生物医学工程学杂志最新文献

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.

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor's diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

To address the conflict between the "fitness" and "feasibility" of body-fitted stents, this paper investigates the impact of various smoothing design strategies on the mechanical behaviour and apposition performance of stent. Based on the three-dimensional projection method, the projection region was fitted with the least squares method (fitting orders 1-6 corresponded to models 1-6, respectively) to achieve the effect of smoothing the body-fitted stent. The simulation included the crimping and expansion process of six groups of stents in stenotic vessels with different degrees of plaque calcification. Various metrics were analyzed, including bending stiffness, stent ruggedness, area residual stenosis rate, contact area fraction, and contact volume fraction. The study findings showed that the bending stiffness, stent ruggedness, area residual stenosis rate, contact area fraction and contact volume fraction increased with the fitting order's increase. Model 1 had the smallest contact area fraction and contact volume fraction, 77.63% and 83.49% respectively, in the incompletely calcified plaque environment. In the completely calcified plaque environment, these values were 72.86% and 82.21%, respectively. Additionally, it had the worst "fitness". Models 5 and 6 had similar values for stent ruggedness, with 32.15% and 32.38%, respectively, which indicated the worst "feasibility" for fabrication and implantation. Models 2, 3, and 4 had similar area residual stenosis rates in both plaque environments. In conclusion, it is more reasonable to obtain the body-fitted stent by using 2nd to 4th order fitting with the least squares method to the projected region. Among them, the body-fitted stent obtained by the 2nd order fitting performs better in the completely calcified environment.

Animal localization and trajectory tracking are of great value for the study of brain spatial cognition and navigation neural mechanisms. However, traditional optical lens video positioning techniques are limited in their scope due to factors such as camera perspective. For pigeons with excellent spatial cognition and navigation abilities, based on the beacon positioning technology, a three-dimensional (3D) trajectory positioning and tracking method suitable for large indoor spaces was proposed, and the corresponding positioning principle and hardware structure were provided. The results of in vitro and in vivo experiments showed that the system could achieve centimeter-level positioning and trajectory tracking of pigeons in a space of 360 cm × 200 cm × 245 cm. Compared with traditional optical lens video positioning techniques, this system has the advantages of large space, high precision, and high response speed. It not only helps to study the neural mechanisms of pigeon 3D spatial cognition and navigation, but also has high reference value for trajectory tracking of other animals.