Frontiers in Bioengineering and Biotechnology最新文献

The bladder is a dynamic organ located in the lower urinary tract, responsible for complex and important physiological activities in the human body, including collecting and storing urine. Severe diseases or bladder injuries often lead to tissue destruction and loss of normal function, requiring surgical intervention and reconstruction. The rapid development of innovative biomaterials has brought revolutionary opportunities for modern urology to overcome the limitations of tissue transplantation. This article first summarized the latest research progress in the processing approaches and functionalization of acellular matrix, hydrogels, nanomaterials, and porous scaffolds in repairing and reconstructing the physiological structure and dynamic function of damaged bladder. Then, we discussed emerging strategies for bladder regeneration and functional recovery, such as cell therapy, organoids, etc. Finally, we outlined the important issues and future development prospects of biomaterials in bladder regeneration to inspire future research directions. By reviewing these innovative biomaterials and technologies, we hope to provide appropriate insights to achieve the ultimate goal of designing and manufacturing artificial bladder substitutes with ideal performance in all aspects.

Introduction: Parkinson's Disease is the second most common neurodegenerative disease in the world. It affects mainly people over 65 and the incidence increases with age. It is characterized by motor and non-motor symptoms and several clinical manifestations. The most evident symptom that affects all patients with Parkinson's Disease is the impairment of motor control, including bradykinesia, tremor, joint rigidity, and postural instability. In the literature, it has been evaluated with muscle synergies, a well-known method for evaluating motor control at the muscular level. However, few studies are available and there is still a major gap to fill to exploit the potential of the method for assessing motor control in Parkinson's Disease, both in the understanding of physiopathology and clinical practice.

Methods: In the light of understanding and fostering future developments for the field, in this review we initially screened 212 papers on Scopus and Web of Science and selected 15 of them to summarize the main features of investigations that employed muscle synergies to analyze patients with Parkinson's Disease. We detailed the features of the screened papers by reporting the clinical findings, a detailed report of EMG processing choices and synergy-based results.

Results: We found that synergistic control is in general altered in patients with Parkinson's Disease, but it can improve if patients are subjected to pharmacological and rehabilitation therapies. Moreover, a further understanding of synergistic control in Parkinson's patients is needed.

Discussion: We discuss the future developments in the field with a detailed assessment of the topic on the view of physicians, including the most promising lines of research for clinical practice and from the perspective of engineers, for methodological application of synergistic approaches.

[This corrects the article DOI: 10.3389/fbioe.2024.1518452.].

Background: Knee osteoarthritis (KOA) constitutes the prevailing manifestation of arthritis. Radiographs function as a common modality for primary screening; however, traditional X-ray evaluation of osteoarthritis confronts challenges such as reduced sensitivity, subjective interpretation, and heightened misdiagnosis rates. The objective of this investigation is to enhance the validation and optimization of accuracy and efficiency in KOA assessment by utilizing fusion deep learning techniques.

Methods: This study aims to develop a highly accurate and lightweight model for automatically predicting and classifying KOA through knee X-ray imaging. We propose a deep learning model named OA-MEN, which integrates a hybrid model combining ResNet and MobileNet feature extraction with multi-scale feature fusion. This approach ensures enhanced extraction of semantic information without losing the advantages of large feature maps provided by high image resolution in lower layers of the network. This effectively expands the model's receptive field and strengthens its understanding capability. Additionally, we conducted unseen-data tests and compared our model with widely used baseline models to highlight its superiority over conventional approaches.

Results: The OA-MEN model demonstrated exceptional performance in tests. In the unseen-data test, our model achieved an average accuracy (ACC) of 84.88% and an Area Under the Curve (AUC) of 89.11%, marking improvements over the best-performing baseline models. These results showcase its improved capability in predicting KOA from X-ray images, making it a promising tool for assisting radiologists in diagnosis and treatment selection in clinical settings.

Conclusion: Leveraging deep learning for osteoarthritis classification guarantees heightened efficiency and accuracy. The future goal is to seamlessly integrate deep learning and advanced computational techniques with the expertise of medical professionals.

An emerging strategy in cancer therapy involves inducing reactive oxygen species (ROS), specifically within tumors using nanozymes. However, existing nanozymes suffer from limitations such as low reactivity, poor biocompatibility, and limited targeting capabilities, hindering their therapeutic efficacy. In response, the PdRu@PEI bimetallic nanoalloys were constructed with well-catalytic activities and effective separation of charges, which can catalyze hydrogen peroxide (H₂O₂) to toxic hydroxyl radical (·OH) under near-infrared laser stimulation. Through facilitating electron transfer and enhancing active sites, the enhanced peroxidase-like (POD-like) enzymatic activity and glutathione (GSH) depletion abilities of nanozymes are boosted through a simple co-reduction process, leading to promising anti-tumor activity. The electron transfer between Pd and Ru of PdRu@PEI nanoalloys contributes to POD-like activity. Then, by oxidizing endogenous overexpressed GSH, enzymatic cycling prevents GSH from consuming ROS. Furthermore, the surface plasmon resonance effect of near-infrared laser on bimetallic nanoalloys ensures its photothermal performance and its local heating, further promoting POD-like activity. The integrated multi-modal therapeutic approach of PdRu@PEI has demonstrated significant anti-cancer effects in vivo studies. The nanozymes exhibit high catalytic efficiency and excellent biocompatibility, offering valuable insights for the development of nano-catalysts/enzymes for biomedical applications.

Salmonella enterica is a foodborne pathogen commonly found in agricultural facilities; its prevalence, as well as increasing levels of disinfectant- and antibiotic-resistance, has significant costs for agriculture as well as human health. In an effort to identify potential new inhibitors of S. enterica on abiotic surfaces, we developed a biomolecule screen of nutrient-type compounds because nutrients would have lower toxicity in animal facilities and bacterial nutrient utilization pathways might prove less susceptible to the development of bacterial resistance. After screening 285 nutrient-type compounds, we identified ten that significantly inhibited the ability of S. enterica to colonize a plastic surface. After conducting a dose-response curve, salicylic acid was selected for further testing due to its low minimal inhibitory concentration (62.5 μM) as well as a low total inhibitory concentration (250 μM). Salicylic acid was also able to inhibit surface colonization of a wide range of bacterial pathogens, suggesting that our biomolecule screen might have broader application beyond S. enterica. Finally, we determined that salicylic acid was also able to inhibit S. enterica colonization of an organic surface on eggshells. Together, these results suggest that nutrient-type biomolecules may provide an avenue for preventing resistant bacteria from contaminating surfaces.

Introduction: Biomechanical simulations can enhance our understanding of spinal disorders. Applied to large cohorts, they can reveal complex mechanisms beyond conventional imaging. Therefore, automating the patient-specific modeling process is essential.

Methods: We developed an automated and robust pipeline that generates and simulates biofidelic vertebrae and intervertebral disc finite element method (FEM) models based on automated magnetic resonance imaging (MRI) segmentations. In a first step, anatomically-constrained smoothing approaches were implemented to ensure seamless contact surfaces between vertebrae and discs with shared nodes. Subsequently, surface meshes were filled isotropically with tetrahedral elements. Lastly, simulations were executed. The performance of our pipeline was evaluated using a set of 30 patients from an in-house dataset that comprised an overall of 637 vertebrae and 600 intervertebral discs. We rated mesh quality metrics and processing times.

Results: With an average number of 21 vertebrae and 20 IVDs per subject, the average processing time was 4.4 min for a vertebra and 31 s for an IVD. The average percentage of poor quality elements stayed below 2% in all generated FEM models, measured by their aspect ratio. Ten vertebra and seven IVD FE simulations failed to converge.

Discussion: The main goal of our work was to automate the modeling and FEM simulation of both patient-specific vertebrae and intervertebral discs with shared-node surfaces directly from MRI segmentations. The biofidelity, robustness and time-efficacy of our pipeline marks an important step towards investigating large patient cohorts for statistically relevant, biomechanical insight.

Introduction: Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings. To address these issues, we propose a novel deep learning-based approach, MIPC-Net, designed for precise boundary segmentation in medical images.

Methods: Our approach, inspired by radiologists' working patterns, introduces two distinct modules: 1. Mutual Inclusion of Position and Channel Attention (MIPC) Module: To improve boundary segmentation precision, we present the MIPC module. This module enhances the focus on channel information while extracting position features and vice versa, effectively enhancing the segmentation of boundaries in medical images. 2. Skip-Residue Module: To optimize the restoration of medical images, we introduce Skip-Residue, a global residual connection. This module improves the integration of the encoder and decoder by filtering out irrelevant information and recovering the most crucial information lost during the feature extraction process.

Results: We evaluate the performance of MIPC-Net on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. The evaluation uses metrics such as the Dice coefficient (DSC) and Hausdorff Distance (HD). Our ablation study confirms that each module contributes to the overall improvement of segmentation quality. Notably, with the integration of both modules, our model outperforms state-of-the-art methods across all metrics. Specifically, MIPC-Net achieves a 2.23 mm reduction in Hausdorff Distance on the Synapse dataset, highlighting the model's enhanced capability for precise image boundary segmentation.

Conclusion: The introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.

[This retracts the article DOI: 10.3389/fbioe.2020.00909.].

[This retracts the article DOI: 10.3389/fbioe.2019.00180.].