生物医学工程学杂志最新文献

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.

Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model's generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.

Transcranial direct current stimulation (tDCS) is an important method for treating mental illnesses and neurodegenerative diseases. This paper reconstructed two ex vivo brain slice models based on rat brain slice staining images and magnetic resonance imaging (MRI) data respectively, and the current densities of hippocampus after cortical tDCS were obtained through finite element calculation. Subsequently, a neuron model was used to calculate the response of rat hippocampal pyramidal neuron under these current densities, and the neuronal responses of the two models under different stimulation parameters were compared. The results show that a minimum stimulation voltage of 17 V can excite hippocampal pyramidal neuron in the model based on brain slice staining images, while 24 V is required in the MRI-based model. The results indicate that the model based on brain slice staining images has advantages in precision and electric field propagation simulation, and its results are closer to real measurements, which can provide guidance for the selection of tDCS parameters and scientific basis for precise stimulation.

Adaptive filtering methods based on least-mean-square (LMS) error criterion have been commonly used in auscultation to reduce ambient noise. For non-Gaussian signals containing pulse components, such methods are prone to weights misalignment. Unlike the commonly used variable step-size methods, this paper introduced linear preprocessing to address this issue. The role of linear preprocessing in improving the denoising performance of the normalized least-mean-square (NLMS) adaptive filtering algorithm was analyzed. It was shown that, the steady-state mean square weight deviation of the NLMS adaptive filter was proportional to the variance of the body sounds and inversely proportional to the variance of the ambient noise signals in the secondary channel. Preprocessing with properly set parameters could suppress the spikes of body sounds, and decrease the variance and the power spectral density of the body sounds, without significantly reducing or even with increasing the variance and the power spectral density of the ambient noise signals in the secondary channel. As a result, the preprocessing could reduce weights misalignment, and correspondingly, significantly improve the performance of ambient-noise reduction. Finally, a case of heart-sound auscultation was given to demonstrate how to design the preprocessing and how the preprocessing improved the ambient-noise reduction performance. The results can guide the design of adaptive denoising algorithms for body sound auscultation.