生物医学工程学杂志最新文献

Understanding the mass transfer behaviors in hollow fiber membrane module of artificial liver is important for improving toxin removal efficiency. A three-dimensional numerical model was established to study the mass transfer of small molecule bilirubin and macromolecule bovine serum albumin (BSA) in the hollow fiber membrane module. Effects of tube-side flow rate, shell-side flow rate, and hollow fiber length on the mass transfer of bilirubin and BSA were discussed. The simulation results showed that the clearance of bilirubin was significantly affected by both convective and diffusive solute transport, while the clearance of macromolecule BSA was dominated by convective solute transport. The clearance rates of bilirubin and BSA increasd with the increase of tube-side flow rate and hollow fiber length. With the increase of shell-side flow rate, the clearance rate of bilirubin first rose rapidly, then slowly rose to an asymptotic value, while the clearance rate of BSA gradually decreased. The results can provide help for designing structures of hollow fiber membrane module and operation parameters of clinical treatment.

Polyurethane materials have good biocompatibility, blood compatibility, mechanical properties, fatigue resistance and processability, and have always been highly valued as medical materials. Polyurethane fibers prepared by electrostatic spinning technology can better mimic the structure of natural extracellular matrices (ECMs), and seed cells can adhere and proliferate better to meet the requirements of tissue repair and reconstruction. The purpose of this review is to present the research progress of electrostatically spun polyurethane fibers in bone tissue engineering, skin tissue engineering, neural tissue engineering, vascular tissue engineering and cardiac tissue engineering, so that researchers can understand the practical applications of electrostatically spun polyurethane fibers in tissue engineering and regenerative medicine.

To investigate the biomechanical effects of direct ventricular assistance and explore the optimal loading mode, this study established a left ventricular model of heart failure patients based on the finite element method. It proposed a loading mode that maintains peak pressure compression, and compared it with the traditional sinusoidal loading mode from both hemodynamic and biomechanical perspectives. The results showed that both modes significantly improved hemodynamic parameters, with ejection fraction increased from a baseline of 29.33% to 37.32% and 37.77%, respectively, while peak pressure, stroke volume, and stroke work parameters also increased. Additionally, both modes showed improvements in stress concentration and excessive fiber strain. Moreover, considering the phase error of the assist device's working cycle, the proposed assist mode in this study was less affected. Therefore, this research may provide theoretical support for the design and optimization of direct ventricular assist devices.

Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.

Hydrogel is a kind of degradable hydrophilic polymer, but excessive hydrophilicity leads to larger volume, lower elastic modulus and looser structure, which further affect its use. Especially in the field of biomedical engineering, excessive swelling of the hydrogel can compress the nerves and improve degradation rate resulting in mismatch of tissue growth and released ions. Therefore, anti-swelling hydrogel has been a research hotspot in recent years. This paper reviews the recent research progress on anti-swelling hydrogel, and expounds the application mechanism and preparation method of hydrogel in biomedical engineering, aiming to provide some references for researchers in the field of anti-swelling hydrogel.

High-grade serous ovarian cancer has a high degree of malignancy, and at detection, it is prone to infiltration of surrounding soft tissues, as well as metastasis to the peritoneum and lymph nodes, peritoneal seeding, and distant metastasis. Whether recurrence occurs becomes an important reference for surgical planning and treatment methods for this disease. Current recurrence prediction models do not consider the potential pathological relationships between internal tissues of the entire ovary. They use convolutional neural networks to extract local region features for judgment, but the accuracy is low, and the cost is high. To address this issue, this paper proposes a new lightweight deep learning algorithm model for predicting recurrence of high-grade serous ovarian cancer. The model first uses ghost convolution (Ghost Conv) and coordinate attention (CA) to establish ghost counter residual (SCblock) modules to extract local feature information from images. Then, it captures global information and integrates multi-level information through proposed layered fusion Transformer (STblock) modules to enhance interaction between different layers. The Transformer module unfolds the feature map to compute corresponding region blocks, then folds it back to reduce computational cost. Finally, each STblock module fuses deep and shallow layer depth information and incorporates patient's clinical metadata for recurrence prediction. Experimental results show that compared to the mainstream lightweight mobile visual Transformer (MobileViT) network, the proposed slicer visual Transformer (SlicerViT) network improves accuracy, precision, sensitivity, and F1 score, with only 1/6 of the computational cost and half the parameter count. This research confirms that the proposed algorithm model is more accurate and efficient in predicting recurrence of high-grade serous ovarian cancer. In the future, it can serve as an auxiliary diagnostic technique to improve patient survival rates and facilitate the application of the model in embedded devices.

Lower limb exoskeleton rehabilitation robots are used to improve or restore the walking and movement ability of people with lower limb movement disorders. However, the required functions for patients differ based on various diseases. For example, patients with weak muscle strength require power assistance, patients with spinal cord injuries require motion compensation, patients with gait abnormalities require gait correction, and patients with strokes require neural rehabilitation. To design a more targeted lower limb exoskeleton rehabilitation robot for different diseases, this article summarised and compared existing lower limb exoskeleton rehabilitation robots according to their main functions and the characteristics and rehabilitation needs of various lower limb movement disorders. The correlations between the functions of existing devices and diseases were summarised to provide certain references for the development of new lower limb exoskeleton rehabilitation robots.

Simulation of the human biological lung is a crucial method for medical professionals to learn and practice the use of new pulmonary interventional diagnostic and therapeutic devices. The study on ventilation effects of the simulation under positive pressure ventilation mode provide valuable guidance for clinical ventilation treatment. This study focused on establishing an electrical simulation ventilation model, which aims to address the complexities in parameter configuration and slow display of air pressure and airflow waveforms in simulating the human biological lung under positive pressure ventilation mode. A simulated ventilation experiment was conducted under pressure-regulated volume control (PRVC) positive pressure ventilation mode, and the resulting ventilation waveform was compared with that of normal adults. The experimental findings indicated that the average error of the main reference index moisture value was 9.8% under PRVC positive pressure ventilation mode, effectively simulating the ventilatory effect observed in normal adults. So the established electrical simulation ventilation model is feasible, and provides a foundation for further research on the simulation of human biological lung positive pressure ventilation experimental platform.

The use of a filling block can improve the initial stability of the fixation plate in the open wedge high tibial osteotomy (OWHTO), and promote bone healing. However, the biomechanical effects of filling block structures and materials on OWHTO remain unclear. OWHTO anatomical filling block model was designed and built. The finite element analysis method was adopted to study the influence of six filling block structure designs and four different materials on the stress of the fixed plate, tibia, screw, and filling block, and the micro-displacement at the wedge gap of the OWHTO fixation system. After the filling block was introduced in the OWHTO, the maximum von Mises stress of the fixation plate was reduced by more than 30%, the maximum von Mises stress of the tibia decreased by more than 15%, and the lateral hinge decreased by 81%. When the filling block was designed to be filled in the posterior position of the wedge gap, the maximum von Mises stress of the fixation system was 97.8 MPa, which was smaller than other filling methods. The minimum micro-displacement of osteotomy space was -2.9 μm, which was larger than that of other filling methods. Compared with titanium alloy and tantalum metal materials, porous hydroxyapatite material could obtain larger micro-displacement in the osteotomy cavity, which is conducive to stimulating bone healing. The results demonstrate that OWHTO with a filling block can better balance the stress distribution of the fixation system, and a better fixation effect can be obtained by using a filling block filled in the posterior position. Porous HA used as the material of the filling block can obtain a better bone healing effect.

Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean ρ values: 0.78 ~ 0.92) but with root mean square errors as high as 5.66°. For lower limb joint force prediction, the model exhibited root mean square errors ranging from 0.44 to 0.68, while ground reaction force root mean square errors ranged from 0.01 to 0.09. The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces, but some errors remain in predicting lower limb joint angles.