis of the experimental results leads to the contribution of this paper as a conjecture that the CNS commands the muscular actuators of the joints according to an adaptive proportional-derivative (PD) control law such that its gains and set points are updated continuously. This conjecture is accompanied with an assumption that the CNS is able to acquire perfect and almost instantaneous position and velocity feedback by means of a fusion of the signals coming from the proprioceptive, somatosensory, and vestibular systems. In order to verify the conjectured control law, an approximate biomechanical model was developed and several simulations were carried out to imitate the experimentally observed motions. The time variations of the set points and the control gains were estimated out of the experimental data. The simulated motions were observed to be considerably close to the experimental motions. Thus, the conjectured control law is validated. However, the experiments also indicate that the mentioned adaptation scheme is quite variable even for the same subject tested repeatedly with the same perturbation. In other words, this experimental study also leads to the implication that the way the CNS updates the control parameters is not quite predictable.

Methods that effectively assess and train dynamic seated balance are critical for enhancing functional independence and reducing risk of secondary health complications in the elderly and individuals with neuromuscular impairments. The objective of this research was to devise and validate a portable tool for assessing and training dynamic seated balance. An instrumented wobble board was designed and constructed that (1) elicits multidirectional perturbations in seated individuals, (2) quantifies seated balance proficiency, and (3) provides real-time, kinematics-based vibrotactile feedback. After performing a technical validation study to compare kinematic wobble board measurements against a gold-standard motion capture system, 15 nondisabled participants performed a dynamic sitting task using the wobble board. Our results demonstrate that the tilt angle measurements were highly accurate throughout the range of wobble board dynamics. Furthermore, the posturographic analyses for the dynamic sitting task revealed that the wobble board can effectively discriminate between the different conditions of perturbed balance, demonstrating its potential to serve as a clinical tool for the assessment and training of seated balance. Vibrotactile feedback decreased the variance of wobble board tilt, demonstrating its potential for use as a balance training tool. Unlike similar instrumented tools, the wobble board is portable, requires no laboratory equipment, and can be adjusted to meet the user's balance abilities. While future work is warranted, obtained findings will aid in effective translation of assessment and training techniques to a clinical setting, which has the potential to enhance the diagnosis and prognosis for individuals with seated balance impairments.

Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called “muscle excitations”), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called “included” muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called “excluded” muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called “synergy extrapolation”). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.

Quantized dynamical entropy (QDE) has recently been proposed as a new measure to quantify the complexity of dynamical systems with the purpose of offering a better computational efficiency. This paper further investigates the viability of this method using five different human gait signals. These signals are recorded while normal walking and while performing secondary tasks among two age groups (young and older age groups). The results are compared with the outcomes of previously established sample entropy (SampEn) measure for the same signals. We also study how analyzing segmented and spatially and temporally normalized signal differs from analyzing whole data. Our findings show that human gait signals become more complex as people age and while they are cognitively loaded. Center of pressure (COP) displacement in mediolateral direction is the best signal for showing the gait changes. Moreover, the results suggest that by segmenting data, more information about intrastride dynamical features are obtained. Most importantly, QDE is shown to be a reliable measure for human gait complexity analysis.

Bone development, maintenance, and regeneration are remarkably sensitive to mechanical cues. Consequently, mechanical stimulation has long been sought as a putative target to promote endogenous healing after fracture. Given the transient nature of bone repair, tissue-level mechanical cues evolve rapidly over time after injury and are challenging to measure noninvasively. The objective of this work was to develop and characterize an implantable strain sensor for noninvasive monitoring of axial strain across a rodent femoral defect during functional activity. Herein, we present the design, characterization, and in vivo demonstration of the device’s capabilities for quantitatively interrogating physiological dynamic strains during bone regeneration. Ex vivo experimental characterization of the device showed that it possessed promising sensitivity, signal resolution, and electromechanical stability for in vivo applications. The digital telemetry minimized power consumption, enabling extended intermittent data collection. Devices were implanted in a rat 6 mm femoral segmental defect model, and after three days, data were acquired wirelessly during ambulation and synchronized to corresponding radiographic videos, validating the ability of the sensor to noninvasively measure strain in real-time. Together, these data indicate the sensor is a promising technology to quantify tissue mechanics in a specimen specific manner, facilitating more detailed investigations into the role of the mechanical environment in dynamic bone healing and remodeling processes.

A computational framework consisting of a one-way coupled hemodynamic–acoustic method and a wave-decomposition based postprocessing approach is developed to investigate the biomechanics of arterial bruits. This framework is then applied for studying the effect of the shear wave on the generation and propagation of bruits from a modeled stenosed artery. The blood flow in the artery is solved by an immersed boundary method (IBM) based incompressible flow solver. The sound generation and propagation in the blood volume are modeled by the linearized perturbed compressible equations, while the sound propagation through the surrounding tissue is modeled by the linear elastic wave equation. A decomposition method is employed to separate the acoustic signal into a compression/longitudinal component (curl free) and a shear/transverse component (divergence free), and the sound signals from cases with and without the shear modulus are monitored on the epidermal surface and are analyzed to reveal the influence of the shear wave. The results show that the compression wave dominates the detected sound signal in the immediate vicinity of the stenosis, whereas the shear wave has more influence on surface signals further downstream of the stenosis. The implications of these results on cardiac auscultation are discussed.

A novel application of phase-space warping (PSW) method to detect fatigue in the musculoskeletal system is presented. Experimental kinematic, force, and physiological signals are used to produce a fatigue metric. The metric is produced using time-delay embedding and PSW methods. The results showed that by using force and kinematic signals, an overall estimate of the muscle group state can be achieved. Further, when using electromyography (EMG) signals the fatigue metric can be used as a tool to evaluate muscles activation and load sharing patterns for individual muscles. The presented method will allow for fatigue evolution measurement outside a laboratory environment, which open doors to applications such as tracking the physical state of players during competition, workers in a plant, and patients undergoing in-home rehabilitation.

A triaxial force-sensitive microrobot was developed to dynamically perturb skin in multiple deformation modes, in vivo. Wiener static nonlinear identification was used to extract the linear dynamics and static nonlinearity of the force–displacement behavior of skin. Stochastic input forces were applied to the volar forearm and thenar eminence of the hand, producing probe tip perturbations in indentation and tangential extension. Wiener static nonlinear approaches reproduced the resulting displacements with variances accounted for (VAF) ranging 94–97%, indicating a good fit to the data. These approaches provided VAF improvements of 0.1–3.4% over linear models. Thenar eminence stiffness measures were approximately twice those measured on the forearm. Damping was shown to be significantly higher on the palm, whereas the perturbed mass typically was lower. Coefficients of variation (CVs) for nonlinear parameters were assessed within and across individuals. Individual CVs ranged from 2% to 11% for indentation and from 2% to 19% for extension. Stochastic perturbations with incrementally increasing mean amplitudes were applied to the same test areas. Differences between full-scale and incremental reduced-scale perturbations were investigated. Different incremental preloading schemes were investigated. However, no significant difference in parameters was found between different incremental preloading schemes. Incremental schemes provided depth-dependent estimates of stiffness and damping, ranging from 300 N/m and 2 Ns/m, respectively, at the surface to 5 kN/m and 50 Ns/m at greater depths. The device and techniques used in this research have potential applications in areas, such as evaluating skincare products, assessing skin hydration, or analyzing wound healing.

Strain gages are commonly used to measure bone strain, but only provide strain at a single location. Digital image correlation (DIC) is an optical technique that provides the displacement, and therefore strain, over an entire region of interest on the bone surface. This study compares vertebral body strains measured using strain gages and DIC. The anterior surfaces of 15 cadaveric porcine vertebrae were prepared with a strain rosette and a speckled paint pattern for DIC. The vertebrae were loaded in compression with a materials testing machine, and two high-resolution cameras were used to image the anterior surface of the bones. The mean noise levels for the strain rosette and DIC were 1 με and 24 με , respectively. Bland–Altman analysis was used to compare strain from the DIC and rosette (excluding 44% of trials with some evidence of strain rosette failure or debonding); the mean difference ± 2 standard deviations (SDs) was −108 με ± 702 με for the minimum (compressive) principal strain and −53 με ± 332 με for the maximum (tensile) principal strain. Although the DIC has higher noise, it avoids the relatively high risk we observed of strain gage debonding. These results can be used to develop guidelines for selecting a method to measure strain on bone.

Shear waves that propagate in soft solids, such as the brain, are strongly nonlinear and can develop into shock waves in less than one wavelength. We hypothesize that these shear shock waves could be responsible for certain types of traumatic brain injuries (TBI) and that the spherical geometry of the skull bone could focus shear waves deep in the brain, generating diffuse axonal injuries. Theoretical models and numerical methods that describe nonlinear polarized shear waves in soft solids such as the brain are presented. They include the cubic nonlinearities that are characteristic of soft solids and the specific types of nonclassical attenuation and dispersion observed in soft tissues and the brain. The numerical methods are validated with analytical solutions, where possible, and with self-similar scaling laws where no known solutions exist. Initial conditions based on a human head X-ray microtomography (CT) were used to simulate focused shear shock waves in the brain. Three regimes are investigated with shock wave formation distances of 2.54 m , 0.018 m , and 0.0064 m . We demonstrate that under realistic loading scenarios, with nonlinear properties consistent with measurements in the brain, and when the shock wave propagation distance and focal distance coincide, nonlinear propagation can easily overcome attenuation to generate shear shocks deep inside the brain. Due to these effects, the accelerations in the focal are larger by a factor of 15 compared to acceleration at the skull surface. These results suggest that shock wave focusing could be responsible for diffuse axonal injuries.

Cells are highly dynamic and mechanical automata powered by molecular motors that respond to external cues. Intracellular signaling pathways, either chemical or mechanical, can be activated and spatially coordinated to induce polarized cell states and directional migration. Physiologically, cells navigate through complex microenvironments, typically in three-dimensional (3D) fibrillar networks. In diseases, such as metastatic cancer, they invade across physiological barriers and remodel their local environments through force, matrix degradation, synthesis, and reorganization. Important external factors such as dimensionality, confinement, topographical cues, stiffness, and flow impact the behavior of migrating cells and can each regulate motility. Here, we review recent progress in our understanding of single-cell migration in complex microenvironments.

Excessive loading of ligaments can activate the neural afferents that innervate the collagenous tissue, leading to a host of pathologies including pain. An integrated experimental and modeling approach was used to define the responses of neurons and the surrounding collagen fibers to the ligamentous matrix loading and to begin to understand how macroscopic deformation is translated to neuronal loading and signaling. A neuron-collagen construct (NCC) developed to mimic innervation of collagenous tissue underwent tension to strains simulating nonpainful (8%) or painful ligament loading (16%). Both neuronal phosphorylation of extracellular signal-regulated kinase (ERK), which is related to neuroplasticity (R 2 ≥ 0.041; p ≤ 0.0171) and neuronal aspect ratio (AR) (R 2 ≥ 0.250; p < 0.0001), were significantly correlated with tissue-level strains. As NCC strains increased during a slowly applied loading (1%/s), a “switchlike” fiber realignment response was detected with collagen reorganization occurring only above a transition point of 11.3% strain. A finite-element based discrete fiber network (DFN) model predicted that at bulk strains above the transition point, heterogeneous fiber strains were both tensile and compressive and increased, with strains in some fibers along the loading direction exceeding the applied bulk strain. The transition point identified for changes in collagen fiber realignment was consistent with the measured strain threshold (11.7% with a 95% confidence interval of 10.2–13.4%) for elevating ERK phosphorylation after loading. As with collagen fiber realignment, the greatest degree of neuronal reorientation toward the loading direction was observed at the NCC distraction corresponding to painful loading. Because activation of neuronal ERK occurred only at strains that produced evident collagen fiber realignment, findings suggest that tissue strain-induced changes in the micromechanical environment, especially altered local collagen fiber kinematics, may be associated with mechanotransduction signaling in neurons.

Sports-related concussions are of significant concern in many impact sports, and their detection relies on accurate measurements of the head kinematics during impact. Among the most prevalent recording technologies are videography, and more recently, the use of single-axis accelerometers mounted in a helmet, such as the HIT system. Successful extraction of the linear and angular impact accelerations depends on an accurate analysis methodology governed by the equations of motion. Current algorithms are able to estimate the magnitude of acceleration and hit location, but make assumptions about the hit orientation and are often limited in the position and/or orientation of the accelerometers. The newly formulated algorithm presented in this manuscript accurately extracts the full linear and rotational acceleration vectors from a broad arrangement of six single-axis accelerometers directly from the governing set of kinematic equations. The new formulation linearizes the nonlinear centripetal acceleration term with a finite-difference approximation and provides a fast and accurate solution for all six components of acceleration over long time periods (>250 ms). The approximation of the nonlinear centripetal acceleration term provides an accurate computation of the rotational velocity as a function of time and allows for reconstruction of a multiple-impact signal. Furthermore, the algorithm determines the impact location and orientation and can distinguish between glancing, high rotational velocity impacts, or direct impacts through the center of mass. Results are shown for ten simulated impact locations on a headform geometry computed with three different accelerometer configurations in varying degrees of signal noise. Since the algorithm does not require simplifications of the actual impacted geometry, the impact vector, or a specific arrangement of accelerometer orientations, it can be easily applied to many impact investigations in which accurate kinematics need to be extracted from single-axis accelerometer data.

Cell migration is a highly regulated and complex cellular process to maintain proper homeostasis for various biological processes. Extracellular environment was identified as the main affecting factors determining the direction of cell crawling. It was observed experimentally that the cell prefers migrating to the area with denser or stiffer array of microposts. In this article, an integrated unidirectional cell crawling model was developed to investigate the spatiotemporal dynamics of unidirectional cell migration, which incorporates the dominating intracellular biochemical processes, biomechanical processes and the properties of extracellular micropost arrays. The interpost spacing and the stiffness of microposts are taken into account, respectively, to study the mechanism of unidirectional cell locomotion and the guidance of extracellular influence cues on the direction of unidirectional cell crawling. The model can explain adequately the unidirectional crawling phenomena observed in experiments such as “spatiotaxis” and “durotaxis,” which allows us to obtain further insights into cell migration.

The implant primary stability of the acetabular cup (AC) is an important parameter for the surgical success of press-fit procedures used for the insertion of cementless hip prostheses. In previous studies by our group (Mathieu, V., Michel, A., Lachaniette, C. H. F., Poignard, A., Hernigou, P., Allain, J., and Haiat, G., 2013, “Variation of the Impact Duration During the in vitro Insertion of Acetabular Cup Implants,” Med. Eng. Phys., 35(11), pp. 1558–1563) and (Michel, A., Bosc, R., Mathieu, V., Hernigou, P., and Haiat, G., 2014, “Monitoring the Press-Fit Insertion of an Acetabular Cup by Impact Measurements: Influence of Bone Abrasion,” Proc. Inst. Mech. Eng., Part H, 228(10), pp. 1027–1034), the impact momentum and duration were shown to carry information on the press-fit insertion of the AC within bone tissue. The aim of the present study is to relate the impact momentum recorded during the AC insertion to the AC biomechanical primary stability. The experimental protocol consisted in testing 13 bovine bone samples that underwent successively series of 15 reproducible mass falls impacts (5 kg, 5 cm) followed by tangential stability testing. Each bone sample was tested with different hole sizes in order to obtain different stability configurations. The impact momentum and the tangential primary stability reach a maximum value for an interference fit equal to around 1 mm. Moreover, a correlation between the impact momentum and the stability was obtained with all samples and all configuration (R2 = 0.65). The implant primary stability can be assessed through the measurement of the impact force signal analysis. This study opens new paths for the development of a medical device which could be used as a decision support system to assist the surgeon during the insertion of the AC implant.

Dynamic biochemical signal control is important in in vitro cell studies. This work analyzes the transportation of dynamic biochemical signals in steady and mixing flow in a shallow, Y-shaped microfluidic channel. The characteristics of transportation of different signals are investigated, and the combined effect of transverse diffusion and longitudinal dispersion is studied. A method is presented to control the widths of two steady flows in the mixing channel from two inlets. The transfer function and the cutoff frequency of the mixing channel as a transmission system are presented by analytically solving the governing equations for the time-dependent Taylor–Aris dispersion and molecular diffusion. The amplitude and phase spectra show that the mixing Y-shaped microfluidic channel acts as a low-pass filter due to the longitudinal dispersion. With transverse molecular diffusion, the magnitudes of the output dynamic signal are reduced compared to those without transverse molecular diffusion. The inverse problem of signal transportation for signal control is also solved and analyzed.

Experimental studies where cells are seeded on micropost arrays in order to quantify their contractile behavior are becoming increasingly common. Interpretation of the data generated by this experimental technique is difficult, due to the complexity of the processes underlying cellular contractility and mechanotransduction. In the current study, a coupled framework that considers strain rate dependent contractility and remodeling of the cytoskeleton is used in tandem with a thermodynamic model of tension dependent focal adhesion formation to investigate the biomechanical response of cells adhered to micropost arrays. Computational investigations of the following experimental studies are presented: cell behavior on different sized arrays with a range of post stiffness; stress fiber and focal adhesion formation in irregularly shaped cells; the response of cells to deformations applied locally to individual posts; and the response of cells to equibiaxial stretching of micropost arrays. The predicted stress fiber and focal adhesion distributions; in addition to the predicted post tractions are quantitatively and qualitatively supported by previously published experimental data. The computational models presented in this study thus provide a framework for the design and interpretation of experimental micropost studies.

The extracellular matrix is no longer considered a static support structure for cells but a dynamic signaling network with the power to influence cell, tissue, and whole organ physiology. In the myocardium, cardiac fibroblasts are the primary cell type responsible for the synthesis, deposition, and degradation of matrix proteins, and they therefore play a critical role in the development and maintenance of functional heart tissue. This review will summarize the extensive research conducted in vivo and in vitro, demonstrating the influence of both physical and chemical stimuli on cardiac fibroblasts and how these interactions impact both the extracellular matrix and, by extension, cardiomyocytes. This work is of considerable significance, given that cardiovascular diseases are marked by extensive remodeling of the extracellular matrix, which ultimately impairs the functional capacity of the heart. We seek to summarize the unique role of cardiac fibroblasts in normal cardiac development and the most prevalent cardiac pathologies, including congenital heart defects, hypertension, hypertrophy, and the remodeled heart following myocardial infarction. We will conclude by identifying existing holes in the research that, if answered, have the potential to dramatically improve current therapeutic strategies for the repair and regeneration of damaged myocardium via mechanotransductive signaling.

Cardiac imaging using magnetic resonance requires a gating signal in order to compensate for motion. Human patients are routinely scanned using an electrocardiogram (ECG) as a gating signal during imaging. However, we found that in sheep the ECG is not a reliable method for gating. We developed a software based method that allowed us to use the left ventricular pressure (LVP) as a reliable gating signal. By taking the time derivative of the LVP (dP/dt), we were able to start imaging at both end-diastole for systolic phase images, and end-systole for diastolic phase images. We also used MR tissue tagging to calculate 3D strain information during diastole. Using the LVP in combination with our digital circuit provided a reliable and time efficient method for ovine cardiac imaging. Unlike the ECG signal the left ventricular pressure was a clean signal and allowed for accurate, nondelay based triggering during systole and diastole.