Journal of Mechanics in Medicine and Biology

The interaction between cells and colloids is an important characteristic that influences cell behavior. Theoretically, much information could be revealed by analyzing the interactions in colloid–cell contact. In this study, in order to explore the interaction between cells and colloids, we developed a novel computational method able to obtain a zeta potential directly calculated from the force distance curve and apply to adhesion analysis, which used atomic force microscope (AFM), based on DLVO (Deryaguin–Landau–Verwey–Overbeek) theory and Mann Whitney U test, and combined with Zetasizer measurement. The calculation and analysis of $\zeta$ of the cell surfaces of ncyc-1324 yeast, ncyc-1681 yeast and Pseudomonas fluorescens showed that pH affected the electrostatic distribution on the cell surface. Compared with the previous research methods, this method significantly reduces the computation and manual control, which is an effective method for multi-element surface analysis and comparison. For example, the reverse calculation and curve fitting method will significantly request more computation and manual control to set up the reference force curve that simulated with set zeta potential, while this method only need to calculate on one force curve. The deconvolution of different adhesion events from force curves showed that the heterogeneity of cell surface can be significantly displayed. This provides a method for determining the complexity of the cell surface. Furthermore, this method was used to study the effect of amoxicillin on cell surface interaction, which showed that the cells surface forces were influenced even the medicine concentration is not enough to make significant influence on microbials optical observation appearance. Thus, AFM force analysis is a more sensitive method to research the medicine influence compared to the traditional method.

Viruses that cause infections spread very quickly and has a fatal risk to people with chronic diseases. Since the virus vaccine and the drugs to be used in treatment are not fully developed, alternative ways to protect from the virus are being investigated. The Covid-19 virus has been recognized by the World Health Organization (WHO) as a pandemic. In this study, the effect of face mask shield against Covid-19 and other infections were investigated using finite element analysis (FEA). Three-dimensional model of the conventional face mask, equipment and shield was performed with the SolidWorks software. Computer-aided simulations were performed using AnsysWorkbench explicit dynamics module. The loading, boundary conditions and material properties were defined in the AnsysWorkbench. The effects of droplets formed because of cough or sneezing on the human model with mask and shield were analyzed. It has been confirmed from the analyzes that both the mask and the shield are effective against the virus.

The procedure involved in evolving a compound that was utilized for producing prosthetic foot with mechanical characteristics and a sensible price is presented. Compound’s characteristics such as Charpy impact strength and tensile were investigated using mechanical tests. During this procedure 10% linear low density polyethylene (LLDPE) was added to 90% high-density polyethylene (HDPE). This reduced the energy impact as well as ultimate yield stress whilst elongated at break from 27% in comparison to those of pure HDPE. Prostheses properties were further studied in comparison to human foot. The analytical part indicates the static analysis outcomes by experimental as well as finite element methods (ANSYS Workbench 14). Thus, the foot was formed, and dorsiflexion was measured. Overall, the nonarticulated (HDPE and LLDPE) foot was compared to SACH foot by cost and weight. It was found that the nonarticulated foot’s weight was lighter by 2% and its cost was lower than SACH foot by approximately 60%. The nonarticulated foot has great properties, for instance a life of foot cycles (9^∘ and 2,193,228) as well as good dorsiflexion in comparison to SACH foot with 6.4$\circ$ and 896,213 life of foot cycles.

Biodegradable materials have various advantages compared to nonbiodegradable materials. Developing implants using biodegradable materials eliminates the need for secondary surgery, improves mechanical and biological properties, and improves biocompatibility. Magnesium (Mg) and its alloys are frequently used in orthopedic applications nowadays. However, the rapid degradation of Mg poses a substantial challenge. As a result, for the bone to heal properly, a proper balance between implant degeneration rate and bone healing must be obtained. Mg has certain other drawbacks, such as the need for an inert atmosphere when employing powder metallurgy and casting procedures to manufacture it because of its reactive nature. In this paper, Additive manufacturing (AM) techniques for manufacturing orthopedic biodegradable implants made of Mg and its alloys are discussed which helps in obtaining improved biological and mechanical properties of the implants. These orthopedic implants should have a controlled rate of degradation and antibacterial functional surfaces. There is also a description of the use of several AM processes utilized to enhance the mechanical and biological characteristics of implants employing Mg. This paper also seeks to present the concept of integrating established techniques into a production process to obtain the needed biodegradable implant material for orthopedic applications.

Human activity intention is indispensable for wearable powered lower extremity exoskeleton such that ensuring the compliant control of the robot. Lots of researches have been done on gait phase detection, which served as a sub-module of locomotion mode recognition to support the follow-on task. Therefore, it is self-evident that locomotion mode recognition is of great importance. Many model-based recognition methods are usually applied in manual extraction of cumbersome features, such as the traditional neural network (NN), support vector machine (SVM), etc. In contrast, the feature mapping layer coming with the convolutional neural network (CNN) can effectively solve the above time-consuming problem. Given that the training of NN is prone to overfitting, SVM with optimal characteristics is considered. A hybrid CNN–SVM model is proposed to identify human locomotion modes by collecting multi-channel inertial measurement unit (IMU) signals and is integrated with the error correction function of the finite state machine (FSM). Therefore, the CNN–SVM model has great influence on the generalization performance and recognition accuracy. The recognition rates of five single locomotion modes and eight mixed locomotion modes reach 97.91% and 98.93%, respectively. The system meets the demand of real-time performance, and the recognition time exceeds 370ms on heel strike.

Phonocardiogram (PCG) signals are contaminated with various noise signals, which hinders the accurate diagnostic interpretation of the signal. Discrete wavelet transform (DWT) is a well-known technique used to remove noise from PCG signals and improve signal quality. The performance of DWT-based denoising depends upon several parameters involved in the process, such as mother wavelets used for decomposition, the number of decomposition levels (DLs), thresholding technique used and the threshold estimation rule followed. In this work, an investigative study is carried out to select the optimal parameter values which give the best denoising performance. The metrics such as mean-square error (MSE), normalized-mean-square error (NMSE), root-mean-square error (RMSE), percentage root-mean-square difference (PRD) and signal-to-noise ratio (SNR) are used to evaluate the performance of the denoising in this study. The results obtained show that the fifth-order Coiflet wavelet is best suited for denoising PCG signals when applied with the soft thresholding (ST) function and rigrsure threshold selection rule. Also, the optimum number of DLs resulting in better performance is level 6. SNR value obtained from the studies shows the efficacy of the parameters selected for denoising. The denoised PCG signals provide accurate information to determine various kinds of heart valve-related disorders (HVDs).

A cycling rehabilitation robot is developed to assist in rehabilitation training for stroke patients. The robot can realize flexion movements of the patient’s hip, knee and ankle. First, based on physiological principles, the range for flexion motion angles of the lower limbs is presented. The mathematical modeling of the lower limb rehabilitation motion angles is established. The angular motion characteristics of the lower limb hip and knee joints are analyzed, and the relationship of the motion between the lower limb hip and knee joints is presented. To verify the correctness of the theoretical model, the structure of the lower limb rehabilitation training robot is designed. The experiments show that the rehabilitation robot can achieve flexion rehabilitation training of the lower limbs effectively, which proves the correctness of the proposed theoretical model. The results provide scientific guidance for patients using cycling rehabilitation devices.

The lower limb rehabilitation process requires external control inputs based on joint kinematics and kinetics to its actuator for generating the motion of a robotic assistive device. This paper describes the procedures for measuring, predicting and validating the joint kinematics and kinetics of human gait for the lower limb exoskeleton. The high-speed multi-camera-based video system associated with passive optical markers and a force plate sensor is used to measure the gait data. The lower limb joint torques have also been predicted using three different machine learning regression models, viz. Decision Tree (DT), Gaussian Process Regression (GPR) and Support Vector Machine (SVM). The analysis is further validated with the geometry-based analytical method. Three lower limb joint torques have been predicted by utilizing gait period, joint angle, joint velocity and joint acceleration as the predictors. The lowest average 10-fold cross-validation Root Mean Square Error (RMSE) using the DT model between measured and predicted torques have been recorded for the ankle, knee and hip joint as 1.9521, 1.5527 and 1.5684Nm, respectively. This study enhances the field of medical rehabilitation by providing the required knowledge of human gait kinematics/kinetics and predicting the joint torque profile without any force sensor.

This paper presents the effects of using two or four mini-implants to support a mandibular overdenture for edentulous patients. The stresses on the mini-implants and the strains on the peri-implant and surrounding bone were evaluated and compared to known values of strength for the mini-implants and physiologic bone strain to potentially stimulate and maintain bone volume of the edentulous ridge. Two finite element models created were identical with the exception of the number of denture-supporting mini-implants. Both models contained modeled mucosa, cortical bone, cancellous bone, and two anterior mini-implants (approximating the position of the mandibular canine teeth). The second model also contained two posterior mini-implants (in the molar region). Clinical masticatory loading of three 70N loads bilaterally was used. The anterior mini-implants in both models showed stresses below the material’s fracture threshold. In contrast, posterior mini-implants experienced stresses above the implant’s yield limit, indicating that the posterior mini-implants could be at risk for failure. Strains in the peri-implant and surrounding bone around the posterior mini-implants were above the physiologic limit, indicating that this region might be at risk for bone resorption. The addition of the posterior mini-implants did not appear beneficial to maintain edentulous bone. Based on the limitations of this study, anterior mini-implants can be safely used for a mandibular overdenture whereas posterior mini-implants may not be a wise treatment option for edentulous patients due to risks of implant fracture and pathologic bone resorption. Dentists must weigh risks and benefits of using posterior mini-implants placed in the molar region.

Breast tumor is one of the causes of women’s death in the world after cardiovascular diseases. Recently, the diagnosis and treatment planning of this kind of tumors are based on magnetic resonance imaging techniques, which are the reference imaging modality in breast tumors analysis since it can better differentiate soft tissues (compared to mammography and ultrasound). Segmentation of the breast cancer is a very important task for cancer response prediction in neoadjuvant chemotherapy treatment based either on texture analysis or parametric response maps. In most of the previous works in the literature, the segmentation is generally done with manual annotation of tumor regions, which is time-consuming and error-prone. In this paper, we propose a new strategy for an automatic segmentation of breast tumors in MRI images. We propose first to separate the two breasts, and then, we use the Expectation–Maximization Algorithm to segment and detect the tumor lesion.