Bioengineering最新文献

Harnessing thermal technology has opened up new possibilities in biomedicine in areas such as cancer treatment, biopreservation, and assisted reproduction [...].

The authors regret to pinpoint two editorial errors in [...].

Spinal medical image segmentation is critical for diagnosing and treating spinal disorders. However, ambiguity in anatomical boundaries and interfering factors in medical images often cause segmentation errors. Current deep learning models cannot fully capture the intrinsic data properties, leading to unstable feature spaces. To tackle the above problems, we propose Verdiff-Net, a novel diffusion-based segmentation framework designed to improve segmentation accuracy and stability by learning the underlying data distribution. Verdiff-Net integrates a multi-scale fusion module (MSFM) for fine feature extraction and a noise semantic adapter (NSA) to refine segmentation masks. Validated across four multi-modality spinal datasets, Verdiff-Net achieves a high Dice coefficient of 93%, demonstrating its potential for clinical applications in precision spinal surgery.

Estimating human joint angles is a crucial task in motion analysis, gesture recognition, and motion intention prediction. This paper presents a novel model-based approach for generating reliable and accurate human joint angle estimation using a dual-branch network. The proposed network leverages combined features derived from encoded sEMG signals and RGB-D image data. To ensure the accuracy and reliability of the estimation algorithm, the proposed network employs a convolutional autoencoder to generate a high-level compression of sEMG features aimed at motion prediction. Considering the variability in the distribution of sEMG signals, the proposed network introduces a vision-based joint regression network to maintain the stability of combined features. Taking into account latency, occlusion, and shading issues with vision data acquisition, the feature fusion network utilizes high-frequency sEMG features as weights for specific features extracted from image data. The proposed method achieves effective human body joint angle estimation for motion analysis and motion intention prediction by mitigating the effects of non-stationary sEMG signals.

As an essential thrombolytic agent, the tissue plasminogen activator receives increasing attention due to its longer half-life, lower immunogenicity, and easier administration, which are superior to other thrombolytic agents. In this study, the isolated and purified plasminogen activator from the sandworm (Perinereis aibuhitensis) was expressed in E. coli (Escherichia coli) to investigate its potential for simplifying the development process. The sandworm plasminogen activator was previously successfully cloned and expressed in E. coli with low yield and activity in the culture supernatant. This low yield and activity prompted us to optimize its DNA sequence. Furthermore, to raise the efficiency in the separation of the target protein, the protein's solubility was enhanced by fusing it with maltose-binding protein (MBP) tags. Eventually, the fibrinolytic activity was successfully restored after digestion with tobacco etch virus (TEV) protease. This study provides an innovative method of efficiently expressing and purifying plasminogen activators from sandworm in E. coli and broadens its applications in therapeutic treatment of cardiovascular diseases, including thrombosis, stroke, and coronary atherosclerotic heart disease.

Targeted drug delivery has emerged as a transformative approach in the treatment of periorbital skin malignancies, offering the potential for enhanced efficacy and reduced side effects compared to traditional therapies. This review provides a comprehensive overview of targeted therapies in the context of periorbital malignancies, including basal cell carcinoma, squamous cell carcinoma, sebaceous gland carcinoma, and Merkel cell carcinoma. It explores the mechanisms of action for various targeted therapies, such as monoclonal antibodies, small molecule inhibitors, and immunotherapies, and their applications in treating these malignancies. Additionally, this review addresses the management of ocular and periocular side effects associated with these therapies, emphasizing the importance of a multidisciplinary approach to minimize impact and ensure patient adherence. By integrating current findings and discussing emerging trends, this review aims to highlight the advancements in targeted drug delivery and its potential to improve treatment outcomes and quality of life for patients with periorbital skin malignancies.

This study evaluates the thermal impact of a one-drill protocol for osteotomy preparation in dental implant surgery. Our findings demonstrate a significant reduction in heat generation compared to traditional sequential drilling, suggesting potential benefits for implant osseointegration and patient comfort. Specifically, the one-drill protocol was associated with lower peak temperatures and a reduced duration of elevated temperatures. These findings suggest that the one-drill protocol may contribute to improved implant stability and reduce the risk of thermal-induced bone damage. While further research is needed to confirm these findings in clinical settings, the results of this study provide promising evidence for the potential advantages of the one-drill protocol in dental implant surgery. Additionally, the one-drill protocol may offer simplified surgical workflows and reduced instrument management, potentially leading to improved efficiency and cost-effectiveness in dental implant procedures.

Polyaxial locking systems are widely used for strategic surgical placement, particularly in cases of osteoporotic bones, comminuted fractures, or when avoiding pre-existing prosthetics. However, studies suggest that polyaxiality negatively impacts system stiffness. We hypothesize that a new plate design, combining a narrow plate with asymmetric holes and polyaxial capabilities, could outperform narrow plates with symmetric holes. Three configurations were tested: Group 1 with six orthogonal screws, and Groups 2 and 3 with polyaxiality in the longitudinal and transverse axes, respectively. A biomechanical model assessed the bone/plate/screw interface under cyclic compression (5000 cycles) and torsion loads until failure. Screws were inserted up to 10° angle. None of the groups showed a significant loss of stiffness during compression (p > 0.05). Group 1 exhibited the highest initial stiffness, followed by Group 3 (<29%) and Group 2 (<35%). In torsional testing, Group 1 achieved the most load cycles (29.096 ± 1.342), while Groups 2 and 3 showed significantly fewer cycles to failure (6.657 ± 3.551 and 4.085 ± 1.934). These results confirm that polyaxiality, while beneficial for surgical placement, reduces biomechanical performance under torsion. Despite this, no group experienced complete decoupling of the screw-plate interface, indicating the robustness of the locking mechanism even under high stress.

In many cases, the etiopathogenesis of oral cavity diseases depends on the presence of variants in some genes. Being able to identify these variants defines the possibilities and limits of therapies. This multidisciplinary case describes several pathologies of the oral cavity in a young patient affected by type 1 diabetes. The patient presented with an impacted palatal canine. Further investigation revealed cervical root resorption of the upper right central incisor. Genetic testing was performed for interleukin, VDR receptor genes, and the evaluation of periodontopathogenic bacteria. The mutational analysis carried out for the VDR polymorphisms and the IL1A, IL1B, IL6, and IL10 polymorphisms showed the presence of pathogenetic variants. The results for bacterial load showed the presence of periodontal pathogenes. The first intervention was the intentional replantation of the incisor. The second intervention was the orthodontic recovery of the impacted canine, using light forces and a hybrid anchorage with a miniscrew. At the end of orthodontic treatment, a crack was found in the upper left first premolar, which was extracted. Throughout treatment, non-invasive periodontal interventions were performed periodically to control periodontal inflammation. This case is an example of the integration of genetic analyses into the multidisciplinary diagnostic pathway.

Background: Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis is critical for effective treatment. However, manual diagnosis using MRI images is time consuming and subjective, leading to a need for automated methods.

Objective: This study aims to develop a convolutional neural network (CNN)-based deep learning model integrated with multiple attention mechanisms to improve the accuracy and robustness of LSS classification via MRI images.

Methods: The proposed model is trained on a standardized MRI dataset sourced from multiple institutions, encompassing various lumbar degenerative conditions. During preprocessing, techniques such as image normalization and data augmentation are employed to enhance the model's performance. The network incorporates a Multi-Headed Self-Attention Module, a Slot Attention Module, and a Channel and Spatial Attention Module, each contributing to better feature extraction and classification.

Results: The model achieved 95.2% classification accuracy, 94.7% precision, 94.3% recall, and 94.5% F1 score on the validation set. Ablation experiments confirmed the significant impact of the attention mechanisms in improving the model's classification capabilities.

Conclusion: The integration of multiple attention mechanisms enhances the model's ability to accurately classify LSS in MRI images, demonstrating its potential as a tool for automated diagnosis. This study paves the way for future research in applying attention mechanisms to the automated diagnosis of lumbar spinal stenosis and other complex spinal conditions.