Frontiers in Bioengineering and Biotechnology最新文献

Introduction: In the field of orthopedic surgery, the notched continuum robot has garnered significant attention due to its passive compliance, making it particularly suitable for procedures in complex and delicate bone and joint regions. However, accurately modeling the notched continuum robot remains a significant challenge.

Methods: This paper proposes a high-precision mechanical modeling method for the notched continuum robot to address this issue. The flexible beam deflection prediction model based on the beam constraint model is established. The force balance friction model considering internal friction is established. An accurate static model is obtained, which can accurately estimate the deformation and deflection behavior of the robot according to the input driving force. The kinematic model of the notched continuum robot based on the static model is established. This method achieves high accuracywhile ensuring computational efficiency.

Results: Experimental results demonstrate that the static model's error is only 0.1629 mm, which corresponds to 0.25% of the total length of the continuum robot, which is 66 mm.

Discussion: This research provides valuable insights into the modeling and control of continuum robots and holds significant implications for advancing precision in orthopedic surgery.

The cornea is a vital tissue of the human body. The health status of the cornea has a great impact on the quality life of person. There has been a great deal of research on the human cornea biomechancis. However, the difficulty in obtaining the human cornea has greatly limited the research of cornea biomechancis. Using finite element modelling has become a very effective and economical means for studying mechanical properties of human cornea. In this review, the geometrical and constitutive models of the cornea are summarised and analysed, respectively. Some factors affecting of the finite element calculation are discussed. In addition, prospects and challenges for the finite element model of the human cornea are presented. This review will be helpful to researchers performing studies in the relevant fields of human cornea finite element analysis.

Background: While the forward bow step is a crucial component of Tai Chi (TC) practice, little research has been conducted on its impact on knee joint load and muscle coordination. This study aims to investigate the effects of three different knee forward positions during the TC forward bow step on knee joint loading.

Methods: Twenty TC practitioners were recruited, and motion capture systems, force platforms, and surface electromyography were utilized to synchronously collect biomechanical parameters of three types of forward bow steps: knee joint not exceeding the tip of the foot (NETT), knee joint forward movement level with the tip of the foot (LTT), and knee joint forward movement exceeding the tip of the foot (ETT). Ligament and muscle forces were calculated using OpenSim software for musculoskeletal modeling and simulation. One-way ANOVA was used to analyze the variations of the indicators during the peak anterior displacement of the knee joint in three movements. Additionally, spm1d one-way ANOVA was employed to examine the variations in the one-dimensional curve of the indicators throughout the entire movement process.

Results: Compared with LTT and ETT, the NETT posture was associated with significantly decreased knee flexion angle (F = 27.445, p = 0.001), knee anterior-posterior translation (F = 36.07, p < 0.001), flexion-extension torque (F = 22.232, p = 0.001), ligament force (F = 9.055, p = 0.011). Additionally, there was also a significant reduction in muscle strength, including quadriceps (F = 62.9, p < 0.001), long biceps femoris (F = 18.631, p = 0.002), lateral gastrocnemius (F = 24.933, p = 0.001) and soleus (F = 7.637, p = 0.017).

Conclusion: This study further confirms that in the forward lunge movement of Tai Chi, the knee joint load is mainly concentrated during the forward movement phase. Compared to the knee joint load at the NETT position, the load is greater at the LTT position; and compared to the LTT position, the load is even greater at the ETT position.

Introduction: Acne vulgaris, one of the most common skin conditions, affects up to 85% of late adolescents, currently no universally accepted assessment system. The biomechanical properties of skin provide valuable information for the assessment and management of skin conditions. Wave-based optical coherence elastography (OCE) quantitatively assesses these properties of tissues by analyzing induced elastic wave velocities. However, velocity estimation methods require significant expertise and lengthy image processing times, limiting the clinical translation of OCE technology. Recent advances in machine learning offer promising solutions to simplify velocity estimation process.

Methods: In this study, we proposed a novel end-to-end deep-learning model, named velocity prediction network (VP-Net), aiming to accurately predict elastic wave velocity from raw OCE data of in vivo healthy and abnormal human skin. A total of 16,424 raw phase slices from 1% to 5% agar-based tissue-mimicking phantoms, 28,270 slices from in vivo human skin sites including the palm, forearm, back of the hand from 16 participants, and 580 slices of facial closed comedones were acquired to train, validate, and test VP-Net.

Results: VP-Net demonstrated highly accurate velocity prediction performance compared to other deep-learning-based methods, as evidenced by small evaluation metrics. Furthermore, VP-Net exhibited low model complexity and parameter requirements, enabling end-to-end velocity prediction from a single raw phase slice in 1.32 ms, enhancing processing speed by a factor of ∼100 compared to a conventional wave velocity estimation method. Additionally, we employed gradient-weighted class activation maps to showcase VP-Net's proficiency in discerning wave propagation patterns from raw phase slices. VP-Net predicted wave velocities that were consistent with the ground truth velocities in agar phantom, two age groups (20s and 30s) of multiple human skin sites and closed comedones datasets.

Discussion: This study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of in vivo healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.

Background: Tumor resection near the proximal end of the femur and revision surgery of the distal femoral prosthesis may result in a very short bone segment remaining at the proximal end of the femur, known as ultrashort residual proximal femur (URPF). In this study, we propose a triangular fixation stem (TFS) prosthesis to improve the fixation of URPF. The aim of this research is to investigate the biomechanical properties of the TFS prosthesis and compare it with the conventional stem (CS) prosthesis through in vitro biomechanical experiments, providing preliminary biomechanical evidence for prosthetic fixation of URPF.

Methods: A biomechanical study was conducted using Sawbones to explore initial stability. Twelve Sawbones were used to create a bone defect model, and prostheses were designed and fabricated to emulate TFS fixation and CS fixation structures. Axial compression and horizontal torsion experiments were performed on the fixed models using a mechanical testing machine, recording maximum displacement, maximum torque, and femoral strain conditions.

Results: Under an axial compressive load of 2800 N, the overall displacement of the TFS group was 3.33 ± 0.58 mm, which was significantly smaller than that of the CS group (4.03 ± 0.32 mm, P = 0.029). The femoral samples of the TFS group demonstrated that the strain value alterations at the medial points 2, 3, 5, 6 and the lateral point 10 were conspicuously smaller than those of the conventional stem group (P < 0.05). Under torsional loads at levels of 1°, 3°, and 5°, the torques of the TFS group were 3.86 ± 0.69 Nm, 3.90 ± 1.26 Nm, and 4.39 ± 1.67 Nm respectively, all of which were significantly greater than those of the CS group (1.82 ± 0.82 Nm, P < 0.001; 2.05 ± 0.89 Nm, P = 0.016; 1.96 ± 0.50 Nm, P = 0.015 respectively).

Conclusion: The TFS prosthesis improves fixation strength and reduces strain on the femur's proximal surface. Compared to CS fixation, it offers better resistance to compression and rotation, as well as improved initial stability.

Introduction: The study aims to predict tooth extraction decision based on four machine learning methods and analyze the feature contribution, so as to shed light on the important basis for experts of tooth extraction planning, providing reference for orthodontic treatment planning.

Methods: This study collected clinical information of 192 patients with malocclusion diagnosis and treatment plans. This study used four machine learning strategies, including decision tree, random forest, support vector machine (SVM) and multilayer perceptron (MLP) to predict orthodontic extraction decisions on clinical examination data acquired during initial consultant containing Angle classification, skeletal classification, maxillary and mandibular crowding, overjet, overbite, upper and lower incisor inclination, vertical growth pattern, lateral facial profile. Among them, 30% of the samples were randomly selected as testing sets. We used five-fold cross-validation to evaluate the generalization performance of the model and avoid over-fitting. The accuracy of the four models was calculated for the training set and cross-validation set. The confusion matrix was plotted for the testing set, and 6 indicators were calculated to evaluate the performance of the model. For the decision tree and random forest models, we observed the feature contribution.

Results: The accuracy of the four models in the training set ranges from 82% to 90%, and in the cross-validation set, the decision tree and random forest had higher accuracy. In the confusion matrix analysis, decision tree tops the four models with highest accuracy, specificity, precision and F1-score and the other three models tended to classify too many samples as extraction cases. In the feature contribution analysis, crowding, lateral facial profile, and lower incisor inclination ranked at the top in the decision tree model.

Conclusion: Among the machine learning models that only use clinical data for tooth extraction prediction, decision tree has the best overall performance. For tooth extraction decisions, specifically, crowding, lateral facial profile, and lower incisor inclination have the greatest contribution.

The wound healing process involves communication among growth factors, cytokines, signaling pathways, and cells in the extracellular matrix, with growth factors acting as key regulators. Although stem cells can promote wound healing by secreting diverse growth factors, their therapeutic potential is hindered by poor survival and engraftment. Mimicking the stem cell-matrix interactions can improve stem cell survival, regulate their fate, and even enhance their paracrine effects. This study investigated the use of composite RGDmix hydrogel, which can support the survival and proliferation of human amniotic mesenchymal stem cells (hAMSCs), and effectively increase the expression of various growth factors, thereby promoting wound re-epithelialization, angiogenesis, and epidermal maturation. At last, the specific role of integrin αv and PI3K/AKT signaling pathways in the secretion of growth factors were examined by silencing them in vitro and in vivo. Results suggested that the RGDmix hydrogel improved the secretion of growth factors by hAMSCs through the RGDSP/integrin αv/PI3K/AKT axis, thereby enhancing the therapeutic effect in wound healing.