生物医学工程学杂志最新文献

Currently, the development of deep learning-based multimodal learning is advancing rapidly, and is widely used in the field of artificial intelligence-generated content, such as image-text conversion and image-text generation. Electronic health records are digital information such as numbers, charts, and texts generated by medical staff using information systems in the process of medical activities. The multimodal fusion method of electronic health records based on deep learning can assist medical staff in the medical field to comprehensively analyze a large number of medical multimodal data generated in the process of diagnosis and treatment, thereby achieving accurate diagnosis and timely intervention for patients. In this article, we firstly introduce the methods and development trends of deep learning-based multimodal data fusion. Secondly, we summarize and compare the fusion of structured electronic medical records with other medical data such as images and texts, focusing on the clinical application types, sample sizes, and the fusion methods involved in the research. Through the analysis and summary of the literature, the deep learning methods for fusion of different medical modal data are as follows: first, selecting the appropriate pre-trained model according to the data modality for feature representation and post-fusion, and secondly, fusing based on the attention mechanism. Lastly, the difficulties encountered in multimodal medical data fusion and its developmental directions, including modeling methods, evaluation and application of models, are discussed. Through this review article, we expect to provide reference information for the establishment of models that can comprehensively utilize various modal medical data.

Glaucoma stands as the leading irreversible cause of blindness worldwide. Regular visual field examinations play a crucial role in both diagnosing and treating glaucoma. Predicting future visual field changes can assist clinicians in making timely interventions to manage the progression of this disease. To integrate temporal and spatial features from past visual field examination results and enhance visual field prediction, a convolutional long short-term memory (ConvLSTM) network was employed to construct a predictive model. The predictive performance of the ConvLSTM model was validated and compared with other methods using a dataset of perimetry tests from the Humphrey field analyzer at the University of Washington (UWHVF). Compared to traditional methods, the ConvLSTM model demonstrated higher prediction accuracy. Additionally, the relationship between visual field series length and prediction performance was investigated. In predicting the visual field using the previous three visual field results of past 1.5~6.0 years, it was found that the ConvLSTM model performed better, achieving a mean absolute error of 2.255 dB, a root mean squared error of 3.457 dB, and a coefficient of determination of 0.960. The experimental results show that the proposed method effectively utilizes existing visual field examination results to achieve more accurate visual field prediction for the next 0.5~2.0 years. This approach holds promise in assisting clinicians in diagnosing and treating visual field progression in glaucoma patients.

The hemodynamic parameters in arteries are difficult to measure non-invasively, and the analysis and prediction of hemodynamic parameters based on computational fluid dynamics (CFD) has become one of the important research hotspots in biomechanics. This article establishes 15 idealized left coronary artery bifurcation models with concomitant stenosis and aneurysm lesions, and uses CFD method to numerically simulate them, exploring the effects of left anterior descending branch (LAD) stenosis rate and curvature radius on the hemodynamics inside the aneurysm. This study compared models with different stenosis rates and curvature radii and found that as the stenosis rate increased, the oscillatory shear index (OSI) and relative residence time (RRT) showed a trend of increase; In addition, the decrease in curvature radius led to an increase in the degree of vascular curvature and an increased risk of vascular aneurysm rupture. Among them, when the stenosis rate was less than 60%, the impact of stenosis rate on aneurysm rupture was greater, and when the stenosis rate was greater than 60%, the impact of curvature radius was more significant. Based on the research results of this article, it can be concluded that by comprehensively considering the effects of stenosis rate and curvature radius on hemodynamic parameters, the risk of aneurysm rupture can be analyzed and predicted. This article uses CFD methods to deeply explore the effects of stenosis rate and curvature radius on the hemodynamics of aneurysms, providing new theoretical basis and prediction methods for the assessment of aneurysm rupture risk, which has important academic value and practical guidance significance.

Fear emotion is a typical negative emotion that is commonly present in daily life and significantly influences human behavior. A deeper understanding of the mechanisms underlying negative emotions contributes to the improvement of diagnosing and treating disorders related to negative emotions. However, the neural mechanisms of the brain when faced with fearful emotional stimuli remain unclear. To this end, this study further combined electroencephalogram (EEG) source analysis and cortical brain network construction based on early posterior negativity (EPN) analysis to explore the differences in brain information processing mechanisms under fearful and neutral emotional picture stimuli from a spatiotemporal perspective. The results revealed that neutral emotional stimuli could elicit higher EPN amplitudes compared to fearful stimuli. Further source analysis of EEG data containing EPN components revealed significant differences in brain cortical activation areas between fearful and neutral emotional stimuli. Subsequently, more functional connections were observed in the brain network in the alpha frequency band for fearful emotions compared to neutral emotions. By quantifying brain network properties, we found that the average node degree and average clustering coefficient under fearful emotional stimuli were significantly larger compared to neutral emotions. These results indicate that combining EPN analysis with EEG source component and brain network analysis helps to explore brain functional modulation in the processing of fearful emotions with higher spatiotemporal resolution, providing a new perspective on the neural mechanisms of negative emotions.

Stent migration is one of the common complications after tracheal stent implantation. The causes of stent migration include size mismatch between the stent and the trachea, physiological movement of the trachea, and so on. In order to solve the above problems, this study designed a non-uniform Poisson ratio tracheal stent by combining the size and structure of the trachea and the physiological movement of the trachea to improve the migration of the stent, meanwhile ensuring the support of the stent. In this study, the stent corresponding to cartilage was constructed with negative Poisson's ratio, and the stent corresponding to the circular connective tissue and muscular membrane was constructed with positive Poisson's ratio. And four kinds of non-uniform Poisson's ratio tracheal stents with different link lengths and negative Poisson's ratio were designed. Then, this paper numerically simulated the expansion and rebound process of the stent after implantation to observe the support of the stent, and further simulated the stretch movement of the trachea to calculate the diameter changes of the stent corresponding to different negative Poisson's ratio structures. The axial migration of the stent was recorded by applying different respiratory pressure to the wall of the tracheal wall to evaluate whether the stent has anti-migration effect. The research results show that the non-uniform Poisson ratio stent with connecting rod length of 3 mm has the largest diameter expansion in the negative Poisson ratio section when the trachea was stretched. Compared with the positive Poisson's ratio structure, the axial migration during vigorous breathing was reduced from 0.024 mm to 0.012 mm. The negative Poisson's ratio structure of the non-uniform Poisson's ratio stent designed in this study did not fail in the tracheal expansion effect. Compared with the traditional stent, the non-uniform Poisson's ratio tracheal stent has an anti-migration effect under the normal movement of the trachea while ensuring the support force of the stent.

Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.

Ultrasonic microfluidic technology is a technique that couples high-frequency ultrasonic excitation to microfluidic chips. To improve the issues of poor disturbance effects with flexible tip structures and the susceptibility of bubbles to thermal deformation, we propose an enhanced ultrasonic microchannel structure that couples flexible tips with bubbles aiming to improve the disturbance effects and the stability duration. Firstly, we used finite element analysis to simulate the flow field distribution characteristics of the flexible tip, the bubble, and the coupling structure and obtained the steady-state distribution characteristics of the velocity field. Next, we fabricated ultrasonic microfluidic chips based on these three structures, employing 2.8 μm polystyrene microspheres as tracers to analyze the disturbance characteristics of the flow field. Additionally, we analyzed the bubble size and growth rate within the adhering bubbles and coupling structures. Finally, we verified the applicability of the coupling structure for biological samples using human red blood cells (RBCs). Experimental results indicated that, compared to the flexible tip and adhering bubble structures, the flow field disturbance range of the coupling structure increased by 439.53% and 133.48%, respectively; the bubble growth rate reduced from 14.4% to 3.3%. The enhanced ultrasonic microfluidic structure proposed in this study shows great potential for widespread applications in micro-scale flow field disturbance and particle manipulation.

The issue of bacterial drug resistance has remained unresolved, and in recent years, biomimetic nanostructured surfaces inspired by nature have garnered significant attention due to their bactericidal properties demonstrated through mechanical mechanisms. This article reviewed the main research progress in the field of nanostructured mechanical bactericidal surfaces, including various preparation methods for nanostructured surfaces with mechanical bactericidal properties, as well as the basic mechanisms and related physical models of the interaction between bacteria and nanostructured surfaces. In addition, the application of nanostructured surfaces in biomedicine was introduced. Finally, the article proposed the major challenges faced by mechanical bactericidal research and the future development direction.

Image fusion currently plays an important role in the diagnosis of prostate cancer (PCa). Selecting and developing a good image fusion algorithm is the core task of achieving image fusion, which determines whether the fusion image obtained is of good quality and can meet the actual needs of clinical application. In recent years, it has become one of the research hotspots of medical image fusion. In order to make a comprehensive study on the methods of medical image fusion, this paper reviewed the relevant literature published at home and abroad in recent years. Image fusion technologies were classified, and image fusion algorithms were divided into traditional fusion algorithms and deep learning (DL) fusion algorithms. The principles and workflow of some algorithms were analyzed and compared, their advantages and disadvantages were summarized, and relevant medical image data sets were introduced. Finally, the future development trend of medical image fusion algorithm was prospected, and the development direction of medical image fusion technology for the diagnosis of prostate cancer and other major diseases was pointed out.

Ischemic stroke often leads to cognitive dysfunction, which delays the recovery process of patients. However, its pathogenesis is not yet clear. In this study, the cerebral ischemia-reperfusion model was built as the experimental object, and the hippocampal dentate gyrus (DG) was the target brain area. TTC staining was used to evaluate the degree of cerebral infarction, and nerve cell membrane potentials and local field potentials (LFPs) signals were collected to explore the mechanism of cognitive impairment in ischemia-reperfusion mice. The results showed that the infarcted area on the right side of the brain of the mice in the model group was white. The resting membrane potential, the number of action potential discharges, the post-hyperpolarization potential and the maximum ascending slope of the hippocampal DG nerve cells in the model mice were significantly lower than those in the control group ( P < 0.01); the peak time, half-wave width, threshold and maximum descending slope of the action potential were significantly higher than those in the control group ( P < 0.01). The time-frequency energy values of LFPs signals in the θ and γ bands of mice in the ischemia and reperfusion groups were significantly reduced ( P < 0.01), and the time-frequency energy values in the reperfusion group were increased compared with the ischemia group ( P < 0.01). The signal complexity of LFPs in the ischemia and reperfusion group was significantly reduced ( P < 0.05), and the signal complexity in the reperfusion group was increased compared with the ischemia group ( P < 0.05). In summary, cerebral ischemia-reperfusion reduced the excitability of nerve cells in the DG area of the mouse hippocampus; cerebral ischemia reduced the discharge activity and signal complexity of nerve cells, and the electrophysiological indicators recovered after reperfusion, but it failed to reach the healthy state during the experiment period.