生物医学工程学杂志最新文献

The impeller, as a key component of artificial heart pumps, experiences high shear stress due to its rapid rotation, which may lead to hemolysis. To enhance the hemolytic performance of artificial heart pumps and identify the optimal combination of blade parameters, an optimization design for existing pump blades is conducted. The number of blades, outlet angle, and blade thickness were selected as design variables, with the maximum shear stress within the pump serving as the optimization objective. A back propagation (BP) neural network prediction model was established using existing simulation data, and a grey wolf optimization algorithm was employed to optimize the blade parameters. The results indicated that the optimized blade parameters consisted of 7 impeller blades, an outlet angle of 25 °, and a blade thickness of 1.2 mm; this configuration achieved a maximum shear stress value of 377 Pa-representing a reduction of 16% compared to the original model. Simulation analysis revealed that in comparison to the original model, regions with high shear stress at locations such as the outer edge, root, and base significantly decreased following optimization efforts, thus leading to marked improvements in hemolytic performance. The coupling algorithm employed in this study has significantly reduced the workload associated with modeling and simulation, while also enhancing the performance of optimization objectives. Compared to traditional optimization algorithms, it demonstrates distinct advantages, thereby providing a novel approach for investigating parameter optimization issues related to centrifugal artificial heart pumps.

In order to seek a patient friendly and low-cost intestinal examination method, a structurally simple pneumatic soft intestinal robot inspired by inchworms is designed and manufactured. The intestinal robot was consisted of two radially expanding cylindrical rubber film airbags for anchoring and one low density polyethylene film airbag for axial elongation, which achieved movement in the intestine by mimicking the crawling of inchworms. Theoretical derivation was conducted on the relationship between the internal air pressure of the anchored airbag and the free deformation size after expansion, and it pointed out that the uneven deformation of the airbag was a phenomenon of expansion instability caused by large deformation of the rubber material. The motion performance of the intestinal robot was validated in different sizes of hard tubes and ex vivo pig small intestine. The running speed in the ex vivo pig small intestine was 4.87 mm/s, with an anchoring force of 2.33 N when stationary, and could smoothly pass through a 90 ° bend. This work expects to provide patients with a new method of low pain and low-cost intestinal examination.

The specific binding of T cell receptors (TCRs) to antigenic peptides plays a key role in the regulation and mediation of the immune process and provides an essential basis for the development of tumour vaccines. In recent years, studies have mainly focused on TCR prediction of major histocompatibility complex (MHC) class I antigens, but TCR prediction of MHC class II antigens has not been sufficiently investigated and there is still much room for improvement. In this study, the combination of MHC class II antigen peptide and TCR prediction was investigated using the ProtT5 grand model to explore its feature extraction capability. In addition, the model was fine-tuned to retain the underlying features of the model, and a feed-forward neural network structure was constructed for fusion to achieve the prediction model. The experimental results showed that the method proposed in this study performed better than the traditional methods, with a prediction accuracy of 0.96 and an AUC of 0.93, which verifies the effectiveness of the model proposed in this paper.

Epileptic seizures and the interictal epileptiform discharges both have similar waveforms. And a method to effectively extract features that can be used to distinguish seizures is of crucial importance both in theory and clinical practice. We constructed state transfer networks by using visibility graphlet at multiple sampling intervals and analyzed network features. We found that the characteristics waveforms in ictal periods were more robust with various sampling intervals, and those feature network structures did not change easily in the range of the smaller sampling intervals. Inversely, the feature network structures of interictal epileptiform discharges were stable in range of relatively larger sampling intervals. Furthermore, the feature nodes in networks during ictal periods showed long-term correlation along the process, and played an important role in regulating system behavior. For stereo-electroencephalography at around 500 Hz, the greatest difference between ictal and the interictal epileptiform occurred at the sampling interval around 0.032 s. In conclusion, this study effectively reveals the correlation between the features of pathological changes in brain system and the multiple sampling intervals, which holds potential application value in clinical diagnosis for identifying, classifying, and predicting epilepsy.

Dementia is a neurodegenerative disease closely related to brain network dysfunction. In this study, we assessed the interdependence between brain regions in patients with early-stage dementia based on phase-lock values, and constructed a functional brain network, selecting network feature parameters for metrics based on complex network analysis methods. At the same time, the entropy information characterizing the EEG signals in time domain, frequency domain and time-frequency domain, as well as the nonlinear dynamics features such as Hjorth and Hurst indexes were extracted, respectively. Based on the statistical analysis, the feature parameters with significant differences between different conditions were screened to construct feature vectors, and finally multiple machine learning algorithms were used to realize the recognition of early categories of dementia patients. The results showed that the fusion of multiple features performed well in the categorization of Alzheimer's disease, frontotemporal lobe dementia and healthy controls, especially in the identification of Alzheimer's disease and healthy controls, the accuracy of β-band reached 98%, which showed its effectiveness. This study provides new ideas for the early diagnosis of dementia and computer-assisted diagnostic methods.

This study aims to investigate the protective effect of resveratrol against liver injury in hindlimb unloading rats. Thirty 2-month-old male SD rats were randomly divided into normal group (Control), hindlimb unloading model group (Model), and hindlimb unloading+resveratrol administration group (Model+Res). The Model + Res group was injected intraperitoneally with 30 mg/kg of resveratrol, and the Control and Model groups were injected intraperitoneally with an equal volume of 0.9% NaCl. Liver tissues were collected after 28 days and analyzed for oxidative stress, inflammatory factors, energy metabolism indices, Na ⁺-K ⁺-ATPase and Ca ²⁺-Mg ²⁺-ATPase activity, and morphological changes were observed by hematoxylin-eosin staining. The protein expression levels of Bax, Bcl-2, p-PI3K, PI3K, p-AKT, and AKT were detected by Western blotting. Compared with the Control group, hepatocytes in the Model group showed swelling, abnormal morphology, nuclear consolidation, and cell membrane disruption. Oxidative stress, inflammatory factor levels, hepatic glycogen accumulation, and energy metabolism were increased in the liver tissues of the Model group, while resveratrol treatment significantly reversed these changes. The results of Western blotting showed that resveratrol significantly reduced the expression of Bax and increased the expression levels of Bcl-2, and the proteins of p-PI3K/PI3K and p-AKT/AKT expression levels. It is suggested that 28 days of hindlimb unloading treatment could lead to liver tissue injury in rats, which is manifested as oxidative stress, inflammatory response, energy metabolism disorder and increased apoptosis level, and resveratrol has a certain mitigating effect on this.

The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and spatiotemporal resolutions, leading to low decoding recognition rates with traditional neural networks. To address this, this paper proposed a three-dimensional (3D) convolutional neural network (CNN) method that learns spatial-frequency feature maps, using Welch method to calculate the power spectrum of EEG frequency bands, converted time-series EEG into a brain topographical map with spatial-frequency information. A 3D network with one-dimensional and two-dimensional convolutional layers was designed to effectively learn these features. Comparative experiments demonstrated that the average decoding recognition rate reached 86.89%, outperforming traditional methods and validating the effectiveness of this approach in motor imagery EEG decoding.

Sit-to-stand is an indispensable functional activity in human daily life, which requires high muscle strength, not only to control the lower limbs, but also to ensure the stable ascension of the trunk. This paper describes in detail the trajectory and speed of the joints through the human sit-to-stand test, analyzes the change rule of the angle of the joints, the angular velocity and the position of the center of mass in the human sit-to-stand, and records in detail the change of the plantar pressure of the subjects in this process. Through the study on joint motion and plantar pressure changes in the process of sit-to-stand, this paper summarizes the kinematics of human body in this process, aiming to provide a basis through the results of this paper for the design of human sit-to-stand assistive devices, which may be used in the future to analyze the sit-to-stand state of patients with lower limb disorders, and carry out the corresponding treatment and rehabilitation training.

To accurately capture and effectively integrate the spatiotemporal features of electroencephalogram (EEG) signals for the purpose of improving the accuracy of EEG-based emotion recognition, this paper proposes a new method combining independent component analysis-recurrence plot with an improved EfficientNet version 2 (EfficientNetV2). First, independent component analysis is used to extract independent components containing spatial information from key channels of the EEG signals. These components are then converted into two-dimensional images using recurrence plot to better extract emotional features from the temporal information. Finally, the two-dimensional images are input into an improved EfficientNetV2, which incorporates a global attention mechanism and a triplet attention mechanism, and the emotion classification is output by the fully connected layer. To validate the effectiveness of the proposed method, this study conducts comparative experiments, channel selection experiments and ablation experiments based on the Shanghai Jiao Tong University Emotion Electroencephalogram Dataset (SEED). The results demonstrate that the average recognition accuracy of our method is 96.77%, which is significantly superior to existing methods, offering a novel perspective for research on EEG-based emotion recognition.

Manual segmentation of coronary arteries in computed tomography angiography (CTA) images is inefficient, and existing deep learning segmentation models often exhibit low accuracy on coronary artery images. Inspired by the Transformer architecture, this paper proposes a novel segmentation model, the double parallel encoder u-net with transformers (DUNETR). This network employed a dual-encoder design integrating Transformers and convolutional neural networks (CNNs). The Transformer encoder transformed three-dimensional (3D) coronary artery data into a one-dimensional (1D) sequential problem, effectively capturing global multi-scale feature information. Meanwhile, the CNN encoder extracted local features of the 3D coronary arteries. The complementary features extracted by the two encoders were fused through the noise reduction feature fusion (NRFF) module and passed to the decoder. Experimental results on a public dataset demonstrated that the proposed DUNETR model achieved a Dice similarity coefficient of 81.19% and a recall rate of 80.18%, representing improvements of 0.49% and 0.46%, respectively, over the next best model in comparative experiments. These results surpassed those of other conventional deep learning methods. The integration of Transformers and CNNs as dual encoders enables the extraction of rich feature information, significantly enhancing the effectiveness of 3D coronary artery segmentation. Additionally, this model provides a novel approach for segmenting other vascular structures.