Bioengineering最新文献

Tremor is one of the most common symptoms of Parkinson's disease (PD), assessed using clinician-assigned clinical scales, which can be subjective and prone to variability. This study evaluates the potential of unsupervised learning for the classification and assessment of tremor severity from wearable sensor data. We analyzed 25 resting tremor signals from 24 participants (13 PD patients and 11 controls), focusing on motion intensities derived from accelerometer recordings. The k-means clustering algorithm was employed, achieving a classification accuracy of 76% for tremor versus non-tremor states. However, performance decreased for multiclass tremor severity classification (57.1%) and binary classification of severe versus mild tremor (71.4%), highlighting challenges in detecting subtle intensity variations. The findings underscore the utility of unsupervised learning in enabling scalable, objective tremor analysis. Integration of such models into wearable systems could improve continuous monitoring, enhance rehabilitation strategies, and support standardized clinical assessments. Future work should explore advanced algorithms, enriched feature sets, and larger datasets to improve robustness and generalizability.

Background: Cholesterol gallstone disease (CGS) is often accompanied by gallbladder contraction dysfunction and chronic inflammation, but effective therapeutic options remain limited. This study investigates whether a low-intensity pulsed ultrasound (LIPUS) treatment can improve gallbladder motility and alleviate chronic inflammation while exploring the underlying mechanisms.

Methods: Gallbladder motility was assessed through in vitro and in vivo contraction tests, while bile condition was evaluated by observing bile crystal clearance. Tissue analysis and Western blotting were performed to examine the expression of the cholecystokinin A receptor (CCKAR) and α-smooth muscle actin (α-SMA) as markers of gallbladder smooth muscle health and the inflammatory microenvironment. Blood cholesterol levels were measured via biochemical assays.

Results: LIPUS treatment obviously enhanced gallbladder contractility in response to CCK-8 stimulation and accelerated bile crystal clearance. It also reduced inflammatory cell infiltration and tissue edema, and promoted new capillary formation in the gallbladder, mitigating the progression of CGS. Furthermore, LIPUS restored CCKAR expression and improved the thickness of the gallbladder smooth muscle layer, providing a structural basis for increased smooth muscle contractility.

Conclusion: LIPUS improves gallbladder motility and reduces chronic inflammation in CGS by enhancing CCKAR expression and smooth muscle integrity. These findings highlight the potential of LIPUS as a non-invasive therapeutic approach for managing CGS.

In this study, a pilot-scale in-vessel composter was used to treat a mixture of industrial biowaste, with soybean curd residue and saw dust as the major substrates. The composter is capable of treating up to 350 tons/month of waste, producing up to 150 tons/month of high-quality compost within a retention time of 7-10 days. The final compost has an average nitrogen-phosphorus-potassium content of 6%, moisture content of 28%, pH of 6.1, organic matter of 68%, and carbon-nitrogen ratio of 19:1. It also has a good amount of humic acid and macronutrients. Composts from all stages of the composting process-pre-mix, directly after discharge, after one-month of curing, and right before packaging-were evaluated with metagenomic analysis to identify the microbes that may add value to the compost.

The motion control of the virtual avatar enhances a sense of embodiment in a virtual reality (VR). Yet, the detailed relationship between motion control, assigned tasks, and the sense of embodiment remains unclear. We aim to investigate the relationships between degrees of control on a full-body avatar and three elements of the sense of embodiment: the sense of self-location, agency, and ownership in standalone and interaction tasks. To do this, we conducted a user study with three conditions of control over a full-body avatar. The types of control are (1) Low-control of an upper-body avatar, (2) Mid-control of a full-body avatar from three sensors, and (3) High-control of a full-body avatar from six sensors. These three control methods, which were used to animate the avatars and imitate the users' pose, differ in accuracy and stability. Participants embodied three kinds of control and performed a single-user task (obstacle avoidance) and a multi-user task (catch-ball). Our results indicate that the degree of control impacts participants' embodiment. However, there was no significant difference between high- and mid-control in the multi-user task, which was a different result from the single-user task. This suggests for virtual bodies that the participants capacity to control and see are the same or different, which may affect embodiment. Our result also shows that the multi-user task enhanced the sense of embodiment compared to the single-user task in the low- and mid-control avatars. Yet, the multi-user task decreased the sense of agency of the high-control avatar. This suggests that a failure of the assigned task may affect the sense of agency, especially when it is close to success, yielding revulsion. We further elucidate the insights into the relationship between the degree of control, the assigned tasks, and the elements of a sense of embodiment.

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitations. Existing imputation methods for dropout events lack comprehensive evaluations in downstream analyses and do not demonstrate robustness across various scenarios. In response to this challenge, we propose a consensus clustering-based imputation (CCI) method. CCI performs clustering on each subset of data sampling across genes and summarizes clustering outcomes to define cellular similarities. CCI leverages the information from similar cells and employs the similarities to impute gene expression levels. Our comprehensive evaluations demonstrate that CCI not only reconstructs the original data pattern, but also improves the performance of downstream analyses. CCI outperforms existing methods for data imputation under different scenarios, exhibiting accuracy, robustness, and generalization.

Traditional methods for evaluating tennis technique, such as visual observation and video analysis, are often subjective and time consuming. On the other hand, a quick and accurate assessment can provide immediate feedback to players and contribute to technical development, particularly in less experienced athletes. This study aims to validate the use of a single inertial measurement system to assess some relevant technical parameters of amateur players. Among other things, we attempt to search for significant correlations between the flexion extension and torsion of the torso and the lateral distance of the ball from the body at the instant of impact. This research involved a group of amateur players who performed a series of standardized gestures (forehands and serves) wearing a sensorized chest strap fitted with a wireless inertial unit. The collected data were processed to extract performance metrics. The percentage coefficient of variation for repeated measurements, Wilcoxon signed-rank test, and Spearman's correlation were used to determine the system's reliability. High reliability was found between sets of measurements in all of the investigated parameters. The statistical analysis showed moderate and strong correlations, suggesting possible applications in assessing and optimizing specific aspects of the technique, like the player's distance to the ball in the forehand or the toss in the serve. The significant variations in technical execution among the subjects emphasized the need for tailored interventions through personalized feedback. Furthermore, the system allows for the highlighting of specific areas where intervention can be achieved in order to improve gesture execution. These results prompt us to consider this system's effectiveness in developing an on-court mobile application.

Ex vivo regional gene therapy is a promising tissue-engineering strategy for bone regeneration: osteogenic mesenchymal stem cells (MSCs) can be genetically modified to express an osteoinductive stimulus (e.g., bone morphogenetic protein-2), seeded onto an osteoconductive scaffold, and then implanted into a bone defect to exert a therapeutic effect. Compared to recombinant human BMP-2 (rhBMP-2), which is approved for clinical use, regional gene therapy may have unique benefits related to the addition of MSCs and the sustained release of BMP-2. However, the cellular and transcriptional mechanisms regulating the response to these two strategies for BMP-2 mediated bone regeneration are largely unknown. Here, for the first time, we performed single-cell RNA sequencing (10x Genomics) of hematoma tissue in six rats with critical-sized femoral defects that were treated with either regional gene therapy or rhBMP-2. Our unbiased bioinformatic analysis of 2393 filtered cells in each group revealed treatment-specific differences in their cellular composition, transcriptional profiles, and cellular communication patterns. Gene therapy treatment induced a more robust chondrogenic response, as well as a decrease in the proportion of fibroblasts and the expression of profibrotic pathways. Additionally, gene therapy was associated with an anti-inflammatory microenvironment; macrophages expressing canonical anti-inflammatory markers were more common in the gene therapy group. In contrast, pro-inflammatory markers were more highly expressed in the rhBMP-2 group. Collectively, the results of our study may offer insights into the unique pathways through which ex vivo regional gene therapy can augment bone regeneration compared to rhBMP-2. Furthermore, an improved understanding of the cellular pathways involved in segmental bone defect healing may allow for the further optimization of regional gene therapy or other bone repair strategies.

Spheroids serve as the building blocks for three-dimensional (3D) bioprinted tissue patches. When larger than 500 μm, the desired size for 3D bioprinting, they tend to have a hypoxic core with necrotic cells. Therefore, it is critical to assess the viability of spheroids in order to ensure the successful fabrication of high-viability patches. However, current viability assays are time-consuming, labor-intensive, require specialized training, or are subject to human bias. In this study, we build a convolutional neural network (CNN) model to efficiently and accurately predict spheroid viability, using a phase-contrast image of a spheroid as its input. A comprehensive dataset of mouse mesenchymal stem cell (mMSC) spheroids of varying sizes with corresponding viability percentages, which was obtained through CCK-8 assays, was established and used to train and validate the model. The model was trained to automatically classify spheroids into one of four distinct categories based on their predicted viability: 0-20%, 20-40%, 40-70%, and 70-100%. The model achieved an average accuracy of 92%, with a consistent loss below 0.2. This deep-learning model offers a non-invasive, efficient, and accurate method to streamline the assessment of spheroid quality, thereby accelerating the development of bioengineered cardiac tissue patches for cardiovascular disease therapies.

(1) Background: The incidence of fragility fractures of the pelvis (FFP) has increased significantly over the past decades. Unilateral non-displaced fractures, defined as FFP II, are the most common type of fracture. When conservative treatment fails, surgical treatment is indicated. We hypothesize that the use of bilateral SI screws (BSIs) or a transsacral screw (TSI) is superior compared to a unilateral screw (USI) because of a significant reduction in the risk of adjacent fractures and a reduction in fracture progression. (2) Methods: A finite element model of a female pelvic ring was constructed. The ligaments were simulated as tension springs. The load was applied through the sacrum with the pelvis fixed to both acetabula. An FFP IIc was simulated and fixed with either a USI or BSI or TSI. The models were analyzed for a quantitative statement of stress and fracture dislocation. (3) Results: The BSI and TSI resulted in less dislocation compared to the USI. The stress distribution on both sides of the sacrum was favorable in the BSI and TSI groups. The BSI resulted in a higher rotational stability compared to the TSI. (4) Conclusions: The use of either a BSI or TSI for fixation of unilateral FFP is biomechanically favorable compared to the use of a USI. In addition, the use of a BSI or TSI reduces the stress on the contralateral uninjured side of the sacrum. This may reduce the risk of an adjacent fracture or fracture progression.

With the aging population rising, the decline in spatial cognitive ability has become a critical issue affecting the quality of life among the elderly. Electroencephalogram (EEG) signal analysis presents substantial potential in spatial cognitive assessments. However, conventional methods struggle to effectively classify spatial cognitive states, particularly in tasks requiring multi-class discrimination of pre- and post-training cognitive states. This study proposes a novel approach for EEG signal classification, utilizing Permutation Conditional Mutual Information (PCMI) for feature extraction and a Multi-Scale Squeezed Excitation Convolutional Neural Network (MSSECNN) model for classification. Specifically, the MSSECNN classifies spatial cognitive states into two classes-before and after cognitive training-based on EEG features. First, the PCMI extracts nonlinear spatial features, generating spatial feature matrices across different channels. SENet then adaptively weights these features, highlighting key channels. Finally, the MSCNN model captures local and global features using convolution kernels of varying sizes, enhancing classification accuracy and robustness. This study systematically validates the model using cognitive training data from a brain-controlled car and manually operated UAV tasks, with cognitive state assessments performed through spatial cognition games combined with EEG signals. The experimental findings demonstrate that the proposed model significantly outperforms traditional methods, offering superior classification accuracy, robustness, and feature extraction capabilities. The MSSECNN model's advantages in spatial cognitive state classification provide valuable technical support for early identification and intervention in cognitive decline.