生物医学工程学杂志最新文献

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.

To facilitate the early intelligent screening of pediatric genu valgum, this study develops a deep learning-based gait recognition model tailored for clinical application. The model is constructed upon a three-dimensional residual network architecture and incorporates a triplet attention module alongside a spatial hierarchical pooling module, jointly enhancing feature interaction across temporal, spatial, and channel dimensions. This design ensures an optimal balance between representational capacity and computational efficiency. Evaluated on a self-constructed dataset, the model achieves precision of 98.0%, 97.1%, and 96.5%, recall rates of 97.5%, 97.0%, and 95.0%, and F ₁-scores of 0.98, 0.97, and 0.96 on the training, validation, and test sets, respectively, demonstrating excellent recognition performance and strong generalization ability. Ablation experiments confirm the importance of the proposed model's core components in improving performance, and comparative experiments further highlight its significant advantages in recognition accuracy and robustness. Visualization experiments reveal that the model effectively focuses on key regions of gait images, with attention regions aligning closely with clinical anatomical landmarks, thereby enhancing the interpretability of the model's decision-making in clinical applications. In summary, the proposed model not only offers an efficient and reliable technical solution for early intelligent screening of genu valgum in children, but also provides a practical pathway for applying gait recognition technology in medical diagnosis.

Thyroid nodules are a common endocrine disorder, and their early detection and accurate diagnosis are crucial for the prevention of thyroid cancer. However, the highly heterogeneous morphology and boundaries of thyroid nodules pose significant challenges to their precise identification and classification. Traditional diagnostic approaches rely heavily on physicians' experience, which increases the risk of misdiagnosis and missed diagnoses. With the rapid advancement of computer-aided diagnosis (CAD) technologies, applying deep learning algorithms to the analysis of thyroid nodule ultrasound images has shown great potential. This paper reviews the latest research progress on deep learning-based CAD methods for thyroid nodules, with a focus on their applications in image preprocessing, segmentation and classification. The advantages and limitations of current techniques are analyzed, and potential future directions are discussed. This review aims to highlight the potential of deep learning in thyroid nodule diagnosis and to provide a foundation for selecting feasible pathways for future clinical applications.

It has been found that the incidence of cardiovascular disease in patients with lower limb amputation is significantly higher than that in normal people, and the risk of developing coronary atherosclerosis is much higher than that in other high-risk groups. Numerous studies have confirmed that high systolic and diastolic blood pressures are potential risk factors for coronary artery disease, and it has been demonstrated that the ascending aortic pressure during diastole increases after amputation. However, the relationship between lower limb amputation and coronary atherosclerosis has not been fully explained from the perspective of hemodynamic environment. Therefore, in this study, a centralized parameter model of the human cardiovascular system and a three-dimensional model of the left coronary artery were established to investigate the effect of amputation on the hemodynamic environment of the coronary artery. The results showed that the abnormal hemodynamic environment induced by amputation, characterized by factors such as increased diastolic pressure in the ascending aorta, led to a significant expansion of the low wall shear stress (WSS) region on the outer lateral aspect of the left coronary artery bifurcation during diastole. The maximum observed increase in the area of low WSS reached up to 50.5%. This abnormal hemodynamic environment elevates the risk of plaque formation in the left coronary artery. Moreover, the more severe the lower limb atrophy, the greater the risk of coronary atherosclerosis in amputees. This study preliminarily reveals the effect of lower limb amputation on the hemodynamic environment of the left coronary artery.

Most current medical image segmentation models are primarily built upon the U-shaped network (U-Net) architecture, which has certain limitations in capturing both global contextual information and fine-grained details. To address this issue, this paper proposes a novel U-shaped network model, termed the Multi-View U-Net (MUNet), which integrates self-attention and multi-view attention mechanisms. Specifically, a newly designed multi-view attention module is introduced to aggregate semantic features from different perspectives, thereby enhancing the representation of fine details in images. Additionally, the MUNet model leverages a self-attention encoding block to extract global image features, and by fusing global and local features, it improves segmentation performance. Experimental results demonstrate that the proposed model achieves superior segmentation performance in coronary artery image segmentation tasks, significantly outperforming existing models. By incorporating self-attention and multi-view attention mechanisms, this study provides a novel and efficient modeling approach for medical image segmentation, contributing to the advancement of intelligent medical image analysis.

Accurate detection of cephalometric landmarks is crucial for orthodontic diagnosis and treatment planning. Current landmark detection methods are mainly divided into heatmap-based and regression-based approaches. However, these methods often rely on parallel computation of multiple models to improve accuracy, significantly increasing the complexity of training and deployment. This paper presented a novel regression method that can simultaneously detect all cephalometric landmarks in high-resolution X-ray images. By leveraging the encoder module of Transformer, a dual-encoder model was designed to achieve coarse-to-fine localization of cephalometric landmarks. The entire model consisted of three main components: a feature extraction module, a reference encoder module, and a fine-tuning encoder module, responsible for feature extraction and fusion of X-ray images, coarse localization of cephalometric landmarks, and fine localization of landmarks, respectively. The model was fully end-to-end differentiable and could learn the intercorrelation relationships between cephalometric landmarks. Experimental results showed that the successful detection rate (SDR) of our algorithm was superior to other existing methods. It attained the highest 2 mm SDR of 89.51% on test set 1 of the ISBI2015 dataset and 90.68% on the test set of the ISBI2023 dataset. Meanwhile, it reduces memory consumption and enhances the model's popularity and applicability, providing more reliable technical support for orthodontic diagnosis and treatment plan formulation.

Endometrial cancer (EC) is one of the most common gynecological malignancies, with an increasing incidence rate worldwide. Accurate segmentation of lesion areas in computed tomography (CT) images is a critical step in assisting clinical diagnosis. In this study, we propose a novel deep learning-based segmentation model, termed spatial choice and weight union network (SCWU-Net), which incorporates two newly designed modules: the spatial selection module (SSM) and the combination weight module (CWM). The SSM enhances the model's ability to capture contextual information through deep convolutional blocks, while the CWM, based on joint attention mechanisms, is employed within the skip connections to further boost segmentation performance. By integrating the strengths of both modules into a U-shaped multi-scale architecture, the model achieves precise segmentation of EC lesion regions. Experimental results on a public dataset demonstrate that SCWU-Net achieves a Dice similarity coefficient (DSC) of 82.98%, an intersection over union (IoU) of 78.63%, a precision of 92.36%, and a recall of 84.10%. Its overall performance is significantly outperforming other state-of-the-art models. This study enhances the accuracy of lesion segmentation in EC CT images and holds potential clinical value for the auxiliary diagnosis of endometrial cancer.

To address issues such as loss of detailed information, blurred target boundaries, and unclear structural hierarchy in medical image fusion, this paper proposes an adaptive feature medical image fusion network based on a full-scale diffusion model. First, a region-level feature map is generated using a kernel-based saliency map to enhance local features and boundary details. Then, a full-scale diffusion feature extraction network is employed for global feature extraction, alongside a multi-scale denoising U-shaped network designed to fully capture cross-layer information. A multi-scale feature integration module is introduced to reinforce texture details and structural information extracted by the encoder. Finally, an adaptive fusion scheme is applied to progressively fuse region-level features, global features, and source images layer by layer, enhancing the preservation of detail information. To validate the effectiveness of the proposed method, this paper validates the proposed model on the publicly available Harvard dataset and an abdominal dataset. By comparing with nine other representative image fusion methods, the proposed approach achieved improvements across seven evaluation metrics. The results demonstrate that the proposed method effectively extracts both global and local features of medical images, enhances texture details and target boundary clarity, and generates fusion image with high contrast and rich information, providing more reliable support for subsequent clinical diagnosis.