生物医学工程学杂志最新文献

The establishment of brain metabolic network is based on ¹⁸fluoro-deoxyglucose positron emission computed tomography ( ¹⁸F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to ¹⁸F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on ¹⁸F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.

Prolonged disorders of consciousness (pDOC) are pathological conditions of alterations in consciousness caused by various severe brain injuries, profoundly affecting patients' life ability and leading to a huge burden for both the family and society. Exploring the mechanisms underlying pDOC and accurately assessing the level of consciousness in the patients with pDOC provide the basis of developing therapeutic strategies. Research of non-invasive functional neuroimaging technologies, such as functional magnetic resonance (fMRI) and scalp electroencephalography (EEG), have demonstrated that the generation, maintenance and disorders of consciousness involve functions of multiple cortical and subcortical brain regions, and their networks. Invasive intracranial neuroelectrophysiological technique can directly record the electrical activity of subcortical or cortical neurons with high signal-to-noise ratio and spatial resolution, which has unique advantages and important significance for further revealing the brain function and disease mechanism of pDOC. Here we reviewed the current progress of pDOC research based on two intracranial electrophysiological signals, spikes reflecting single-unit activity and field potential reflecting multi-unit activities, and then discussed the current challenges and gave an outlook on future development, hoping to promote the study of pathophysiological mechanisms related to pDOC and provide guides for the future clinical diagnosis and therapy of pDOC.

Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.

This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture types. Firstly, an extended U-Net three-layer cascaded segmentation network was used to accurately segment the most important joint surface and non joint surface areas for identifying fractures. Then, the images of the joint surface area and non joint surface area separately were classified and trained to distinguish fractures. Finally, based on the classification results of the two images, the normal or ABC fracture classification results could be comprehensively determined. The accuracy rates of normal, A-type, B-type, and C-type fracture on the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81, respectively. The proposed automatic recognition method is generally better than experts, and can be used for preliminary auxiliary diagnosis of distal radius fractures in scenarios without expert participation.

The locking mechanism between bracket and shape memory alloy (SMA) archwire in the newly developed domestic orthodontic device is the key to controlling the precise alignment of the teeth. To meet the demand of locking force in clinical treatment, the tightening torque angle of the locking bolt and the required torque magnitude need to be precisely designed. For this purpose, a design study of the locking mechanism is carried out to analyze the correspondence between the tightening torque angle and the locking force and to determine the effective torque value, which involves complex coupling of contact, material and geometric nonlinear characteristics. Firstly, a simulation analysis based on parametric orthogonal experimental design is carried out to determine the SMA hyperelastic material parameters for the experimental data of SMA archwire with three-point bending. Secondly, a two-stage fine finite-element simulation model for bolt tightening and archwire pulling is established, and the nonlinear analysis is converged through the optimization of key contact parameters. Finally, multiple sets of calibration experiments are carried out for three tightening torsion angles. The comparison results between the design analysis and the calibration experiments show that the deviation between the design analysis and the calibration mean value of the locking force in each case is within 10%, and the design analysis method is valid and reliable. The final tightening torque angle for clinical application is determined to be 10° and the rated torque is 2.8 N∙mm. The key data obtained can be used in the design of clinical protocols and subsequent mechanical optimization of novel orthodontic devices, and the research methodology can provide a valuable reference for force analysis of medical devices containing SMA materials.

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.

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.