Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine最新文献

Bone is a highly heterogeneous and anisotropic material with a hierarchical structure. The effect of diaphysis locations and directions of loading on elastic-plastic compressive properties of bovine femoral cortical bone was examined in this study. The impact of location and loading directions on elastic-plastic compressive properties of cortical bone was found to be statistically insignificant in this study. The variances of most of the compressive properties were also observed to be location and directionality independent except for the locational differences in modulus of resilience (distal to central for longitudinal loading) and plastic work (central to distal for transverse loading) as well as differences in variances of the modulus of resilience and elastic modulus values for two directions of loading. The micro-mechanisms of cortical bone failure for longitudinal and transverse directions of loading were considered to be responsible for the difference in variances in the later properties values as well as for the maximum and minimum coefficient of variation (CV) obtained for compressive properties in two directions of loading. The representative cubical volume at the tested hierarchical level contained all unique microstructural features of the plexiform bone and therefore produced the homogeneous and isotropic elastic-plastic compressive properties of cortical bone. It is expected that the outcome of this study may be helpful in the area of bone tissue engineering and finite element simulation of cortical bone.

Subject-specific finite element models of knee joint contact mechanics are used in assessment of interventions and disease states. Cartilage thickness distribution is one factor influencing the distribution of pressure. Precision of cartilage geometry capture varies between imaging protocols. This work evaluated the cartilage thickness distribution precision needed for contact mechanics prediction in models of the tibiofemoral joint by comparing model outputs to experimental measurements for three cadaveric specimens. Models with location-specific cartilage thickness were compared to those with a uniform thickness, for a fixed relative orientation of the femur and tibia and with tibial freedom of movement. Under constrained conditions, the advantage of including location-specific cartilage thickness was clear. Models with location-specific thickness predicted the proportion of force through each condyle with an average error of 5% (compared to 27% with uniform thickness) and predicted the experimental contact area with an error of 21 mm² (compared to 98 mm² with uniform thickness). With tibial freedom, the advantage of location-specific cartilage thickness not clear. The attempt to allow three degrees of relative freedom at the tibiofemoral joint resulted in a high degree of experimental and computational uncertainty. It is therefore recommended that researchers avoid this level of freedom. This work provides some evidence that highly constrained conditions make tibiofemoral contact mechanics predictions more sensitive to cartilage thickness and should perhaps be avoided in studies where the means to generate subject-specific cartilage thickness are not available.

This paper creates 3D models of Kitchon Root Controlled Auxiliary Archwire (Kitchon-RCAA) with different material properties and assembles them onto the main archwire equipped with brackets. By setting different loading methods and conducting Finite Element Analysis (FEA), the range of Orthodontic Torque/Support Force (OT/SF) values can be obtained. From the obtained values, it can be seen that changes in material properties have a significant impact on the mechanical properties of Kitchon-RCAA. When the properties of the Kitchon-RCAA material change two or more times, the mechanical values generated by Kitchon-RCAA cannot be directly added from two or more separate changes in the properties of the material. Therefore, it is necessary to simulate the model after each parameter change to obtain new results. And then the maxillary bio-model is reconstructed in reverse based on Cone Beam Computerized Tomography (CBCT) images. The biomechanical data equivalent to the mechanical mechanics generated by the root control assisted archwire is also added to the corresponding tooth positions, making indirect orthodontic behavior of Kitchon-RCAA on teeth possible. From the obtained results, it can be seen that the von Mises stress and total deformation magnitude for both normal teeth and corresponding Periodontal Ligament (PDL) position show a stable trend, while the Right Cuspid (R-C) and corresponding PDL with malformed root have a large stress concentration and may have a mold penetration problem. Overall, this paper not only analyses the mechanical behavior of the Kitchon-RCAA, this article not only analyzed the mechanical behavior of Kitchon-RCAA, but also its effect on the indirect biomechanical behavior of the teeth and PDL. And in combination with simulation result nephograms, it also enables predictability and visualization of orthodontic results. This helps dentists to provide safer and more reliable individualized orthodontic treatment plans for patients.

High-efficiency and high-quality sterilization technologies for medical materials can significantly reduce iatrogenic infection. This study investigates the synergistic effects of laser-induced periodic surface structures (LIPSS) and ultrasonic cleaning on the removal of bacteria from medical material surfaces. We specifically examined how ultrasonic parameters and structural defects in LIPSS impact the effectiveness of bacterial removal. As an emerging medical metal, Zr-BMG was chosen for the target material. Femtosecond laser processing was employed to create LIPSS with both complete linear arrays and discontinuous linear arrays structures featuring surface defects by adjusting the scanning overlap rate. A high-concentration solution of S. aureus was used for co-cultivation, resulting in a surface bacterial coverage rate exceeding 95%. The study analyzed the synergistic sterilization effect of microstructured surfaces through variations in ultrasonic cleaning power and duration. The results indicated that surfaces with microstructures demonstrated significantly improved bacterial removal following ultrasonic cleaning. The bacterial removal rate was found to be proportional to the ultrasonic vibrator power, and the surface with a LIPSS structure outperformed the discontinuous LIPSS surface in bacterial removal efficiency. Optimal results were achieved with the LIPSS surface after 30 min of cleaning at 100 W ultrasonic power. However, there was minimal difference in bacterial removal between 10 and 30 min at the same power level. This study aims to provide methodological insights and data support for the efficient and high-quality cleaning of medical metal surfaces.

Human motion has been analyzed for decades based on experimentally collected subject data, serving various purposes, from enhancing athletic performance to assisting patients' recovery in rehabilitation and many individuals can benefit significantly from study advancements. Human motion prediction, is a more challenging task because no experimental data are available in advance, particularly concerning repetitive tasks, such as box lifting and tossing, to prevent injury risks. Tossing, a common task in various industries, involves the simultaneous vertical and horizontal movement of objects but often results in bodily strain. This paper presents an optimization-based method for predicting two-dimensional (2D) symmetric tossing motion without relying on experimental data. The method employs sequential quadratic programming, which optimizes dynamic effort by incorporating both static and dynamic joint torque limits. To validate the proposed model, experimental data were collected from 10 subjects performing tossing tasks using a motion capture system and force plates. The predicted joint angles and ground reaction forces considering dynamic joint strength constraints were compared with their corresponding experimental data to validate the model. In addition, the predicted joint torques differences are compared between joint dynamics strengths and static strengths. The results showed that the predicted optimal tossing motions range between the maximum and minimum of the experimental standard deviation for kinematic data across all subjects and the ground reaction forces are also within the experimental data range. This supports the validity of the prediction model. The findings of this study could have practical applications, especially in preventing the potential risk of injuries among workers who have daily tossing jobs.

Repair and regeneration of damaged tissues due to disease and accidents have become a severe challenge to tissue engineers and researchers. In recent years, biocompatible metal materials such as stainless steels, cobalt alloys, titanium alloys, tantalum alloys, nitinol, and Mg alloys have been studied for tissue engineering applications; as suitable candidates in orthopedic and dentistry implants. These materials and their alloys are used for load-bearing and physiological roles in biological applications. Due to the suitable conditions provided by a porous material, many studies have been performed on the porous implants, including Mg-based scaffolds. Mg alloy scaffolds are attractive due to some outstanding features and susceptibilities, such as providing a cell matrix for cell proliferation, migration, and regeneration, providing metabolic substances for bone tissue growth, biocompatibility, good biodegradability, elastic modulus comparable to the natural bone, etc. Accordingly, in the present study, a general classification of all the production methods of Mg-based scaffolds is provided. Strengths and weaknesses, the effect of the production approach on the final properties of scaffolds, including mechanical and biological capabilities, and the impact of alloying elements and process parameters have been reviewed, and discussed. Finally, the manufacturing methods have been compared and the upcoming challenges have been stated.

During the robotic grinding of vertebral plates in high-risk laminectomy procedures, programmed operations may inadvertently induce force or temperature-related damage to the bone tissue. Therefore, it is imperative to explore a control methodology aimed at minimizing such damage during the robotic grinding of vertebral plate cortical bone, contingent upon optimal grinding parameters. Initially, predictive models for both the grinding force and temperature of vertebral plate cortical bone were developed using the response surface design (RSD) methodology. Subsequently, employing the satisfaction function approach, multi-objective parameter optimization of these predictive models was conducted to ascertain the optimal combination of parameters conducive to low-damage grinding. The optimum grinding parameters identified were a speed of 6000 r/min, a depth of grind of 0.4 mm, and a feed rate of 3.8 mm/s. Moreover, a multi-layer adaptive fuzzy control strategy was devised, and a corresponding multi-layer adaptive fuzzy controller (MFLC) was then implemented to dynamically adjust the grinding feed speed. The efficacy of this control module was corroborated through Simulink simulations. Simulation results demonstrated that the magnitude of the grinding force fluctuated within the range of 2.2-2.6 N after FLC control, while the fluctuation range of the grinding force was limited to 2.2-2.48 N after MFLC control. This indicates that MFLC control brings the force closer to the target expectation value of 2.39 N compared with FLC control. Finally, the dynamic fuzzy control method predicated on optimal grinding parameters was validated through experimental porcine spine grinding conducted on a robotic vertebral plate grinding platform.

Arterial stiffness has emerged as a prominent marker of risk for cardiovascular diseases. Few studies are interested in predicting symptomatic or asymptomatic arterial stiffness from hemodynamics and biomechanics parameters. Machine learning models can be used as an intelligent tool for arterial stiffness detection based on hemodynamic and biomechanical parameters. Indeed, in the case of arterial stiffness hemodynamics and biomechanics parameters present significant change, such as an increase in age, local wave velocity, arterial elastance, Young's modulus, reflected wave amplitude, decrease in arterial compliance, reflected wave arrival time, and reflection coefficient. This study aims to assess the impact of artificial intelligence using machine-learning algorithms for the detection of arterial stiffness. The ability of various machine-learning approaches can be investigated to predict wall stiffness in the carotid artery and to evaluate the risk of cardiovascular events. A mathematical model developed in previous work was used to determine hemodynamic and biomechanical parameters. Accuracy, sensitivity, and specificity are calculated to evaluate the performance of the proposed models. All used classifiers demonstrated high performance in predicting arterial stiffness, notably with the Support Vector Machine, Artificial Neural Network, and Decision Tree classifiers achieving exceptional accuracies of 100%. In this study, the potential of machine learning based on hemodynamic parameters for the prediction of symptomatic and asymptomatic arterial stiffness was demonstrated.

Similar to how fiber orientation affects composite materials, osteon orientation affects the elasticity and fracture behavior of cortical bone. The objective of this work is to predict the combined effect of orientations of the osteon, applied load, and various crack lengths on the fracture characteristics of cortical bone. Orthotropic modeling and analyses of cortical bone were carried out using the linear-elastic fracture mechanics (LEFM) based extended finite element method (XFEM). Five values of applied mode-I and mode-II load, five distinct crack lengths, and seven angular osteon orientations were taken into consideration to predict the change in SIF. In this work, the 2-D plane stress assumption with a straight-edge crack was taken into consideration. It was found that the values of SIF significantly increased when the load (15-35 MPa) and fracture length (1.8-2.2 mm) increased. SIF (K_I) values under mode-I loading were discovered to be substantially lower than SIF (K_I and K_II) values under mode-II loading. Results of this study showed that osteon orientations with different crack lengths and applied loads had a significant impact on cortical bone fracture characteristics. Only the crack's opening was discovered to be caused by mode-I loading; however, both the opening and shearing of the crack were found to be caused by mode-II loading. Despite differences in applied loads, crack lengths, and osteon orientations, the values of the SIF predicted in this work (under mode-I loading) using LEFM-based XFEM exhibited good agreement with the prior published experimental and numerical data.

This investigation attempts to propose a novel Wavelet and Local Binary Pattern-based Xception feature Descriptor (WLBPXD) framework, which uses a deep-learning model for classifying chronic infection amongst other infections. Chronic infection (COVID-19 in this study) is identified via RT-PCR test, which is time-consuming and requires a dedicated laboratory (materials, equipment, etc.) to complete the clinical results. X-rays and computed tomography images from chest scans offer an alternative method for identifying chronic infections. It has been demonstrated that chronic infection can be diagnosed from X-ray images acquired in a real-world setting. The images are transformed using the discrete wavelet transform (DWT), combined with the local binary pattern (LBP) technique. Pre-trained deep-learning models, such as AlexNet, Xception, VGG-16 and Inception Resnet50, extract the features. Subsequently, the extracted features are fused using feature-fusion approaches and subjected to classification. The AlexNet, in conjunction with the DWT model, produced 99.7% accurate results, whereas the AlexNet and the LBP model produced 99.6% accurate results. Therefore, the proposed method is efficient as it offers a better detection accuracy and eventually enhances the scope of early detection, thus assisting the clinical perspectives.