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Surgeons routinely perform incremental releases on overly tight ligaments during total knee arthroplasty (TKA) to reduce ligament tension and achieve their desired implant alignment. However, current methods to assess whether the surgeon achieved their desired reduction in the tension of a released ligament are subjective and/or do not provide a quantitative metric of tension in an individual ligament. Accordingly, the purpose of this study was to determine whether shear wave tensiometry, a novel method to assess tension in individual ligaments based on the speed of shear wave propagation, can detect changes in ligament tension following incremental releases. In seven medial and eight lateral collateral porcine ligaments (MCL and LCL, respectively), we measured shear wave speeds and ligament tension before and after incremental releases consisting of punctures with an 18-gauge needle. We found that shear wave speed squared decreased linearly with decreasing tension in both the MCL (r^2 avg = 0.76) and LCL (r^2 avg = 0.94). We determined that errors in predicting tension following incremental releases were 24.5 N and 12.2 N in the MCL and LCL, respectively, using specimen-specific calibrations. These results suggest shear wave tensiometry is a promising method to objectively measure the tension reduction in released structures. Clinical Significance: Direct, objective measurements of the tension changes in individual ligaments following release could enhance surgical precision during soft tissue balancing in TKA. Thus, shear wave tensiometry could help surgeons reduce the risk of poor outcomes associated with overly tight ligaments, including residual knee pain and stiffness.

PURPOSE Polymer covered stents have demonstrated promising clinical outcomes with improved patency rates compared to traditional bare-metal stents. However, little is known on the mechanical implication of stent covering. This study aims to provide insight into the role of a polymeric cover on the biomechanical performance of self-expanding laser-cut stents through a combined experimental-computational approach. METHODS Experimental bench top tests were conducted on bare and covered versions of a commercial stent to evaluate the radial, axial and bending response. In parallel, a computational framework with a novel covering strategy was developed that accurately predicts stent mechanical performance. Different stent geometries and polymer materials were also considered to further improve understanding on covered stent mechanics. RESULTS Results show that stent covering causes increased initial axial stiffness and up to 60% greater radial resistive force at small crimp diameters as the cover folds and self-contacts. The incorporation of a cover allows stent designs without interconnecting struts, thereby providing improved flexibility without compromising radial force. It was also shown that use of a stiffer PET polymeric covering material caused significant alterations to the radial and axial response, with the initial axial stiffness increasing six-fold and the maximum radial resistive force increasing four-fold compared to a PTFE-PU covered stent. CONCLUSION This study demonstrates that stent covering has a substantial effect on the overall stent mechanical performance and highlights the importance of considering the mechanical properties of the combined cover and stent.

In recent years, thanks to additive manufacturing technology, researchers have gone towards the optimization of bone scaffolds for the bone reconstruction. Bone scaffolds should have appropriate biological as well as mechanical properties in order to play a decisive role in bone healing. Since the fabrication of scaffolds is time consuming and expensive, numerical methods are often utilized to simulate their mechanical properties in order to find a nearly optimum one. Finite element analysis is one of the most common numerical methods that is used in this regard. In this paper, a parametric finite element model is developed to assess the effects of layers penetration׳s effect on inter-layer adhesion, which is reflected on the mechanical properties of bone scaffolds. To be able to validate this model, some compression test specimens as well as bone scaffolds are fabricated with biocompatible and biodegradable poly lactic acid using fused deposition modeling. All these specimens are tested in compression and their elastic modulus is obtained. Using the material parameters of the compression test specimens, the finite element analysis of the bone scaffold is performed. The obtained elastic modulus is compared with experiment indicating a good agreement. Accordingly, the proposed finite element model is able to predict the mechanical behavior of fabricated bone scaffolds accurately. In addition, the effect of post-heating of bone scaffolds on their elastic modulus is investigated. The results demonstrate that the numerically predicted elastic modulus of scaffold is closer to experimental outcomes in comparison with as-built samples.

Objective: This study describes the development and offline validation of a heuristic algorithm for accurate prediction of ground terrain in a lower limb prosthesis. This method is based on inference of the ground terrain geometry using estimation of prosthetic limb kinematics during gait with a single integrated inertial measurement unit. Methods: We asked five subjects with below-knee amputations to traverse level ground, stairs, and ramps using a high-range-of-motion powered prosthesis while internal sensor data were remotely logged. We used these data to develop three terrain prediction algorithms. The first two employed state-of-the-art machine learning approaches, while the third was a directly tuned heuristic using thresholds on estimated prosthetic ankle joint translations and ground slope. We compared the performance of these algorithms using resubstitution error for the machine learning algorithms and overall error for the heuristic algorithm. Results: Our optimal machine learning algorithm attained a resubstitution error of $3.4\%$ using 45 features, while our heuristic method attained an overall prediction error of $2.8\%$ using only 5 features derived from estimation of ground slope and horizontal and vertical ankle joint displacement. Compared with pattern recognition, the heuristic performed better on each individual subject, and across both level and non-level strides. Conclusion and significance: These results demonstrate a method for heuristic prediction of ground terrain in a powered prosthesis. The method is more accurate, more interpretable, and less computationally expensive than machine learning methods considered state-of-the-art for intent recognition, and relies only on integrated prosthesis sensors. Finally, the method provides intuitively tunable thresholds to improve performance for specific walking conditions.

Mechanical control is essential for adaptive regulation in biological systems. This work presents a magnetic, non-contact approach to simultaneous detection and actuation in a microscale tissue testbed. The platform builds upon previously developed passive mechanical platforms, where tissues self-assemble on flexible pillars. Standard detection is typically derived from microscope images and actuation is often conducted using invasive approaches. In the presented platform, actuation and detection, both derived from magnetic fields, are demonstrated with high spatial, temporal, and force resolution resulting in a detectable $0.6~\mu \text{m}$ step-size in air. The forced deflection range is +/−125 microns (+/−10% strain), and an arbitrary magnitude step-and-settle actuation is achieved in less than 1 ms. Engineered human cardiac microtissue is used to demonstrate the capabilities of the system, as cardiac tissues generate contractions and adapt to external forces. Spontaneous contractions are monitored for an hour by a built-in sensor with a signal-to-noise of 2. Cyclic actuation at 1 Hz using a 1 mT/mm magnetic field is demonstrated, where max tissue strain is 0.3%. All this is achieved in a moderate-throughput, compact device, which is easily integrated into the typical flow of biological experimentation. Simultaneous control of actuation and detection enables decisions to be made on a sample-specific basis, and in future developments, will enable arbitrary design of the mechanical environment for 3D tissue conditioning, maturation, and control. [2020-0296]

Computer modeling and simulation is a powerful tool for assessing the performance of medical devices such as bioprosthetic heart valves (BHVs) that promises to accelerate device design and regulation. This study describes work to develop dynamic computer models of BHVs in the aortic test section of an experimental pulse-duplicator platform that is used in academia, industry, and regulatory agencies to assess BHV performance. These computational models are based on a hyperelastic finite element extension of the immersed boundary method for fluid–structure interaction (FSI). We focus on porcine tissue and bovine pericardial BHVs, which are commonly used in surgical valve replacement. We compare our numerical simulations to experimental data from two similar pulse duplicators, including a commercial ViVitro system and a custom platform related to the ViVitro pulse duplicator. Excellent agreement is demonstrated between the computational and experimental results for bulk flow rates, pressures, valve open areas, and the timing of valve opening and closure in conditions commonly used to assess BHV performance. In addition, reasonable agreement is demonstrated for quantitative measures of leaflet kinematics under these same conditions. This work represents a step towards the experimental validation of this FSI modeling platform for evaluating BHVs. Electronic supplementary material The online version of this article (10.1007/s10439-020-02466-4) contains supplementary material, which is available to authorized users.

In this study, a coupled computational modelling framework for bone fracture repair is presented that enables predictions of both healing and remodelling phases of the fracture region and is used to investigate the role of an internal fixation plate on the long-term healing performance of a fracture tibia under a range of different conditions. It was found that introduction of a titanium plate allowed the tibia to undergo successful healing at higher loading conditions and fracture gaps, compared to the non-plated versions. While these plated cases showed faster rates of repair in the healing phase, their performance was substantially different once they entered the remodelling phase, with substan-tial regions of stress shielding predicted. This framework is one of the few im-plementations of both fracture healing and remodelling phases of bone repair and includes several innovative approaches to smoothing, time-averaging and time incrementation in its implementation, thereby avoiding any unwanted abrupt changes between tissue phenotypes. This provides a better representa-tion of tissue development in the fracture site when compared to fracture healing models alone and provides a suitable platform to investigate the long-term performance of orthopaedic fixation devices. This would enable the more effective design of permanent fixation devices and optimisation of the spatial and temporal performance of bioabsorbable implants

This research proposes a low-cost and simple operation microfluidic chip to enhance the magnetic labeling efficiency of two ischemic stroke biomarkers: cellular fibronectin (c-Fn) and matrix metallopeptidase-9 (MMP9). This fully portable and pump-free microfluidic chip is operated based on capillary attractions without any external power source and battery. It uses an integrated cellulose sponge to absorb the samples. At the same time, a magnetic field is aligned to hold the target labeled by the magnetic nanoparticles (MNPs) in the pre-concentrated chamber. By using this approach, the specific targets are labeled from the beginning of the sampling process without preliminary sample purification. The proposed study enhanced the labeling efficiency from 1 hr to 15 min. The dynamic interactions occur in the serpentine channel, while the crescent formation of MNPs in the pre-concentrated chamber, acting as a magnetic filter, improves the biomarker-MNP interaction. The labeling optimization by the proposed device influences the dynamic range by optimizing the MNP ratio to fit the linear range across the clinical cutoff value. The limit of detection (LOD) of 2.8 ng/mL and 54.6 ng/mL of c-Fn measurement were achieved for undiluted and four times dilutions of MNP, respectively. While for MMP9, the LODs were 11.5 ng/mL for undiluted functionalized MNP and 132 ng/mL for four times dilutions of functionalized MNP. The results highlight the potential use of this device for clinical sample preparation and specific magnetic target labeling. When combined with a detection system could also be used as an integrated component of a point-of-care platform.

This study examines the system integration of a game engine with robotics middleware to drive an 8 degree offreedom (DoF) robotic upper limb to generate human-like motion for telerobotic applications. The developed architectureencompasses a pipeline execution design using Blender Game Engine (BGE) including the acquisition of real humanmovements via the Microsoft Kinect V2, interfaced with a modeled virtual arm, and replication of similar arm movements on the physical robotic arm. In particular, this study emphasizes the integration of a human “pilot” with ways to drive such a robotic arm through simulation and later, into a finished system. Additionally, using motion capture technology, a human upper limb action was recorded and applied onto the robot arm using the proposed architecture flow. Also, we showcase the robotic arm’s actions which include reaching, picking, holding, and dropping an object. This paper presentsa simple and intuitive kinematic modeling and 3D simulation process, which is validated using 8-DoF articulated robot to demonstrate methods for animation, and simulation using the designed interface.

This study assesses the modelling capabilities of four constitutive hyperelastic material models to fit the experimental data of the porcine sclera soft tissue. It further estimates the material parameters and discusses their applicability to a finite element model by examining the statistical dispersion measured through the standard deviation. Fifteen sclera tissues were harvested from porcine’ slaughtered at an abattoir and were subjected to equi-biaxial testing. The results show that all the four material models yielded very good correlations at correlations above 96%. The polynomial (anisotropic) model gave the best correlation of 98%. However, the estimated material parameters varied widely from one test to another such that there would be need to normalise the test data to avoid long optimisation processes after applying the average material parameters to finite element models. However, for application of the estimated material parameters to finite element models, there would be need to consider normalising the test data to reduce the search region for the optimisation algorithms. Although the polynomial (anisotropic) model yielded the best correlation, it was found that the Choi-Vito had the least variation in the estimated material parameters, thereby making it an easier option for application of its material parameters to a finite element model and requiring minimum effort in the optimisation procedure. For the porcine sclera tissue, it was found that the anisotropy was more influenced by the fiber-related properties than the background material matrix-related properties.

Automated microscope slide stainers are usually very expensive and unless the laboratory performs heavy histological work it is difficult to justify buying a 2000-10000€ machine. As a result, histology and pathology labs around the world lose thousands of working hours for following procedures that could be easily automated. Herein, we propose a simple modification of an open-source 3D printer, the Creality Ender-3, into an automated microscope slide autostainer, the HistoEnder. The HistoEnder is cheap (less than 200€), modular, and easy to set up, with only two 3D-printed parts needed. Additionally, the 3D printer retains its full functionality, and it can be reverted back into 3D printing in less than 1 minute. The g-code associated with the procedure is extremely simple, and can be written by anyone. The HistoEnder can also be used in chemistry and material science laboratories for automating surface modifications and dip coating.

This study covers an extended spectrum of clinical cases (1) to analyze the accurate tumour size, (2) to demarcate accurate tumour boundaries in order to plan an effective target volumes for radiotherapy, thermal ablation including radiofrequency ablation and nanoparticles induced thermal ablation. Once the clinical size of the tumour is established, realistic three-dimensional volume of the tumour can be calculated. Accurate margins (0.5 cm -1 cm) can only be sacrificed if true tumour boundaries with irregular nodal spread can be retrieved.

This paper describes the design and testing of a novel hydraulic control valve for use in the minimally invasive surgical robotic manipulator. The use of hydraulics for surgical robotics opens new possibilities for miniaturization and robustness. However, to enable this, there exists a need for hydraulic components which bridge the size gap between traditional fluidics and microfluidics. This paper provides motivation for the development of a miniature hydraulic valve designed specifically to enable a serpentine style hydraulic surgical manipulator. Included are a description of the various considerations relevant to the valve and its specific application, such as the method of manipulation for the valve, as well as a theoretical valve design and a mathematical description of the operating principles. Two possible methods of valve activation, piezoelectric and electromagnetic, are discussed along with two physical realizations of the valve design are presented which demonstrate the theoretical design. Finally, the results of experimental testing performed on valve prototypes is described to evaluate the design options and help inform the selection of the final configuration.

It is still unknown how molecules coordinate their activity and operate at high speeds in the crowded environment of a cell. The study focuses on the geometry of biomolecules, assuming B-DNA, α-helix, β-strand, water molecules, and chemical bonds, including hydrogen bonds, as various types of antennas. The analysis demonstrates that living systems have highly sophisticated wireless and wired communication infrastructures for regulating and coordinating molecular activities, revealing why water is essential for molecular dynamics and indicating how we evolved. The study also includes a few equations linking antenna fields with Einstein’s general relativity, Kepler’s law of planetary motion, and Newton’s law of gravitation, which divides the gravitational field into antenna field zones and clarifies many astronomical facts. The findings, furthermore, suggest that the gravitational field is the antenna field of astronomical objects; and that nature's antennas, such as molecules and astronomical objects, communicate via gravitational waves. We hope that the study, which uses a classical approach to explain the facts of living systems and the Universe, will find applications in biology, astronomy, communication engineering, and other areas of science.

Immuno-Charge is an application that focuses on collecting clinical data for clinical trial purposes and helps to fix appointments for vaccination. The application acts as a bridge between the user and pharmaceutical company as it provides required data to the company and provides an easy interface to use, both for user and company. It also keeps records and track of vaccines taken by users. Users must fill in the data that is provided to them in a question-and-answer format. The limitation is that the authentication of the users medical data & symptoms is not done. The progress of immunization can be a long, complex process that regularly lasts 10-15 years and includes a combination of public and private enterprise involvement. Vaccines are designed, tested, and directed in a really similar way to other drugs. Clinical trials are research studies or observations done in clinical research. Such planned biomedical or behavioral studies on human members are designed to answer specific questions about biomedical or behavioral interventions, including new treatments (such as novel vaccines, drugs etc) and known intercessions that warrant encouraging thought about and comparison. Clinical studies provide information about measurement, safety, and feasibility.

The modelling of tendon behaviour during failure stages is nonlinear and heavily random. However, the understanding of its behavior during such stages, and development of models that can give an accurate prediction of its behavior during failure can provide a means for developing effective tendon therapies. This study is aimed at demonstrating the capability of an artificial neural network in the modelling of failure phases in tendons. A nonlinear autoregressive with exogenous inputs network is applied to three different tensile test data of the human supraspinatus tendons. Owing to data scarcity, the network was trained using two different test data which were randomly sampled and divided into 50%, 25% and 25% proportions for training, validation and preliminary testing. The third test data were used for the final testing phase. The procedure was cyclically performed for each of the results that have been presented in this study. The neural network predictions are presented as curves fitted over actual test results with corresponding error plots. The results indicate that the network is able to accurately predict the failure behaviour of these tendons with correlations of above 99 % for all tests. This is an excellent and very promising result in the light of the difficulties that most deterministic mechanistic models encounter in the modelling of soft tissue failure behaviour. With further development of this technique, sports and exercise physicians would enhance knowledge in mechanisms of tendon failure and be able to devise more injury preventive strategies.

Biological rhythms permeate all living organisms at a variety of timescales. These rhythms are fundamental to physiological homeostasis, and their disruption is thought to play a key role in the initiation, progression, and expression of disease. In the last two decades, neuromodulation has been established as an effective adjunct therapy for medically refractory neurological disorders. To date, however, due to the limited sensing and algorithm capabilities of neuromodulation devices, exploring the influence of biological rhythms on therapy efficacy has not been feasible. However, with the development of new bioelectronic devices capable of long-term data recording and adaptive stimulation parameter adjustments, clinical neuroscience researchers are now gaining unprecedented insight into patient physiology across a variety of neurological diseases, including longitudinal rhythmic behavior. In this perspective, we propose that future bioelectronic devices should integrate chronobiological considerations in their physiological control structure to maximize the benefits of therapy. We specifically highlight this need for deep brain stimulation (DBS) chronotherapy, where the DBS therapeutic dosage would be titrated based on the time-of-day and synchronized to each patient’s individual chronotype/sleep-wake cycle. This is motivated by preliminary longitudinal data recorded from both patients with Parkinson’s disease (PD) and epilepsy, which show periodic symptom biomarkers synchronized to sub-daily (ultradian), daily (circadian), and longer time scale (infradian) rhythms. In addition, considering side effects, tonic stimulation can undermine diurnal patterns and cause fragmentation of sleep-wake rhythms. Based on these observations, we suggest a control structure for future bioelectronic devices which incorporates anticipatory, time-based adaptation of stimulation control, locked to patient-specific biological rhythms, as an adjunct to classical feedforward and feedback control methods. Initial results from three case studies using chronotherapy-enabled prototypes will illustrate the concept. The proposed control architecture for a future bioelectronic implant mimics more closely the classical integration of adaptive, feedforward, and feedback control methods found in physiology, and could be useful as a general method for personalized therapy refinement.

The ability to transport and store a large human blood inventory for transfusions is an essential requirement for medical institutions. Thus, there is an important need for rapid and low-cost characterization tools for analyzing the properties of human red blood cells (RBCs) while in storage. In this study, we investigate the ability to use dielectrophoresis (DEP) for measuring the storage-induced changes in RBC electrical properties. Fresh human blood was collected, suspended in K2-EDTA anticoagulant and stored in a blood bank refrigerator for a period of 20 days. Cells were removed from storage at 5-day intervals and subjected to a glutaraldehyde crosslinking reaction to “freeze” cells at their ionic equilibrium at that point in time and prevent ion leakage during DEP analysis. The DEP behavior of RBCs was analyzed in a high permittivity DEP buffer using a three-dimensional DEP chip (3DEP) and also compared to measurements taken with a 2D quadrupole electrode array. The DEP analysis confirms that RBC electrical property changes occur during storage and are only discernable with the use of the cell crosslinking reaction above a glutaraldehyde fixation concentration of 1.0 w/v%. In particular, cytoplasm conductivity was observed to decrease by more than 75% while the RBC membrane conductance was observed to increase by more than 1000% over a period of 20 days. These results show that the presented combination of chemical crosslinking and DEP can be used as rapid characterization tool for monitoring electrical properties changes of human red blood cells while subjected to refrigeration in blood bank storage.

Consumers' demand, perception, and preference for high quality, safe food, and awareness of health hazards of synthetic and chemical preservatives have attracted the food processors, food technologist's and industries to develop innovative techniques with the incorporation of natural components and minimal processing. Developing and utilizing the active edible coatings for shelf stability of the semi-perishable foods is one of these new innovative techniques. That's why the present study is planned to develop active edible coating using clove and peppermint essential oils as active ingredients and whey powder as the base material. Essential oils of peppermint and clove possess the antioxidant and antimicrobial properties. It is supposed that these properties of essential oils may enhance the safety, quality, and shelf stability of the cheese during storage. Soft cheese will be prepared, and these coatings will be applied to it. Then products will be stored at 4 and 25 ℃ for 15 days and will be analyzed every 3rd day for various safety, quality, and shelf stability attributes. All the obtained data will be statically analyzed to check the level of significance of these coatings.

Whey is nutritious by-product of cheese industry which is dumped into fields, canals and rivers in Bangladesh. It has biological active peptides, proteins and other functional characteristics, due to which it could be used in different food products such as infant formula, beverages and sports nutrition products. Grapes and papaya are rich source of sugar, vitamins, bioactive compounds, dietary antioxidants and fructo-oligosaccharide. They improve the mineral absorption, decrease the serum cholesterol level and stimulate the intestinal microflora due to their prebiotic effects. The current study is being planned to develop low cost nutritive whey-based fruit drink in Bangladesh. Grapes and papaya will be added in the whey with different ratio for development of suitable drink. The drinks will be pasteurized or unpasteurized filled in bottles and keep in refrigerator for the period of 15 days. During storage drinks will be evaluated for physicochemical attributes (ash, pH, crude fat, crude protein, acidity, solid not fat, viscosity and total solids), Microbial (TVC and Coliform) and sensory evaluation (appearance, taste, flavor and over all acceptability) will also be came out during storage. Consequently, the obtained data will be analyzed statistically following two factors under CRD design.

Emerging research in biosensors has attracted much attention worldwide, particularly in re-sponse to the recent pandemic outbreak of coronavirus disease 2019 (COVID-19). Neverthe-less, initiating research in biosensing applied to the diagnostic of diseases is still challenging for researchers, either from the preferences of biosensor platforms, selection of biomarkers, detection strategies, and other aspects (e.g., cutoff values) to fulfill the clinical purpose. There are two sides to the development of a diagnostic tool: the biosensor development side and the clinical side. From the development side, the research engineers seek the typical characteristics of a biosensor: sensitivity, selectivity, linearity, stability, and reproducibility. On the other side are the physicians that expect a diagnostic tool that provides fast acquisition of patient in-formation to obtain an early diagnostic or an efficient patient stratification, which conse-quently allows making assertive and efficient clinical decisions. The development of diagnostic devices always involves assay developer researchers, working as pivots to bridge both sides, which role is to find detection strategies suitable to the clinical needs. First, by understanding the intended use of the technology and its basic principle; second, the preferable type of test: qualitative or quantitative, sample matrix challenges, biomarker(s) threshold (cutoff value), and if the system requires a mono or multiplex assay format. This review highlights the challenges for the development of biosensors for clinical assessment and its broad application in multidisciplinary fields. This review paper highlights the following biosensor technologies: magnetoresistive (MR)-based, transistor-based, quartz crystal microbalance (QCM), and op-tical-based biosensors. Its working mechanisms are discussed with their pros and cons. The article also gives an overview of the most critical parameters that are optimized by developing a diagnostic tool.

Since the discovery of synthetic HAp in the 1950s, hydroxyapatite is becoming a significant covering material for bio implants. A regulated surface roughness/porosity, appropriate chemical resistance, and a desirable tri-biological behavior are required for HAp coatings. On substrates with a variety of structure, composition, size, and shape, the coating process must be applied at varied scales and at a fast enough rate. There is a full description of both dry and wet coating procedures included in this article. Cathode efficiency fell as tc- increased, although it was still better than DC coatings. In this paper, the mechanism of HAp electrodeposition is examined, as well as the effect of operational variables on deposit characteristics. Recent advances in the field are critically examined. HAp composite coatings, including those reinforced with metallic, ceramic, and polymeric particles, as well as nanotubes, modified graphene’s, chitosan, and heparin, are discussed in depth. On the other hand, a glance towards the future in the field of electrodeposited HAp coatings is taken. Different experimental parameters were explored to establish the optimal reaction conditions for HA-Ag nanocomposites. Pulse reverse plating (PRP) in combination with an anionic surfactant, sodium dodecylsulphate (SDS), was utilized for the first time to generate nanocomposite Co-Al2O3 electrodeposited coatings, using a technique similar to that used for Co – IF WS2 deposition in prior work. The optimal plating setups in the pulse-reverse electroplating (PRP) mechanism for non-anomalous plating of Co–Ni deposits (i.e., the metal composition of deposits equals that of the plating solutions) from chloride solutions were determined using experimental strategies such as fractional factorial design (FFD), path of steepest ascent, and central composite design (CCD) combined with the response surfaces (RSM). The FFD research found that the potentials and time duration of pulse-plating had a significant impact on the composition of Co–Ni deposits. The two parameters were the sharpest ascending route and the best circumstances for non-anomalous plating of Co–Ni layers. NiFe thin films produced by pulse reverse (PR) electrodeposition are potential alternatives for the next phase of core magnetic materials that will be utilized in high shifting frequency magnetic elements. For statistical modeling and analysis of the nickel electroplating process outcomes, the central composite experimental design and response surface technique were used. The empirical models developed in terms of design variables (current density J (A/dm2), temperature T (C), and pH) were found to be statistically adequate to describe the process responses, namely cathode efficiency Y%, coating thickness U (m), brightness V%, and hardness W%. (HV). The response surfaces were explored and analyzed using graphical representations consisting of 2D contour plots and 3D surface plots in order to determine the main, quadratic, and interaction effects. The desirability function method was used to do multi-response optimization of the nickel electroplating process. To this aim, a genetic algorithm was employed to solve the multi-response issue mathematically. The optimization method resulted in the Pareto optimum set, which is a collection of similar solutions.

The competing structural and hemodynamic considerations in valve design generally require a large amount of in vitro hydrodynamic and durability testing during development, often resulting in inefficient “trial-and-error” prototyping. While in silico modelling through Finite Element Analysis (FEA) has been widely used to inform valve design by optimizing structural performance, few studies have exploited the potential insight FEA could provide into critical hemodynamic performance characteristics of the valve. The objective of this study is to demonstrate the potential of FEA to predict the hydrodynamic performance of aortic valve implants obtained during development through in vitro testing. Several variations of surgical tri-leaflet aortic valves were de-signed and manufactured using a synthetic polymer and hydrodynamic testing carried out using a pulsatile flow rig according to ISO 5840, with bulk hydro-dynamic parameters measured. In silico models were developed in tandem and suitable surrogate measures were investigated as predictors of the hydro-dynamic parameters. Through regression analysis, the in silico parameters of leaflet coaptation area, geometric orifice area and opening pressure were found to be suitable indicators of experimental in vitro hydrodynamic param-eters: regurgitant fraction, effective orifice area and transvalvular pressure drop performance, respectively.

Extensive scientific analysis on the susceptibility of different populations to COVID-19 highlights that compared to populations with no co-morbidities, people with co-morbidities such as diabetes mellitus (DM) are at a significantly higher risk of serious infection, hospitalization, or even death. This underscores the importance of controlling DM through self-monitoring of blood glucose (SMBG) or continuous glucose monitoring (CGM) using biosensors. These biosensors, which can be either enzymatic or non-enzymatic, have undergone several rounds of development in terms of the materials used for device construction. In terms of the immobilization agent needed to anchor enzymatic or non-enzymatic detection elements to the electrode surface, marine polysaccharides, such as chitosan and alginate, hold a distinct advantage. This, in turn, can be ascribed to their biocompatibility, chemical stability, film-forming ability, capacity to bind with enzymes/diagnostic elements, and easy availability. In this review, we focus extensively on the use of cationic chitosan and anionic alginate in the past decade for designing advanced glucose biosensors. Their role in enhancing sensor response via physical/chemical interactions with the conducting and diagnostic elements is analyzed in detail from a structural point of view. In addition, the possibility of using these polysaccharides in non-invasive CGM sensors is discussed and several potential future research avenues are presented.

The use of conventional in vitro and preclinical animal models often fail to properly recapitulate the complex nature of human diseases and hamper the success of translational therapies in humans [1-3] Consequently, research has moved towards organ-on-chip technology to better mimic human tissue interfaces and organ functionality. Herein, we describe a novel approach for the fabrication of a biocompatible membrane made of porous silicon (PSi) for use in organ-on-chip technology that provides key advantages when modeling complex tissue interfaces seen in vivo. By combining well-established methods in the semiconductor industry with organ-on-chip technology, we have developed a novel way of producing thin (25 μm) freestanding PSi biocompatible membranes with both nano (~15.5 nm diameter pores) and macroporous (~0.5 μm diameter pores) structures. To validate the proposed novel membrane, we chose to recapitulate the dynamic environment of the alveolar blood gas exchange interface in alveolar co-culture. Viability assays and immunofluorescence imaging indicate that human pulmonary cells remain viable on the PSi membrane during long-term culture (14 days). Interestingly, it was observed that macrophages can significantly remodel and degrade the PSi membrane substrate in culture. This degradation will allow for more intimate physiological cellular contact between cells, mimicking a true blood-gas exchange interface as observed in vivo. Broadly, we believe that this novel PSi membrane may be used in more complex organ-on-chip and lab-on-chip model systems to accurately recapitulate human anatomy and physiology to provide further insight into human disease pathology and pre-clinical response to therapeutics.

We aim to demystify the development of neuro-biomechanical modeling in OpenSim with zero configuration, easy to share models while accessing to free GPUs on a web-based platform of Google Colaboratory. OpenSim is an open-source biomechanical package. OpenSim is used in a variety of applications and developed in C++; however, it is available for a wide range of users with bindings in MATLAB, Python, Jython and Java via OpenSim Application Programing Interface (API). OpenSim installation on a personal computer is well described by the developers but its implementation may still be time-consuming and challenging for the new users. Cloud-based computing is expanding in almost all engineering domains with zero configuration, though it is in its early stages within biomechanics community. In this study, we aim to access OpenSim functionality on the Google cloud platform. The methods can also be used in other cloud-based platforms. We installed OpenSim on the Google Colab via Anaconda cloud and named it OpenColab. To use OpenColab, one requires only a connection to the internet and a Gmail account. Moreover, such a platform enables the users to access vast libraries of machine learning available within free Google products e.g., TensorFlow. OpenColab takes advantage of zero configuration of cloud-based platforms even on a smart phone, provides access to free GPUs and enables users to share and reproduce modeling approaches for further validation. Finally, we performed inverse problem in biomechanics and compared OpenColab results with OpenSim GUI’s for validation. Step-by-step installation processes and examples can be found freely at: https://simtk.org/projects/opencolab.

Functional, comfortable prosthetic limbs depend on personalised sockets, currently designed using an iterative, expert-led process, which can be expensive and inconvenient. Computer-aided design and manufacturing (CAD/CAM) offers enhanced repeatability, but far more use could be made from clinicians’ extensive digital design records. Knowledge-based socket design using smart templates could collate successful design features and tailor them to a new patient. Based on 67 residual limb scans and corresponding sockets, this paper develops a method of objectively analysing personalised design approaches by expert prosthetists, using machine learning: principal component analysis (PCA) to extract key categories in anatomic and surgical variation, and k-means clustering to identify local ‘rectification’ design features. Rectification patterns representing Total Surface Bearing and Patella Tendon Bearing design philosophies are identified automatically by PCA, which reveals trends in socket design choice for different limb shapes that match clinical guidelines. Expert design practice is quantified by measuring the size of local rectifications identified by k-means clustering. Implementing smart templates based on these trends requires clinical assessment by prosthetists and does not substitute training. This study provides methods for population-based socket design analysis, and example data, which will support developments in CAD/CAM clinical practice and accuracy of biomechanics research.

Deafblindness is the condition in which a person has little or no useful hearing and vision. The dysfunction in both senses can occur in different ways and degrees for each person. These differences impose specific barriers and strategies for the rehabilitation of this public, regarding protocols and assistive devices. Most of the technologies and procedures are geared towards a better adaptation of communication. Inspired by sign languages and Braille, we propose the construction of a device capable of transducing specific hand movements of an individual (emitter), into specific vibration patterns on the body of another individual (receiver). Through this device it is possible to adjust different stimulation parameters, based on the modulation of amplitude, frequency of vibration and temporal sequence of pulses, making possible enabling a sensory refinement of wrist and finger's movements, in order to have an improvement associated with the alphabet intended to be represented. This prototype allows communication through crossing-modalities (movement and touch) without the need of visual contact or auditory perception, enabling a future application for deaf-blind people.

Hyperspectral fluorescence imaging is widely used when multiple fluorescent probes with close emission peaks are required. In particular, Fourier transform imaging spectroscopy (FTIS) provides unrivaled spectral resolution; however, the imaging throughput is very low due to the amount of interferogram sampling required. In this work, we apply deep learning to FTIS and show that the interferogram sampling can be drastically reduced by an order of magnitude without noticeable degradation in the image quality. For the demonstration, we use bovine pulmonary artery endothelial cells stained with three fluorescent dyes and 10 types of fluorescent beads with close emission peaks. Further, we show that the deep learning approach is more robust to the translation stage error and environmental vibrations. Thereby, the He-Ne correction, which is typically required for FTIS, can be bypassed, thus reducing the cost, size, and complexity of the FTIS system. Finally, we construct neural network models using Hyperband, an automatic hyperparameter selection algorithm, and compare the performance with our manually-optimized model.

Orthopaedic surgical cutting instruments are required to generate sufficient forces to penetrate bone tissue, while minimizing the risk of thermal and mechanical damage to the surrounding environment. This study presents a combined experimental-computational approach to deter-mine relationships between key cutting parameters and overall cutting performance of a polyu-rethane-based synthetic trabecular bone analogue under orthogonal cutting conditions. An ex-perimental model of orthogonal cutting was developed, whereby an adaptable cutting tool fix-ture driven by a servo-hydraulic uniaxial test machine was used to carry out cutting tests on Sawbone® trabecular bone analogues. A computational model of the orthogonal cutting process was developed using Abaqus/Explicit, whereby an Isotropic Hardening Crushable Foam elas-tic-plastic model was used to capture the complex post-yield behaviour of the synthetic trabecu-lar bone. It was found that lower tool rake-angles resulted in the formation of larger discontin-uous chips and higher cutting forces, while higher rake angles tended to lead to more continu-ous chip formation and lower cutting forces. The computational modelling framework provided excellent predictions of both chip formation and axial cutting forces over the wide range of cut-ting parameters, when compared to experimental observations. This represents the first experi-mentally-validated computational modelling framework for orthogonal cutting of trabecular bone and excellent potential to be applied to more complex three-dimensional cutting processes in the future.

Bone healing has been traditionally described as a four-phase process: inflammatory response, soft callus formation, hard callus development, and remodeling. The remodeling phase has been largely neglected in most numerical mechanoregulation models of fracture repair in favor of capturing early healing using a pre-defined callus domain. However, in vivo evidence suggests that remodeling occurs concurrently with repair and causes changes in cortical bone adjacent to callus that are typically neglected in numerical models of bone healing. The objective of this study was to use image processing techniques to quantify this early-stage remodeling in ovine osteotomies. To accomplish this, we developed a numerical method for radiodensity profilometry with optimization-based curve fitting to mathematically model the bone density gradients in the radial direction across the cortical wall and callus. After assessing data from 26 sheep, we defined a dimensionless density fitting function that revealed significant remodeling occurring in the cortical wall adjacent to callus during early healing, a 23% average reduction in density compared to intact. This fitting function is robust for modeling radial density gradients in both intact bone and fracture repair scenarios and can capture a wide variety of the healing responses. The fitting function can also be scaled easily for comparison to numerical model predictions and may be useful for validating future mechanoregulatory models of coupled fracture repair and remodeling.

Defined ordered structures for biomaterials and tissue engineering applications can be achieved by a variety of techniques, one of which includes the electrohydrodynamic (EHD)-based application of melt electrowriting (MEW), the extrusion of a molten polymer filament under pressure across a defined electric field. In this study, we investigate how to translate small fibremeshes which are usually formed on flat surfaces, to curved contours that would have more applicability to human anatomical structures. By modelling the electric field strength associated with the MEW process, we found that incorporation of a non conductive three-dimensional (3D) custom printed mould on the conductive collector plate offers the ability to accurately print patterns on non-flat surfaces successfully. Importantly, while the electric field strength is a constant in the MEW process; the electrostatic behaviour of the deposited polymer has the greatest impact on the accuracy of fibre patterning and stacking. Consequently, controlled fibre deposition was exhibited, provided that a constant electrical field strength and a continuous vertical distance between the nozzle and the mould is maintained.Overall, this study establishes the groundwork to support further developments in MEW technologies, from flat to anatomically relevant 3D structures in the fields of regenerative medicine and biofabrication.

Purpose: To perform activities of daily living (ADL), people with lower limb amputation depend on the prosthetic socket for stability and proprioceptive feedback. Poorly fitting sockets can cause discomfort, pain, limb tissue injuries, limited device usage, and potential rejection. Semi-passively controlled adjustable socket technologies exist, but these depend upon the user’s perception to determine safe interfacial pressure levels. This paper presents a framework for automatic control of an adjustable transtibial prosthetic socket that enables active adaptation of residuum-socket interfacial loading through localized actuators, based on soft tissue injury risk estimation. Method: Using finite element analysis, local interfacial pressure vs. compressive tissue strain relationships were estimated for three anatomical actuator locations, for tissue injury risk assessment within a control structure. Generalized Predictive Control of multiple actuators was implemented to maintain interfacial pressure within estimated safe and functional limits. Results: Controller simulation predicted satisfactory dynamic performance in several scenarios, based on previous related studies. Actuation rates of 0.06 – 1.51kPa/s with 0.67% maximum overshoot, and 0.75 – 1.58kPa/s were estimated for continuous walking, and for a demonstrative loading sequence of ADL, respectively. Conclusion: The developed platform could be useful for extending recent efforts in adjustable lower limb prosthetic socket design, particularly for individuals with residuum sensory impairment.

This paper focuses on step length estimation using inertial measurement sensors. Accurate step length estimation has a number of useful health applications, including its use in characterizing the postural instability of Parkinson’s disease patients. Three different sensor configurations are studied using sensors on the shank and/or thigh of a human subject. The estimation problem has several challenges due to unknown measurement bias, misalignment of the sensors on the body and the desire to use a minimum number of sensors. A nonlinear estimation problem is formulated that aims to estimate shank angle, thigh angle, bias parameters of the inertial sensors and step lengths. A nonlinear observer is designed using Lyapunov analysis and requires solving an LMI to find a stabilizing observer gain. It turns out that global stability over the entire operating region can only be obtained by using switched gains, one gain for each piecewise monotonic region of the nonlinear output function. Experimental results are presented on the performance of the nonlinear observer and compared with gold standard reference measurements from an infrared camera capture system. An innovative technique that utilizes three sensors is shown to provide a step length accuracy nearly equal to that of the four-sensor configuration.

Here, an ultra-small size, simple and inexpensive metal oxide semiconductor field effect transistor (MOSFET)-integrated needle type EGFET pH microsensor was fabricated. The EGFET pH mi?crosensor has the potential to be applied to fast and precise local pH measurements. The system was composed of two components; a pH sensitive probe and a MOSFET. The pH sensitive probe wasmade by electrochemically coating the surface of a Pt ultra micro-electrode with polypyrrole, a semi-conductor polymer. The pH sensitive probe was then integrated with the gate of the MOSFET to carry out measurements in PBS at different pH values. The real time response of the EGFET pH microsen?sor was also tested by cycling the probe in three solutions at different pH. The results showed that the developed pH microsensor is sensitive to pH change. It is expected that the EGFET pH microsensor will allow local pH analysis in biological samples or corrosion studies

Bunion or Hallux valgus as a definition is one of the most common deformities in the forefoot, with a spread rate of 23% to 35%. The causes of this deformity might be hereditary or by wearing tight shoes. The bunion deformation passes through many stages; in the first stage patient might not feel pain but in the last stages s/he can have severe symptoms and complications if not treated probably. There are two types of bunion treatments; surgical and nonsurgical. However, studies have shown that surgical treatment is the best way to get rid of the bunion. This article includes a survey to investigate more about bunion in the UAE and the shoe model that reduces stress concentration in the bunion region.

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focusing on diagnosis and stratification of COVID-19 from medical images. Despite this large-scale research effort, these models have found limited practical application due in part to unproven generalization of these models beyond their source study. This study investigates the generalizability of key published models using the publicly available COVID-19 Computed Tomography data through cross dataset validation. We then assess the predictive ability of these models for COVID-19 severity using an independent new dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. The study shows high variability in the generalization of models trained on these datasets due to varied sample image provenances and acquisition processes amongst other factors. We show that under certain conditions, an internally consistent dataset can generalize well to an external dataset despite structural differences between these datasets with f1 scores up to 86%. Our best performing model shows high predictive accuracy for lung involvement score for an independent dataset for which expertly labelled lung involvement stratification is available. Creating an ensemble of our best model for disease positive prediction with our best model for disease negative prediction using a min-max function resulted in a superior model for lung involvement prediction with average predictive accuracy of 75% for zero lung involvement and 96% for 75-100% lung involvement with almost linear relationship between these stratifications., 39 Pages, 8 figures, 14 tables comparing the generalization of COVID-19 CT Deep Learning Models

Despite the potential for biomechanical conditioning with prosthetic use, the soft tissues of residual limbs following lower-limb amputation are vulnerable to damage. Imaging studies revealing morphological changes in these soft tissues have not distinguished between superficial and intramuscular adipose distribution, despite the recognition that intramuscular fat levels indicate reduced tolerance to mechanical loading. Furthermore, it is unclear how these changes may alter tissue tone and stiffness, which are key features in prosthetic socket design. This study was designed to compare the morphology and biomechanical response of limb tissues to mechanical loading in individuals with and without transtibial amputation, using magnetic resonance imaging in combination with tissue structural stiffness. The results revealed higher adipose infiltrating muscle in residual limbs than in intact limbs (residual: median 2.5% (range 0.2–8.9%); contralateral: 1.7% (0.1–5.1%); control: 0.9% (0.4–1.3%)), indicating muscle atrophy and adaptation post-amputation. The intramuscular adipose content correlated negatively with daily socket use, although there was no association with time post-amputation. Residual limbs were significantly stiffer than intact limbs at the patellar tendon site, which plays a key role in load transfer across the limb-prosthesis interface. The tissue changes following amputation have relevance in the clinical understanding of prosthetic socket design variables and soft tissue damage risk in this vulnerable group.

We present a computationally efficient full-wave spectral model of OCT-scan formation with the following capabilities/features: (i) the illuminating beam may have arbitrary phase-amplitude profile with allowance of a sharp diaphragm; (ii) paraxial approximation that limits the degree of focusing/divergence is not used; (iii) the broadly used approximation of ballistic (single) scattering by discrete scatterers is assumed without additional limitations on the density of scatterers, their distribution in space and scattering strengths with possible frequency-dependence; (iv) besides rigorous accounting for the influence of focusing/divergence of the waves, factors describing the wave decay can readily be introduced by analogy with Monte-Carlo approaches; (v) arbitrary measurement noises can easily be added to simulate required signal-to-noise ratios. The model also allows one to account for arbitrary (e.g., random or flow/deformation-induced) displacements of scatterers between subsequently generated scans. Thus, in view of the above-listed features the model can be characterized as comprehensive in the framework of ballistic scattering by discrete scatterers. The model is computationally efficient due to the use of only rapid summations and fast Fourier transforms. Main model features are illustrated, including simulations of OCT scans for a nearly non-diverging Bessel beam and a focused Gaussian beam, with the possibility to introduce at the tissue boundary arbitrary aberrations represented via Zernike polynomials often utilized for describing aberrations in ophthalmology. The unprecedented flexibility and high computational efficacy of the model open a broad range of possibilities for studying OCT-scan properties and developing new methods of their processing for biomedical diagnostics.

The deep learning leveraged FE$^2$ algorithm for two-scale modeling of elastic solids eliminates the need to solve the RVE problem on-the-fly by replacing the effective input-output causality by a neural network. This potentially reduces the computational cost of the FE$^2$ algorithm significantly. In this work, we put forth the use of snapshot proper orthogonal decomposition to improve the accuracy of the machine learning leveraged algorithm. Instead of training one neural net, multiple neural nets are trained with the coefficients of the basis of the snapshot matrix as the target.

We present a simple, low-cost and scalable method for fabricating externally pressurized paper-based microfluidic devices. Known as microfluidic pressure in paper (µPiP), devices fabricated by this technique are capable of sustaining a pressure gradient for use in precise liquid handling and manipulation applications. In addition, the simple nature of this fabrication technique allows device designs to be scalably manufactured and deployed with minimal time, equipment and training requirements. This new platform democratizes the field of microfluidics and offers budget-friendly technology for both academic device prototyping and largescale commercial device production.

Tactile shear force sensors are increasingly popular for medical applications especially for orthopedic rehabilitation such as measuring interfacial shear stresses between a residual limb and prosthetic socket to manage socket fit and residual limb tissue health, or measuring shearing between a foot sole and shoe for assessing performance in athletes. However, there are considerable challenges in implementing shear force sensors for orthopedic applications due to the requirements for noninvasiveness, light weight, low power, and robustness against motion artifacts, normal force, or electromagnetic fields. To address these challenges, this paper describes the design, fabrication, and characterization of a simple, low-cost, optoelectronic sensor that can measure multi-axial shear stresses. The sensor is based on a red, green, and blue (RGB) light-emitting diode (LED) cycling among red, green, and blue lights onto a color pattern surface. As shear strain causes a displacement between the LED and the color pattern, the relative intensities of reflected lights among the different colors change. A photodiode is used to capture the reflected light intensity at each color illumination, allowing the determination of the color pattern surface displacement, and in turn the shear, along two axes. In this paper, the efficacy of the sensor under benchtop testing conditions is reported, confirming the potential of this technology for shear monitoring at orthopedic devices such as protheses or shoes. Future efforts will focus on miniaturization and packaging of the sensors, and characterizing their performance for more medical and other types of applications.

Almost 60 years after the assassination of John F. Kennedy in 1963 the majority of Americans are still reluctant to believe the official reports of commissions from 1964 and again in 1976 that determined the direction of the shot resulting in the fatal head injury. Long-withheld, confidential government files released in 2017 reignited the controversy.The present investigation computationally simulated projectile-skull impacts from the direction specified in official reports and from three other directions. Detailed geometric models of the human head and ammunition, as well as known parameters from the assassination site served as the supportive base for analysis. Constitutive mathematical models for the impact of projectile material with skull tissues at supersonic speed were employed to analyze bone and bullet fragmentation mechanics. Simulated fracture characteristics of the bone and the bullet were compared with photographic and X-ray evidence. The most likely origin of the fatal shot was determined based on the degree of corresponding deformation and fragmentation between simulation and documented evidence. Computational corroboration could be established as physically consistent with high-speed impact from the rear, as established by the official commissions. Simulations of three other speculative shot origins did not correspond to the documented evidence.

Introduction: Loading of a residual limb within a prosthetic socket can cause tissue damage such as ulceration. Computational models and simulations may be useful tools for estimating tissue loading within the socket and thus provide insights into how interventions (e.g. prosthesis designs or rehabilitation techniques) affect residual limb-socket interface dynamics. The purpose of this study was to model and simulate residual limb-socket interface dynamics and evaluate the effects of varied prosthesis stiffness on interface dynamics during gait. Methods: A spatial contact model of a residual limb-socket interface was developed and integrated into a gait model with a below-knee amputation. Gait trials were simulated for four subjects walking with low, medium, and high prosthesis stiffness settings. The effects of prosthesis stiffness on interface kinematics, normal pressure, and shear stresses were evaluated. Mean group responses and a subject-specific case study were analyzed. Results: Model-predicted outcome values were similar to those reported previously in sensor-based experiments; however, there were no discernable effects of prosthesis stiffness on interface dynamics among the group data (p>0.05). Subject-specific data showed decreased external rotation in the low stiffness trials, though high variability was present in the data. Conclusions: These methods may be useful to aid experimental studies by providing insights into the effects of varied prosthesis design parameters or gait conditions on residual limb-socket interface dynamics. Data suggest that these effects may be subject-specific.

Measurements of intracranial brain displacement in cadaveric specimens have been instrumental to the validation finite element (FE) models of brain injury. These data collections have used radiographic and sonomicrometric techniques, requiring the use of tissue-embedded tracking markers; however, marker accuracy has never been adequately characterized. Marker tracking precision has been previously conflated with measurement accuracy, not accounting for changes in the natural responseof surrounding tissues due to marker presence. Non-negligible inertia, high stiffness, and the aspect ratio of markers all contribute to this interference. This work investigated the dynamic coupling between published marker designs (NDTs, Sonomicrometry Crystals, and Tin) and a new elastomeric marker, and a block of tissue simulant subjected to a drop impact. The measured strains were compared to the baseline response of the simulant containing massless markers. The results found notable evidence of interference in simulant strain amplitudes as well as considerable directional bias in the response of some markers. The elastomeric marker was found to have minimal interference in the deformation field. FutureFE model validation will need to account for the considerable interference and directional biases to the natural response of brain tissue in existing cadaveric datasets to maintain confidence in strain predictions.

Direct aspiration with large bore catheter as the first line option in treatment of acute Ischemic stroke (AIS) is a fast and effective technique with promising outcomes. In silico models are widely used for design and preclinical assessment of new developed devices and therapeutic methods. Accurate modelling of the mechanical behaviour of blood clot is a key factor in the design and simulation of aspiration devices. In this study we develop a new constitutive model which incorporates the unrecoverable plastic deformation of clots. The model is developed based on the deformation-induced microstructural changes in fibrin network, including the formation and dissociation of the cross-links between fibrin fibres. The model is calibrated using previously reported experimentally measured permanent clot deformation following uniaxial stretching. The calibrated plasticity model is then used to simulate aspiration thrombectomy. Results reveal that inclusion of permanent plastic deformation results in ~15 % increase in clot aspiration length at an applied aspiration pressure of 200 mmHg. The constitutive law developed in this study provides a basis for improved design and evaluation of novel aspiration catheters leading to increased first-pass revascularization rate.

Introduction: Simulations based on computational musculoskeletal models are powerful tools for evaluating the effects of potential biomechanical interventions, such as implementing a novel prosthesis. However, the utility of simulations to evaluate the effects of varied prosthesis design parameters on gait mechanics has not been fully realized due to lack of a readily-available limb loss-specific gait model and methods for efficiently modeling the energy storage and return dynamics of passive foot prostheses. The purpose of this study was to develop and validate a forward simulation-capable gait model with lower limb loss and a semi-active variable-stiffness foot (VSF) prosthesis. Methods: A seven-segment 28-DoF gait model was developed and forward kinematics simulations, in which experimentally-observed joint kinematics were applied and the resulting contact forces under the prosthesis evolved accordingly, were computed for four subjects with unilateral below-knee amputation walking with a VSF. Results: Model-predicted resultant ground reaction force (GRFR) matched well under trial-specific optimized parameter conditions (mean R2: 0.97, RMSE: 7.7% body weight (BW)) and unoptimized (subject-specific, but not trial-specific) parameter conditions (mean R2: 0.93, RMSE: 12% BW). Simulated anterior-posterior center of pressure demonstrated a mean R2 = 0.64 and RMSE = 14% foot length. Simulated kinematics remained consistent with input data (0.23 deg RMSE, R2 > 0.99) for all conditions. Conclusions: These methods may be useful for simulating gait among individuals with lower limb loss and predicting GRFR arising from gait with novel VSF prostheses. Such data are useful to optimize prosthesis design parameters on a user-specific basis.

Blast injuries remain a serious threat to defence and civilian populations around the world. ‘Primary’ blast injuries (PBIs) are caused by direct blast wave interaction with the human body, particularly affecting air-containing organs. Work to define blast loading conditions for injury research has received relatively little attention, though with a continued experimental focus on PBIs and idealised explosion assumptions, meaningful test outcomes and subsequent clinical applications, rely on appropriate simulated conditions. This paper critically evaluates and combines existing PBI criteria (grouped into those affecting the auditory system, pulmonary injuries and brain trauma) as a function of idealised blast wave parameters. For clinical blast injury researchers, analysis of the multi-injury criteria indicates zones of appropriate loading conditions for human-scale test items and demonstrates the importance of simulating blast conditions that are both realistic and relevant to the injury type. For certain explosive scenarios, spatial interpretation of the ‘zones of relevance’ could support emergency response and hazard preparedness by informing triage, patient management and resource allocation, thus leading to improved health outcomes. This work will prove useful to clinical blast injury researchers, blast protection engineers and clinical practitioners involved in the triage, diagnosis, and treatment of PBIs.

Transparent interaction, or the reduction of human-robot interaction forces, is an important quality of gait training exoskeletons. In this paper, we investigate the feasibility of using a repetitive controller for reducing impedance of gait training exoskeletons using force feedback. We used a two-mass spring damper model system, and simulated the application of repetitive force controllers with the objective of reducing the end-point impedance of the distal mass. We designed and applied three repetitive controllers: a 1st order, a 2nd order designed for random signal period error, and a 2nd order designed for constant signal period error. We compared these three repetitive controllers subject to plant model parameter error, random signal period error, and constant signal period error. Numerical simulations under nominal conditions show that via repetitive force control, it is possible to reduce the endpoint impedance to the targeted magnitude and RMSE force below the limit achievable with force controllers while guaranteeing passivity. Furthermore, we established that the application of a 2nd order repetitive controller designed for random period error is highly robust to random period error - exceeding the performance of the passive proportional controller up to 30% error of nominal frequency. Furthermore, this 2nd order repetitive controller designed for random period error maintains a 100% convergence rate through 60% plant parameter error.

Inductive circuits and devices are a ubiquitous and important design element in many applications such as magnetic drives, galvanometers, magnetic scanners, applying DC magnetic fields to systems, RF coils in NMR systems and vast array of other applications. They are widely used to generate both DC and AC magnetic fields. Many of these applications require a rapid step and settling time, turning the DC or AC magnetic field on and off quickly. The inductive response normally makes this a challenging thing to do. In this article we discuss open loop control algorithms for achieving rapid step and settling times in four general categories of applications: DC and AC systems where the system is either under or over damped. Each of these four categories requires a different algorithm which we describe here. We show the operation of these drive methods using Simulink and Simscape modeling tools, analytical solutions to the underlying differential equations and in experimental results using an inductive magnetic coil and a Hall sensor. Finally, we demonstrate application of these techniques to significantly reduce ringing in a standard NMR circuit. We intend this article to be practical with useful, easy to apply algorithms and helpful tuning tricks.