Bioengineering最新文献

Alzheimer's disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, given the limitations of current diagnostic biomarkers and the need for non-invasive, accessible tools. This study aims to address these gaps by exploring driving performance as a novel, non-invasive biomarker for MCI detection. Using the LiveDrive AI system, equipped with multimodal sensing (MMS) technology and a driving performance assessment strategy, the proposed work analyzes the predictive capacity of driving patterns in indicating cognitive decline. Machine learning models, trained on an expert-annotated in-house dataset, were employed to detect MCI status from driving performance. Key findings demonstrate the feasibility of using nuanced driving features, such as velocity and acceleration during turning, as indicators of cognitive decline. This approach holds promise for integration into smartphone or car applications, enabling real-time, continuous cognitive health monitoring. The implications of this work suggest a transformative step towards scalable, real-world solutions for early AD diagnosis, with the potential to improve patient outcomes and disease management.

Being essential intermediates for the biosynthesis of heme, chlorophyll, and several other biologically critical compounds, porphyrins have wide practical applications. However, up till now, their bio-based production remains challenging. In this study, we identified potential metabolic factors limiting the biosynthesis of type-III stereoisomeric porphyrins in Escherichia coli. To alleviate this limitation, we developed bioprocessing strategies by redirecting more dissimilated carbon flux toward the HemD-enzymatic pathway to enhance the production of type-III uroporphyrin (UP-III), which is a key precursor for heme biosynthesis. Our approaches included the use of antioxidant reagents and strain engineering. Supplementation with ascorbic acid (up to 1 g/L) increased the UP-III/UP-I ratio from 0.62 to 2.57. On the other hand, overexpression of ROS-scavenging genes such as sod- and kat-genes significantly enhanced UP production in E. coli. Notably, overexpression of sodA alone led to a 72.9% increase in total porphyrin production (1.56 g/L) while improving the UP-III/UP-I ratio to 1.94. Our findings highlight the potential of both antioxidant supplementation and strain engineering to mitigate ROS-induced oxidative stress and redirect more dissimilated carbon flux toward the biosynthesis of type-III porphyrins in E. coli. This work offers an effective platform to enhance the bio-based production of porphyrins.

In quiet standing, the central nervous system implements a pre-programmed ankle strategy of postural control to maintain upright balance and stability. This strategy comprises a synchronized common neural drive delivered to synergistically grouped muscles. This study evaluated connectivity between EMG signals of the unilateral and bilateral homologous muscle pairs of the lower legs during various standing balance conditions using magnitude-squared coherence (MSC). The leg muscles examined included the right and left tibialis anterior (TA), medial gastrocnemius (MG), and soleus (S). MSC is a frequency domain measure that quantifies the linear phase relation between two signals and was analyzed in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz) neural frequency bands for feet together and feet tandem, with eyes open and eyes closed conditions. Results showed that connectivity in the beta and lower and upper gamma bands (30-100 Hz) was influenced by standing balance conditions and was indicative of a neural drive originating from the motor cortex. Instability was evaluated by comparing less stable standing conditions with a baseline-eyes open feet together stance. Changes in connectivity in the beta and gamma bands were found to be most significant in the muscle pairs of the back leg during a tandem stance regardless of dominant foot placement. MSC identified the MG:S muscle pair as significant for the right and left leg. The results of this study provided insight into the neural mechanism of postural control.

Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. Specifically, unrealistic segmentation predictions deviating from actual anatomical structures, known as a Seg-Hallucination, often occur in deep learning-based models. The Seg-Hallucinations can result in erroneous quantitative analyses and distort critical imaging biomarker information, yet effective audits or corrections to address these issues are rare. Therefore, we propose an automated Seg-Hallucination surveillance and correction (ASHSC) algorithm utilizing only 3D organ mask information derived from CT images without reliance on the ground truth. Two publicly available datasets were used in developing the ASHSC algorithm: 280 CT scans from the TotalSegmentator dataset for training and 274 CT scans from the Cancer Imaging Archive (TCIA) dataset for performance evaluation. The ASHSC algorithm utilizes a two-stage on-demand strategy with mesh-based convolutional neural networks and generative artificial intelligence. The segmentation quality level (SQ-level)-based surveillance stage was evaluated using the area under the receiver operating curve, sensitivity, specificity, and positive predictive value. The on-demand correction performance of the algorithm was assessed using similarity metrics: volumetric Dice score, volume error percentage, average surface distance, and Hausdorff distance. Average performance of the surveillance stage resulted in an AUROC of 0.94 ± 0.01, sensitivity of 0.82 ± 0.03, specificity of 0.90 ± 0.01, and PPV of 0.92 ± 0.01 for test dataset. After the on-demand refinement of the correction stage, all the four similarity metrics were improved compared to a single use of the AI-segmentation model. This study not only enhances the efficiency and reliability of handling the Seg-Hallucination but also eliminates the reliance on ground truth. The ASHSC algorithm offers intuitive 3D guidance for uncertainty regions, while maintaining manageable computational complexity. The SQ-level-based on-demand correction strategy adaptively minimizes uncertainties inherent in deep-learning-based organ masks and advances automated auditing and correction methodologies.

The WHO grading of pancreatic neuroendocrine neoplasms (PanNENs) is essential in patient management and an independent prognostic factor for patient survival. Radiomics features from CE-CT images hold promise for the outcome and tumor grade prediction. However, variations in reconstruction parameters can impact the predictive value of radiomics. 127 patients with histopathologically confirmed PanNENs underwent CT scans with filtered back projection (B20f) and iterative (I26f) reconstruction kernels. 3190 radiomic features were extracted from tumors and pancreatic volumes. Wilcoxon paired tests assessed the impact of reconstruction kernels and ComBat harmonization efficiency. SVM models were employed to predict tumor grade using the entire set of radiomics features or only those identified as harmonizable. The models' performance was assessed on an independent dataset of 36 patients. Significant differences, after correction for multiple testing, were observed in 69% of features in the pancreatic volume and 51% in the tumor volume with B20f and I26f kernels. SVM models demonstrated accuracy ranging from 0.67 (95%CI: 0.50-0.81) to 0.83 (95%CI: 0.69-0.94) in distinguishing grade 1 cases from higher grades. Reconstruction kernels alter radiomics features and iterative kernel models trended towards higher performance. ComBat harmonization mitigates kernel impacts but addressing this effect is crucial in studies involving data from different kernels.

Aim: The purpose of this study was to evaluate the accuracy and efficacy of a new wireframe template methodology in analyzing three-dimensional facial soft tissue asymmetry.

Materials and methods: Three-dimensional facial soft tissue data were obtained for 24 patients. The wireframe template was established by identifying 34 facial landmarks and then forming a template on the face with the MeshLab 2020 software. The angle asymmetry index was automatically scored using the template. The mirroring and overlapping technique is accepted as the golden standard method to diagnose facial asymmetry by acquiring deviation values of one's face. Consistency rates between the two methodologies were determined through a statistical comparison of the angle asymmetry index and deviation values.

Results: Overall consistency rates in the labial, mandibular angle, cheek, chin, and articular regions were 87.5%, 95.8%, 87.5%, 91.7%, and 100%, respectively. Regions with consistency rates in three dimensions of more than 85% are the x-axis and the z-axis of all regions and the y-axis of the mandibular angle, chin, and articular region.

Conclusions: Soft tissue facial asymmetry can be diagnosed accurately and effectively by using a three-dimensional soft tissue spatial angle wireframe template. Precise localization of asymmetry can be offered, and indiscernible tiny asymmetry can be identified.

Thermal therapy is a commonly used local treatment technique in clinical practice. Monitoring the treatment process is essential for ensuring its success. In this review, we analyze recent image-based methods for thermal therapy monitoring, focusing particularly on their feasibility for synchronous or immediate postoperative monitoring. This includes thermography and other techniques that track the physical changes in tissue during thermal ablation. Potential directions and challenges for further clinical applications are also summarized.

Bioprinting, an innovative combination of biotechnology and additive manufacturing, has emerged as a transformative technology in healthcare, enabling the fabrication of functional tissues, organs, and patient-specific implants. The implementation of the aforementioned, however, introduces unique intellectual property (IP) challenges that extend beyond conventional biotechnology. The study explores three critical areas of concern: IP protection for bioprinting hardware and bioinks, ownership and ethical management of digital files derived from biological data, and the implications of commercializing bioprinted tissues and organs. Employing a multidisciplinary approach, the paper analyzes existing IP frameworks, highlights their limitations when applied to bioprinting, and examines ethical dilemmas, such as ownership of bioprinted human tissues and the commodification of biological innovations. Findings suggest that current IP laws inadequately address the complexities of bioprinting, particularly in managing the intersection of proprietary technologies and ethical considerations. The study underscores the need for adaptive legal and ethical frameworks to balance innovation with equitable access and sustainability. Recommendations include the development of tailored IP policies for bioprinting and enhanced international collaboration to harmonize legal protections across jurisdictions. This work aims to provide a comprehensive foundation for stakeholders to navigate the rapidly evolving landscape of bioprinting IP.

Osteoarthritis (OA) is a common joint disease characterized by pain and functional impairment, which severely impacts the quality of life of middle-aged and elderly individuals. During normal bone development, chondrocyte hypertrophy is a natural physiological process. However, in the progression of OA, chondrocyte hypertrophy becomes one of its key pathological features. Although there is no definitive evidence to date confirming that chondrocyte hypertrophy is the direct cause of OA, substantial experimental data indicate that it plays an important role in the disease's pathogenesis. In this review, we first explore the mechanisms underlying chondrocyte hypertrophy in OA and offer new insights. We then propose strategies for inhibiting chondrocyte hypertrophy from the perspectives of targeting signaling pathways and tissue engineering, ultimately envisioning the future prospects of OA treatment.

Three-dimensional printing was introduced in the 1980s, though bioprinting started developing a few years later. Today, 3D bioprinting is making inroads in medical fields, including the production of biomedical supplies intended for internal use, such as biodegradable staples. Medical bioprinting enables versatility and flexibility on demand and is able to modify and individualize production using several established printing methods. A great selection of biomaterials and bioinks is available, including natural, synthetic, and mixed options; they are biocompatible and non-toxic. Many bioinks are biodegradable and they accommodate cells so upon implantation, they integrate within the new environment. Bioprinting is suitable for printing tissues using living or viable components, such as collagen scaffolding, cartilage components, and cells, and also for printing parts of structures, such as teeth, using artificial man-made materials that will become embedded in vivo. Bioprinting is an integral part of tissue engineering and regenerative medicine. The addition of newly developed smart biomaterials capable of incorporating dynamic changes in shape depending on the nature of stimuli led to the addition of the fourth dimension of time in the form of changing shape to the three static dimensions. Four-dimensional bioprinting is already making significant inroads in tissue engineering and regenerative medicine, including new ways to create dynamic tissues. Its future lies in constructing partial or whole organ generation.