Journal of Biomimetics, Biomaterials and Biomedical Engineering Vol. 64

Abstract: Gold nanoparticles (AuNPs) have garnered significant interest in the field of biomaterials and biomedical engineering due to their wide-ranging applications, excellent biocompatibility, low toxicity, and customizable stability. This study focuses on synthesizing AuNPs through an environmentally friendly approach, specifically by utilizing the aqueous leaf extract of Allium tuberosum as both a reducing and capping agent. The synthesized AuNPs were characterized using UV-Vis Spectroscopy, revealing an absorption peak at 548 nm within the surface plasmon resonance (SPR) of AuNPs. Morphological analysis conducted via SEM showed a mixture of rod-shaped and spherical-shaped AuNPs, with dimensions of 41.0 nm (width) and 181.6 nm (length) confirmed through DLS measurements. EDX analysis confirmed the high abundance of gold in the synthesized AuNPs. Furthermore, a zeta potential value of -26.2 mV indicates that the AuNPs have decent stability. Phytochemical analyses and FT-IR results implicated that the Saponin present in the Allium tuberosum leaf extract played a crucial role in reducing metal ions and stabilizing the AuNPs. The potential of Allium tuberosum leaf extract for synthesizing diverse metal nanoparticles highlights its promise for biomaterials and biomedical engineering. The synthesized AuNPs show versatility for applications like targeted drug delivery, non-invasive imaging, and emerging biomedical uses.

Abstract: The crucial role of saliva in the prevention of dental caries is associated with the regulation of its flow rate as well as salivary protein. Black tea, derived from the plant Camellia sinensis, contains catechins and has been shown to have a beneficial effect on saliva in preventing tooth decay. Nevertheless, research on the oral health benefits of black tea is still limited. This study aimed to investigate the effects of black tea (Camellia sinensis) on salivary pH (potential hydrogen), salivary flow rate, lysozyme, and sIgA levels in caries and caries-free patients. The study used a quasi-experimental design with a pre-test and a post-test. Twenty six subjects (male or female) were selected and divided into two groups (caries and caries free), thirteen subjects for each. Saliva samples were obtained before and thirty minutes after tea consumption. The flow rate of saliva was measured by dividing the weight of saliva collected by the time. At the same time, secretory immunoglobulin A (sIgA) levels were analyzed by enzyme-linked immunosorbent assay (ELISA). The t-dependent and t-independent tests and Mann-Whitney tests were selected to evaluate the effect of drinking black tea on salivary flow rate and sIgA value. The results showed that black tea significantly increased salivary flow rate but did not significantly affect the concentration of sIgA. No statistically significant differences in salivary flow rate and sIgA were observed between patients with and without caries after tea consumption. Although the concentration of sIgA did not demonstrate a significant change, however, the salivary flow rate was significantly enhanced. Therefore, drinking black tea did not negatively affect the saliva in the oral cavity, suggesting it can be a good option for daily consumption due to its protective role against dental caries.

Abstract: Orthopedic cement is an essential component of cemented Total Hip Replacements (THR). It must ensure three essential functions: very good implant-cement adhesion, good bone-implant load transfer, and good antibiotic transport. The main objective of the present work is to study the fracture behavior of orthopedic cement in total hip replacements. The analysis is performed using the submodel technique. Two cases are being considered. The first case involves ordinary cracks, while the second case involves cracks emanating from cavities in the cement of the THR acetabular part. The effects of crack position and implant orientation on the variation of stress intensity factors (SIF) in the three failure modes are discussed. It has also been shown that the circumferential positions of cracks present a major risk of loosening of the prosthesis, especially when the defect is aligned with its axis.

Abstract: In the context of this numerical study is particularly to analyze and observe the effect of mechanical properties and masticatory efforts on the intensity and distribution of biomechanical stresses induced in the mandibular bone (the cortical bone, the spongy bone) and in the elements which constitute the structure of the dental bridge (abutments, implants, bridge). The 3D model studied is subjected to loading in the three directions of space (corrono-apical, disto-medial, bucolingual). The numerical analysis allowed us to highlight the localization of the stress concentration zones, on the one hand, at the level of the regions of contact between the elements of the dental bridge structure and on the other hand, at the level of the mandibular bone. This parametric approach for the mechanical properties of bridges is used to better visualize and quantify the biomechanical behavior of dental bridges.

Abstract: To improve the human-machine cooperativity of a wearable lower limb exoskeleton, a gait recognition method based on surface electromyography (sEMG) was proposed. sEMG of rectus femoris, vastus medialis, vastus lateralis, semitendinosus and biceps femoris were acquired. Then, time domain, frequency domain, time-frequency domain and nonlinear features were extracted. The integrated value of electromyography, variance, root mean square and wavelength were selected as the time domain features and the frequency domain feature includes mean power frequency. Wavelet packet energy was selected as the time-frequency domain feature. Nonlinear features including approximate entropy, sample entropy and fuzzy entropy of sEMG were extracted. Classification accuracy of different feature matrices and different muscle groups were constructed and verified. The optimal multi-dimensional fusion feature matrix was determined. Introducing the Bayesian optimization algorithm, the Bayesian optimized Random Forest classification model was constructed to identify different gait phases. Comparing with Random Forest, the accuracy of the optimized Random Forest was improved by 5.89%. Applying Random Forest algorithm with Bayesian optimization to gait prediction based on sEMG, the followership and consistency of gait control in lower limb exoskeleton can be improved. This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text.

Abstract: The use of bionic drag-reducing microstructures in artificial blood vessels can effectively reduce their resistance to blood flow. The characteristics of the blood vessel are analysed and simplified, and the resistance reduction effects of three bionic microstructure models, namely V-shaped, rectangular and semi-circular, are compared and analysed by numerical simulations, and the resistance reduction effects of the three groove structures in the tubular model are verified. The results show that the V-shaped groove structure occupies a smaller volume compared to the rectangular and semi-circular structures of the same size, has a significant drag reduction effect, is highly achievable and stable, and is the best choice as a drag reduction microstructure for artificial blood vessels. In addition, the wall shear stresses of the V-groove structure were further analysed to verify the shear effect of this microstructure in artificial blood vessels and to reveal the shear mechanism of the V-groove microstructure.

Abstract: Sleep apnea (SA) is considered one of the most dangerous sleep disorders. That happens when a person is sleeping, his or her breathing repeatedly stops and starts. In order to develop therapies and management strategies that will be effective in treating SA, it is critical to precisely diagnose sleep apnea episodes. In this study, the single-lead electrocardiogram (ECG), one of the most physiologically pertinent markers for SA, is analyzed to identify the SA issue. In this paper, a novel signal processing method is proposed, in which noise filtering is added and the detection of R peaks is utilized. Particularly, the Teager Energy Operator (TEO) algorithm is applied to detect R peaks and then obtain the RR intervals and amplitudes. Afterward, the SE-ResNeXt 50 deep learning model, which has never been used in SA detection before, is used as a classifier to perform the objective. The proposed model, which is a variation of ResNet 50, has the ability to use global information to highlight helpful information while allowing for feature recalibration. In order to confirm the proposed method, the benchmark dataset PhysioNet ECG Sleep Apnea v1.0.0 is used. Results are better than current research, with 89.21% accuracy, 90.29% sensitivity, and 87.36% specificity. This is also clear evidence that the ECG signals can be taken advantage of to efficiently detect SA.

Abstract: Conclusions in literature regarding the effect of Autism on the size of different brain structures are contradictory. The aim of this study is to determine the effect of Autism on the volumes of different brain subcortical structures, and the age stage at which those changes occur. 7 main brain structures were segmented and their volumes were obtained. Volumes and the ratio of the volume to total brain volume (SBR) were compared in Autism group to their corresponding values in Control group. Then, each group was divided into 4 subgroups based on age; the comparison was repeated for each subgroup. Independent t-test was used to determine if significant differences existed between compared groups. Significant reductions were observed in the SBR of Autistic Pallidum and Accumbens compared to Control group when considering the full range of ages (5–25 years). However, Amygdala’s volume was significantly smaller in Autism in the 5–8 year subgroup. In addition, the SBR of Putaman, Pallidum, Hippocampus, and Accumbens were reduced in the 18–25 year Autism subgroup. In conclusion, the alteration in the ratio of structure’s volume to total brain volume is a better indicator of Autism diagnosis than change in the absolute volume alone.

Abstract: This research paper investigates the application of a Double Gate (DG) Tunnel Field-Effect Transistor (DG-TFET) for the detection of cell lines derived from breast cancer tissue, namely Hs578T, MDA-MB-231, MCF-7, and T47D. The device incorporates two nanocavities positioned beneath the two gate electrodes, significantly enhancing detection capabilities. The study emphasizes the differentiation between healthy non-tumorigenic cells (MCF-10A) and breast cancer-derived cell lines by incorporating gate engineering into the TFET. Furthermore, the research explores the impact of changes in dielectric values specific to different breast malignant cell types on the biosensor's detection capabilities. Additionally, the investigation delves into the influence of variations in device geometry, including cavity dimensions and dielectric layer thickness, on critical parameters such as drain current sensitivity, transconductance sensitivity, and ION/IOFF sensitivity. Sensitivity analysis concerns drive current, ION/IOFF ratio, threshold voltage (Vth), and transconductance. The structural design of the device is tailored to facilitate array-based diagnosis and screening of cell lines derived from breast cancer tissue. This design offers several advantages, including a simplified transduction process, compatibility with CMOS processes, cost-effectiveness, reproducibility, and adjustable electrical responses. The researchers employed ATLAS, a two-dimensional (2D) device simulator from Silvaco, to model and define the device structure. The numerical simulations validate the device's performance, demonstrating favorable ON-OFF transition profiles.