Journal: Annals of biomedical engineering
Active electronic implants are powered by primary batteries, which induces the necessity of implant replacement after battery depletion. This causes repeated interventions in a patients' life, which bears the risk of complications and is costly. By using energy harvesting devices to power the implant, device replacements may be avoided and the device size may be reduced dramatically. Recently, several groups presented prototypes of implants powered by subcutaneous solar cells. However, data about the expected real-life power output of subcutaneously implanted solar cells was lacking so far. In this study, we report the first real-life validation data of energy harvesting by subcutaneous solar cells. Portable light measurement devices that feature solar cells (cell area = 3.6 cm(2)) and continuously measure a subcutaneous solar cell’s output power were built. The measurement devices were worn by volunteers in their daily routine in summer, autumn and winter. In addition to the measured output power, influences such as season, weather and human activity were analyzed. The obtained mean power over the whole study period was 67 µW (=19 µW cm(-2)), which is sufficient to power e.g. a cardiac pacemaker.
The incidence of wound infections that do not adequately respond to standard-of-care antimicrobial treatment has been increasing. To address this challenge, a novel antimicrobial magnetic thermotherapy platform has been developed in which a high-amplitude, high-frequency, alternating magnetic field is used to rapidly heat magnetic nanoparticles that are bound to Staphylococcus aureus (S. aureus). The antimicrobial efficacy of this platform was evaluated in the treatment of both an in vitro culture model of S. aureus biofilm and a mouse model of cutaneous S. aureus infection. We demonstrated that an antibody-targeted magnetic nanoparticle bound to S. aureus was effective at thermally inactivating S. aureus and achieving accelerated wound healing without causing tissue injury.
Baseball pitching imposes a dangerous valgus load on the elbow that puts the joint at severe risk for injury. The goal of this study was to develop a musculoskeletal modeling approach to enable evaluation of muscle-tendon contributions to mitigating elbow injury risk in pitching. We implemented a forward dynamic simulation framework that used a scaled biomechanical model to reproduce a pitching motion recorded from a high school pitcher. The medial elbow muscles generated substantial, protective, varus elbow moments in our simulations. For our subject, the triceps generated large varus moments at the time of peak valgus loading; varus moments generated by the flexor digitorum superficialis were larger, but occurred later in the motion. Increasing muscle-tendon force output, either by augmenting parameters associated with strength and power or by increasing activation levels, decreased the load on the ulnar collateral ligament. Published methods have not previously quantified the biomechanics of elbow muscles during pitching. This simulation study represents a critical advancement in the study of baseball pitching and highlights the utility of simulation techniques in the study of this difficult problem.
A striking feature of stress relaxation in biological soft tissue is that it frequently follows a power law in time with an exponent that is independent of strain even when the elastic properties of the tissue are highly nonlinear. This kind of behavior is an example of quasi-linear viscoelasticity, and is usually modeled in a purely empirical fashion. The goal of the present study was to account for quasi-linear viscoelasticity in mechanistic terms based on our previously developed hypothesis that it arises as a result of isolated micro-yield events occurring in sequence throughout the tissue, each event passing the stress it was sustaining on to other regions of the tissue until they themselves yield. We modeled stress relaxation computationally in a collection of stress-bearing elements. Each element experiences a stochastic sequence of either increases in elastic equilibrium length or decreases in stiffness according to the stress imposed upon it. This successfully predicts quasi-linear viscoelastic behavior, and in addition predicts power-law stress relaxation that proceeds at the same slow rate as observed in real biological soft tissue.
Numerical simulations are performed on patient-specific basilar aneurysms that are treated with shape memory polymer (SMP) foam. In order to assess the post-treatment hemodynamics, two modeling approaches are employed. In the first, the foam geometry is obtained from a micro-CT scan and the pulsatile blood flow within the foam is simulated for both Newtonian and non-Newtonian viscosity models. In the second, the foam is represented as a porous media continuum, which has permeability properties that are determined by computing the pressure gradient through the foam geometry over a range of flow speeds comparable to those of in vivo conditions. Virtual angiography and additional post-processing demonstrate that the SMP foam significantly reduces the blood flow speed within the treated aneurysms, while eliminating the high-frequency velocity fluctuations that are present within the pre-treatment aneurysms. An estimation of the initial locations of thrombus formation throughout the SMP foam is obtained by means of a low fidelity thrombosis model that is based upon the residence time and shear rate of blood. The Newtonian viscosity model and the porous media model capture similar qualitative trends, though both yield a smaller volume of thrombus within the SMP foam.
Head impact exposure in youth football has not been well-documented, despite children under the age of 14 accounting for 70% of all football players in the United States. The objective of this study was to quantify the head impact exposure of youth football players, age 9-12, for all practices and games over the course of single season. A total of 50 players (age = 11.0 ± 1.1 years) on three teams were equipped with helmet mounted accelerometer arrays, which monitored each impact players sustained during practices and games. During the season, 11,978 impacts were recorded for this age group. Players averaged 240 ± 147 impacts for the season with linear and rotational 95th percentile magnitudes of 43 ± 7 g and 2034 ± 361 rad/s(2). Overall, practice and game sessions involved similar impact frequencies and magnitudes. One of the three teams however, had substantially fewer impacts per practice and lower 95th percentile magnitudes in practices due to a concerted effort to limit contact in practices. The same team also participated in fewer practices, further reducing the number of impacts each player experienced in practice. Head impact exposures in games showed no statistical difference. While the acceleration magnitudes among 9-12 year old players tended to be lower than those reported for older players, some recorded high magnitude impacts were similar to those seen at the high school and college level. Head impact exposure in youth football may be appreciably reduced by limiting contact in practices. Further research is required to assess whether such a reduction in head impact exposure will result in a reduction in concussion incidence.
Current selection of cushioning materials for therapeutic footwear and orthoses is based on empirical and anecdotal evidence. The aim of this investigation is to assess the biomechanical properties of carefully selected cushioning materials and to establish the basis for patient-specific material optimisation. For this purpose, bespoke cushioning materials with qualitatively similar mechanical behaviour but different stiffness were produced. Healthy volunteers were asked to stand and walk on materials with varying stiffness and their capacity for pressure reduction was assessed. Mechanical testing using a surrogate heel model was employed to investigate the effect of loading on optimum stiffness. Results indicated that optimising the stiffness of cushioning materials improved pressure reduction during standing and walking by at least 16 and 19% respectively. Moreover, the optimum stiffness was strongly correlated to body mass (BM) and body mass index (BMI), with stiffer materials needed in the case of people with higher BM or BMI. Mechanical testing confirmed that optimum stiffness increases with the magnitude of compressive loading. For the first time, this study provides quantitative data to support the importance of stiffness optimisation in cushioning materials and sets the basis for methods to inform optimum material selection in the clinic.
Recent research has suggested possible long term effects due to repetitive concussions, highlighting the importance of developing methods to accurately quantify concussion risk. This study introduces a new injury metric, the combined probability of concussion, which computes the overall risk of concussion based on the peak linear and rotational accelerations experienced by the head during impact. The combined probability of concussion is unique in that it determines the likelihood of sustaining a concussion for a given impact, regardless of whether the injury would be reported or not. The risk curve was derived from data collected from instrumented football players (63,011 impacts including 37 concussions), which was adjusted to account for the underreporting of concussion. The predictive capability of this new metric is compared to that of single biomechanical parameters. The capabilities of these parameters to accurately predict concussion incidence were evaluated using two separate datasets: the Head Impact Telemetry System (HITS) data and National Football League (NFL) data collected from impact reconstructions using dummies (58 impacts including 25 concussions). Receiver operating characteristic curves were generated, and all parameters were significantly better at predicting injury than random guessing. The combined probability of concussion had the greatest area under the curve for all datasets. In the HITS dataset, the combined probability of concussion and linear acceleration were significantly better predictors of concussion than rotational acceleration alone, but not different from each other. In the NFL dataset, there were no significant differences between parameters. The combined probability of concussion is a valuable method to assess concussion risk in a laboratory setting for evaluating product safety.
Bicycling is the leading cause of sports-related traumatic brain injury. Most of the current bike helmets are made of expanded polystyrene (EPS) foam and ultimately designed to prevent blunt trauma, e.g., skull fracture. However, these helmets have limited effectiveness in preventing brain injuries. With the availability of high-rate micro-electrical-mechanical systems sensors and high energy density batteries, a new class of helmets, i.e., expandable helmets, can sense an impending collision and expand to protect the head. By allowing softer liner medium and larger helmet sizes, this novel approach in helmet design provides the opportunity to achieve much lower acceleration levels during collision and may reduce the risk of brain injury. In this study, we first develop theoretical frameworks to investigate impact dynamics of current EPS helmets and airbag helmets-as a form of expandable helmet design. We compared our theoretical models with anthropomorphic test dummy drop test experiments. Peak accelerations obtained from these experiments with airbag helmets achieve up to an 8-fold reduction in the risk of concussion compared to standard EPS helmets. Furthermore, we construct an optimization framework for airbag helmets to minimize concussion and severe head injury risks at different impact velocities, while avoiding excessive deformation and bottoming-out. An optimized airbag helmet with 0.12 m thickness at 72 ± 8 kPa reduces the head injury criterion (HIC) value to 190 ± 25 at 6.2 m/s head impact velocity compared to a HIC of 1300 with a standard EPS helmet. Based on a correlation with previously reported HIC values in the literature, this airbag helmet design substantially reduces the risks of severe head injury up to 9 m/s.
Optimizing the protective capabilities of helmets is one of several methods of reducing brain injury risk in sports. This paper presents the experimental and analytical development of a hockey helmet evaluation methodology. The Summation of Tests for the Analysis of Risk (STAR) formula combines head impact exposure with brain injury probability over the broad range of 227 head impacts that a hockey player is likely to experience during one season. These impact exposure data are mapped to laboratory testing parameters using a series of 12 impact conditions comprised of three energy levels and four head impact locations, which include centric and non-centric directions of force. Injury risk is determined using a multivariate injury risk function that incorporates both linear and rotational head acceleration measurements. All testing parameters are presented along with exemplar helmet test data. The Hockey STAR methodology provides a scientific framework for manufacturers to optimize hockey helmet design for injury risk reduction, as well as providing consumers with a meaningful metric to assess the relative performance of hockey helmets.