生物医学工程学杂志最新文献

To address the challenges in blood cell recognition caused by diverse morphology, dense distribution, and the abundance of small target information, this paper proposes a blood cell detection algorithm - the "You Only Look Once" model based on hybrid mixing attention and deep over-parameters (HADO-YOLO). First, a hybrid attention mechanism is introduced into the backbone network to enhance the model's sensitivity to detailed features. Second, the standard convolution layers with downsampling in the neck network are replaced with deep over-parameterized convolutions to expand the receptive field and improve feature representation. Finally, the detection head is decoupled to enhance the model's robustness for detecting abnormal cells. Experimental results on the Blood Cell Counting Dataset (BCCD) demonstrate that the HADO-YOLO algorithm achieves a mean average precision of 90.2% and a precision of 93.8%, outperforming the baseline YOLO model. Compared with existing blood cell detection methods, the proposed algorithm achieves state-of-the-art detection performance. In conclusion, HADO-YOLO offers a more efficient and accurate solution for identifying various types of blood cells, providing valuable technical support for future clinical diagnostic applications.

Stent migration is one of the common complications following transcatheter valve implantation. This study aims to design a "drum-shaped" balloon-expandable aortic valve stent to address this issue and conduct a mechanical analysis. The implantation process of the stent was evaluated using a method that combines numerical simulation and in vitro experiments. Furthermore, the fatigue process of the stent under pulsatile cyclic loading was simulated, and its fatigue performance was assessed using a Goodman diagram. The process of the stent migrating toward the left ventricular side was simulated, and the force-displacement curve of the stent was extracted to evaluate its anti- migration performance. The results showed that all five stent models could be crimped into a 14F sheath and enabled uniform expansion of the native valve leaflets. The stress in each stent was below the ultimate stress, so no fatigue fracture occurred. As the cell height ratio decreased, the contact area fraction between the stent and the aortic root gradually decreased. However, the mean contact force and the maximum anti-migration force first decreased and then increased. Specifically, model S5 had the smallest contact area fraction but the largest mean contact force and maximum anti-migration force, reaching approximately 0.16 MPa and 10.73 N, respectively. The designed stent achieves a "drum-shaped" change after expansion and has good anti-migration performance.

Colorectal polyps are important early markers of colorectal cancer, and their early detection is crucial for cancer prevention. Although existing polyp segmentation models have achieved certain results, they still face challenges such as diverse polyp morphology, blurred boundaries, and insufficient feature extraction. To address these issues, this study proposes a parallel coordinate fusion network (PCFNet), aiming to improve the accuracy and robustness of polyp segmentation. PCFNet integrates parallel convolutional modules and a coordinate attention mechanism, enabling the preservation of global feature information while precisely capturing detailed features, thereby effectively segmenting polyps with complex boundaries. Experimental results on Kvasir-SEG and CVC-ClinicDB demonstrate the outstanding performance of PCFNet across multiple metrics. Specifically, on the Kvasir-SEG dataset, PCFNet achieved an F1-score of 0.897 4 and a mean intersection over union (mIoU) of 0.835 8; on the CVC-ClinicDB dataset, it attained an F1-score of 0.939 8 and an mIoU of 0.892 3. Compared with other methods, PCFNet shows significant improvements across all performance metrics, particularly in multi-scale feature fusion and spatial information capture, demonstrating its innovativeness. The proposed method provides a more reliable AI-assisted diagnostic tool for early colorectal cancer screening.

Heart sounds are critical for early detection of cardiovascular diseases, yet existing studies mostly focus on traditional signal segmentation, feature extraction, and shallow classifiers, which often fail to sufficiently capture the dynamic and nonlinear characteristics of heart sounds, limit recognition of complex heart sound patterns, and are sensitive to data imbalance, resulting in poor classification performance. To address these limitations, this study proposes a novel heart sound classification method that integrates improved Mel-frequency cepstral coefficients (MFCC) for feature extraction with a convolutional neural network (CNN) and a deep Transformer model. In the preprocessing stage, a Butterworth filter is applied for denoising, and continuous heart sound signals are directly processed without segmenting the cardiac cycles, allowing the improved MFCC features to better capture dynamic characteristics. These features are then fed into a CNN for feature learning, followed by global average pooling (GAP) to reduce model complexity and mitigate overfitting. Lastly, a deep Transformer module is employed to further extract and fuse features, completing the heart sound classification. To handle data imbalance, the model uses focal loss as the objective function. Experiments on two public datasets demonstrate that the proposed method performs effectively in both binary and multi-class classification tasks. This approach enables efficient classification of continuous heart sound signals, provides a reference methodology for future heart sound research for disease classification, and supports the development of wearable devices and home monitoring systems.

Current studies on electroencephalogram (EEG) emotion recognition primarily concentrate on discrete stimulus paradigms under controlled laboratory settings, which cannot adequately represent the dynamic transition characteristics of emotional states during multi-context interactions. To address this issue, this paper proposes a novel method for emotion transition recognition that leverages a cross-modal feature fusion and global perception network (CFGPN). Firstly, an experimental paradigm encompassing six types of emotion transition scenarios was designed, and EEG and eye movement data were simultaneously collected from 20 participants, each annotated with dynamic continuous emotion labels. Subsequently, deep canonical correlation analysis integrated with a cross-modal attention mechanism was employed to fuse features from EEG and eye movement signals, resulting in multimodal feature vectors enriched with highly discriminative emotional information. These vectors are then input into a parallel hybrid architecture that combines convolutional neural networks (CNNs) and Transformers. The CNN is employed to capture local time-series features, whereas the Transformer leverages its robust global perception capabilities to effectively model long-range temporal dependencies, enabling accurate dynamic emotion transition recognition. The results demonstrate that the proposed method achieves the lowest mean square error in both valence and arousal recognition tasks on the dynamic emotion transition dataset and a classic multimodal emotion dataset. It exhibits superior recognition accuracy and stability when compared with five existing unimodal and six multimodal deep learning models. The approach enhances both adaptability and robustness in recognizing emotional state transitions in real-world scenarios, showing promising potential for applications in the field of biomedical engineering.

Protein lysine β-hydroxybutyrylation (Kbhb) is a newly discovered post-translational modification associated with a wide range of biological processes. Identifying Kbhb sites is critical for a better understanding of its mechanism of action. However, biochemical experimental methods for probing Kbhb sites are costly and have a long cycle. Therefore, a feature embedding learning method based on the Transformer encoder was proposed to predict Kbhb sites. In this method, amino acid residues were mapped into numerical vectors according to their amino acid class and position in a learnable feature embedding method. Then the Transformer encoder was used to extract discriminating features, and the bidirectional long short-term memory network (BiLSTM) was used to capture the correlation between different features. In this paper, a benchmark dataset was constructed, and a Kbhb site predictor, AutoTF-Kbhb, was implemented based on the proposed method. Experimental results showed that the proposed feature embedding learning method could extract effective features. AutoTF-Kbhb achieved an area under curve (AUC) of 0.87 and a Matthews correlation coefficient (MCC) of 0.37 on the independent test set, significantly outperforming other methods in comparison. Therefore, AutoTF-Kbhb can be used as an auxiliary means to identify Kbhb sites.

This study aims to investigate the therapeutic efficacy of 50 Hz-0.6 mT low-frequency pulsed electromagnetic field (PEMF) on postmenopausal osteoporosis in ovariectomized rats. Thirty 3-month-old female SD rats were selected and divided into a sham operation group (Sham), an ovariectomized model group (OVX), and a low-frequency pulsed electromagnetic field (PEMF) treatment group, with 10 rats in each group. After 8 weeks, the whole-body bone mineral density (BMD) of each group of rats was measured. The treatment group began to receive PEMF stimulation for 90 minutes daily, while the OVX group only received a simulated placement without electricity. After 6 weeks of intervention, all rats were sacrificed and tested for in vitro BMD, micro-CT, biomechanics, serum biochemical indicators, and bone tissue-related proteins. The results showed that the BMD of the OVX group was significantly lower than that of the Sham group 8 weeks after surgery, indicating successful modeling. After 6 weeks of treatment, compared with the OVX group, the PEMF group exhibited significantly increased BMD in the whole body, femur, and vertebral bodies. Micro-CT analysis results showed improved bone microstructure, significantly increased maximum load and bending strength of the femur, elevated levels of serum bone formation markers, and increased expression of osteogenic-related proteins. In conclusion, this study demonstrates that daily 90-minute exposure to 50 Hz-0.6 mT PEMF effectively enhances BMD, improves bone biomechanical properties, optimizes bone microstructure, stimulates bone formation, and inhibits bone resorption in ovariectomized rats, highlighting its therapeutic potential for postmenopausal osteoporosis.

The three-dimensional (3D) printed bone tissue repair guide scaffold is considered a promising method for treating bone defect repair. In this experiment, chitosan (CS), sodium alginate (SA), and mineralized collagen (MC) were combined and 3D printed to form scaffolds. The experimental results showed that the printability of the scaffold was improved with the increase of chitosan concentration. Infrared spectroscopy analysis confirmed that the scaffold formed a cross-linked network through electrostatic interaction between chitosan and sodium alginate under acidic conditions, and X-ray diffraction results showed the presence of characteristic peaks of hydroxyapatite, indicating the incorporation of mineralized collagen into the scaffold system. In the in vitro collagen release experiments, a weakly alkaline environment was found to accelerate the release rate of collagen, and the release amount increased significantly with a lower concentration of chitosan. Cell experiments showed that scaffolds loaded with mineralized collagen could significantly promote cell proliferation activity and alkaline phosphatase expression. The subcutaneous implantation experiment further verified the biocompatibility of the material, and the implantation of printed scaffolds did not cause significant inflammatory reactions. Histological analysis showed no abnormal pathological changes in the surrounding tissues. Therefore, incorporating mineralized collagen into sodium alginate/chitosan scaffolds is believed to be a new tissue engineering and regeneration strategy for achieving enhanced osteogenic differentiation through the slow release of collagen.

Tumor treating fields (TTF) therapy is an innovative tumor treatment modality. Currently, the TTF devices predominantly employ insulated ceramic electrodes as the electric field transmission medium, resulting in low energy transfer efficiency of the electric field and poor portability of the devices. This study proposed an innovative TTF transmission mode and independently designed a conducted-electrode TTF cell culture dish utilizing inert titanium materials. The electric field conduction characteristics were verified through finite element simulations and experimental tests. Finally, based on the self-manufactured conducted-electrode TTF cell culture dish, experiments on the proliferation inhibition of U87 tumor cells by TTF were conducted. The results demonstrated that under an applied TTF voltage of 10 V and frequency of 200 kHz, the electric field intensities within the medium for conducted and insulated electrodes are approximately 2.5 V/cm and 0.7 V/cm, respectively. Compared to conventional insulated TTF systems, the conducted-electrode TTF configuration exhibited a lower electrode voltage drop and a higher electric field intensity in the culture medium, indicating superior electric field transmission efficiency. Following 36 hours of treatment with conducted-electrode TTF on U87 cells, the proliferation inhibition rate reached approximately 50%, demonstrating effective suppression of tumor cell growth. This approach presents a potential direction for optimizing TTF treatment modality and device design.

To address the current issues of data imbalance and scarcity in photoplethysmography (PPG) data for type 2 diabetes mellitus (T2DM) prediction, this study proposes an improved conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP). The algorithm integrated gated recurrent unit (GRU) networks and self-attention mechanisms to construct a generator, aiming to produce high-quality PPG signals. Various data augmentation methods, including the improved CWGAN-GP, were employed to expand the PPG dataset, and multiple classifiers were applied for T2DM prediction analysis. Experimental results showed that the model trained on data generated by the improved CWGAN-GP achieved the optimal prediction performance. The highest accuracy reached 0.895 0, and compared with other data enhancement methods, this approach exhibited significant advantages in terms of precision and F1-score. The generated data notably enhances the accuracy and generalization ability of T2DM prediction models, providing a more reliable technical basis for non-invasive early T2DM screening based on PPG signals.