工程技术最新文献

Decellularized blood vessels with low immunogenicity and excellent biocompatibility are promising for tissue engineering and clinical applications. However, current decellularization methods face limitations in cell removal efficiency, matrix preservation, and biosafety. This study optimized the Triton X-100/SDS (TX-100/SDS) decellularization method using ultrasound technology by systematically evaluating the effects of ultrasound power, temperature, and processing time on decellularization efficiency. The optimized method achieved a 72% reduction in nucleic acid residues at 100 W power while preserving matrix integrity and significantly reducing chemical reagent residues. Structural and biosafety evaluations confirmed that the optimized scaffolds met biological safety standards and demonstrated excellent stability, providing a strong foundation for developing high-performance decellularized vascular materials for clinical applications.

Endovascular aneurysm repair (EVAR) is a widely accepted treatment for aortic pathologies owing to its minimally invasive nature. However, long-term complications, such as stent graft migration and infection, remain unresolved, primarily due to the persistent presence of synthetic materials and limited tissue integration. This pilot study evaluated the feasibility of a novel tissue-engineered stent graft (TESG) combining a bioresorbable poly-L-lactic acid (PLLA) stent with decellularized porcine veins. The veins were processed using a sodium dodecyl sulfate and the Triton X-100 decellularization protocol. Histological and ultrastructural analyses confirmed effective cell removal while preserving extracellular matrix components. Quantitative deoxyribonucleic acid (DNA) analysis showed a > 97% reduction in DNA content. The TESGs were assembled by suturing the decellularized veins into bioresorbable PLLA stents and implanted into porcine iliac arteries (n = 3). Commercially available prosthetic grafts were used as control implants to evaluate differences in tissue responses. Graft patency and morphology were assessed at implantation and on postoperative day 14 using angiography and intravascular ultrasonography. All TESGs remained patent, with no evidence of thrombosis or aneurysmal changes. Histological analysis revealed early endothelialization and smooth muscle cell infiltration within the TESG wall, in contrast to the prosthetic graft controls, which lacked comparable cellular integration. This study demonstrated the short-term feasibility and biological compatibility of a fully bioresorbable TESG. Although long-term outcomes remain to be established, these results support further development of TESG to reduce late complications through improved tissue integration and avoidance of permanent synthetic materials.

Cell membrane-derived vesicles play essential roles in intercellular communication, material transport, and waste disposal. Despite their biomedical and industrial potential, isolating extracellular vesicles from natural sources remains technically challenging, limiting purification efficiency and scalability. This study introduces cell membrane extrusion as an alternative approach to optimize the production of cell membrane-derived vesicles (CSMs), from eukaryotic and prokaryotic cells. CSMs, generated from HeLa and SH-SY5Y cells exhibited a distinctive cup-shaped morphology and sizes of 151.36 ± 72.36 nm, and 416.86 ± 108.49 nm at 20 °C by DLS respectively, showing remarkable thermal stability at 4-70 °C range. Furthermore, loaded vesicles interacted with mammalian cells and achieved successful cargo internalization. CSMs were also produced from E. coli membranes, forming unilamellar vesicles of approximately 100 nm, as observed by Cryo-TEM. These vesicles displayed an inverse correlation between vesicle size and thermal stability and efficient cargo incorporation detected in 85% ± 3% of CSMs. However, under tested conditions, no interaction with prokaryotic cells occurred, and consequently, no delivery of the loaded molecule was observed. Overall, thesefindings highlight the potential of generating cell membrane-derived nanovesicles through extrusion, offering a promising strategy to mimic extracellular vesicles for innovative biomedical and industrial applications, including targeted drug delivery system.

Traffic accidents usually result from driver's inattention, sleepiness, and distraction, posing a substantial danger to worldwide road safety. Advances in computer vision and artificial intelligence (AI) have provided new prospects for designing real-time driver monitoring systems to reduce these dangers. In this paper, we assessed four known deep learning models, MobileNetV2, DenseNet201, NASNetMobile, and VGG19, and offer a unique Hybrid CNN-Transformer architecture reinforced with Efficient Channel Attention (ECA) for multi-class driver activity categorization. The framework defines seven important driving behaviors: Closed Eye, Open Eye, Dangerous Driving, Distracted Driving, Drinking, Yawning, and Safe Driving. Among the baseline models, DenseNet201 (99.40%) and MobileNetV2 (99.31%) achieved the highest validation accuracies. In contrast, the proposed Hybrid CNN-Transformer with ECA attained a near-perfect validation accuracy of 99.72% and further demonstrated flawless generalization with 100% accuracy on the independent test set. Confusion matrix studies further indicate a few misclassifications, verifying the model's high generalization capacity. By merging CNN-based local feature extraction, attention-driven feature refinement, and Transformer-based global context modeling, the system provides both robustness and efficiency. These findings show the practicality of using the suggested technology in real-time intelligent transportation applications, presenting a viable avenue toward reducing traffic accidents and boosting overall road safety.

Little is known about whether direct and vicarious rewards affect bilingual language control in social learning. We used a dual-electroencephalogram (EEG) to simultaneously record the effects of direct and vicarious rewards on language control when bilinguals switched between their two languages. We found that both direct and vicarious rewards elicited more switch behavior. On an electrophysiological level, although both direct and vicarious rewards elicited Reward-positivity and Feedback-P3 when receiving reward outcomes, direct rewards induced greater reward effects than vicarious rewards. In addition to an N2 effect in language switching, vicarious rewards elicited more pronounced LPCs relative to direct rewards. More important, in the alpha band, there was a predictive effect of behaviors on rewards in binding vicarious rewards and language switching activities. These findings demonstrate that both direct and vicarious rewards influence language control during language selection.

Spiking neural networks (SNNs) have gained significant attention for their biological plausibility, event-driven operation, and low power consumption, establishing them as a leading model for processing event stream data. However, current models often oversimplify neuronal dynamics to balance computational cost and performance. To address this limitation and enhance the dynamical behavior of spiking neurons, this paper introduces two key innovations. First, inspired by biological autaptic connections and memristive devices, we propose the memristive autapse (M-Autapse), a self-connection mechanism that enables adaptive modulation of a neuron's membrane potential. Second, recognizing the need for attention mechanisms that match SNNs' spatio-temporal nature, we design a spatio-temporal synergistic attention (STSA) mechanism to bolster simultaneous focus on both temporal and spatial dimensions of input data. Extensive experiments on the neuromorphic speech benchmarks SHD and SSC validate our methods. On SHD, our model demonstrates performance competitive with the state-of-the-art, while also achieving strong results on the SSC dataset.

Accurate assessment of mental workload (MWL) from electroencephalography (EEG) signals is crucial for real-time cognitive monitoring in safety-critical domains such as aviation and human-computer interaction. Although various computational approaches have been proposed, those mostly suffer from limited robustness, interpretability, or fail to fully exploit both temporal and non-linear neural dynamics. This article introduces a novel hybrid deep learning and XGBoost stacking ensemble framework for reliable and interpretable MWL classification from EEG. The proposed pipeline systematically includes preprocessing of raw EEGs, followed by comprehensive feature extraction (time-domain, frequency-domain, wavelet-based, entropy, and fractal dimension features), and subsequent discriminative feature selection phase using ANOVA F-values, yielding a compact set of 200 highly informative features. The proposed architecture consists of dual processing branches: a CNN-BiLSTM-Attention based deep learning branch for automatic learning of spatiotemporal dynamics, and an XGBoost branch for robust classification from engineered features. Predictions from both branches are integrated using a logistic regression stacking ensemble, maximizing complementary strengths and improving generalization. Experiments are conducted on the STEW (simultaneous workload) and EEGMAT (mental arithmetic task) dataset. Proposed model yields 96.87% and 99.40% of classification accuracy by outperforming 16 and 7 previously published state-of-the-art techniques on STEW and EEGMAT dataset respectively. Attention heatmaps and SHAP value analysis provide intuitive visual explanations and interpretability of the model's decision making, while systematic ablation studies validate the contribution of each architectural module. This work demonstrates that a carefully engineered stacking ensemble, informed by both deep learning and classical machine learning, capable of delivering not only improved performance but also enhanced interpretability for EEG-based MWL assessment in real-world applications.

Vanadium dioxide is a well-known candidate for memristor applications due to its insulator-to-metal transition characteristics, this is because vanadium dioxide memristors are versatile devices whose operating mechanism is based on an abrupt and volatile change of resistivity. This manuscript introduces the fractal-fractional framework for a third-order vanadium dioxide memristor neuron model that investigates the role of non-local dynamics on chaotic behavior. The third-order vanadium dioxide memristor neuron model is analyzed under three conditions of fractal-fractional differential operators (i) deviation of fractional parameter with fixed fractal order, (ii) deviation of fractal parameter with fixed fractional order, and (iii) simultaneous deviation of both parameters. The mathematical model of third-order vanadium dioxide memristor neuron has been discretized by means of Adams-Bashforth-Moulton method for the sake of numerical simulations. The results highlight the fractal-fractional framework as a versatile tool for tailoring vanadium dioxide memristor neuron's dynamics namely irregular oscillations, dispersed attractors with enhanced chaoticity, bounded loops with tunable stability and excessive fluctuations. These findings confirm that fractional order acts as a memory controller, while fractal order governs structural scaling, together enabling precise modulation between chaos and stability.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and the existing clinical diagnosis mainly relies on subjective behavioral assessment and lacks objective biomarkers. This paper proposes a hierarchical deep learning architecture, VaeTF, incorporating community-aware mechanisms based on resting-state functional magnetic resonance imaging (rs-fMRI) data. VaeTF introduces a priori knowledge of the functional community, extracts localized features through a variational auto-encoder (VAE), captures global dependencies across brain regions using the Transformer module, and incorporates an improved pooling mechanism to enhance the expressive power and model generalization performance. Experimental results on the ABIDE database show that VaeTF achieves 71.4% accuracy in ASD and typically performs well in group classification tasks. Further feature weighting analysis reveals that VaeTF is capable of identifying local functional abnormalities and cross-network functional synergistic dysfunctions closely related to ASD, thereby uncovering the underlying neurobiological mechanisms. VaeTF not only improves the classification performance of ASD but also provides a new method and theoretical support for objective assessment and early diagnosis based on fMRI.

Building upon our prior introduction of the Delay concept within a neuron-astrocyte electromagnetic coupling system, this study provides a deeper investigation into this phenomenon. The focus is on a specific time interval, termed Delay, which occurs after the cessation of external stimuli. During this period, neurons continue their firing activity before transitioning to a resting state. We initially elucidate that the prolonged neuronal firing, termed Delay, originates from astrocytic involvement rather than magnetic effects. Moreover, the periodic calcium activity of astrocytes can periodically induce the occurrence of neuronal Delay. Finally, we provide a thorough analysis of the duration and structural composition of the neuron Delay induced by astrocytes. The significance of our findings lies in the potential functional role of the Delay phase in the modulation and processing of neural information. Our findings offer a novel perspective on the complex dynamics governing the transition from active firing to resting in neurons, thereby enhancing the understanding of neural response and adaptability.