Search

Extracellular vesicles (EVs) are membrane-bound vesicles that are shed or secreted from the cell membrane and enveloped by a lipid bilayer. They possess stability, low immunogenicity, and non-cytotoxicity, exhibiting extensive prospects in regenerative medicine (RM). However, natural EVs pose challenges, such as insufficient targeting capabilities, potential biosafety concerns, and limited acquisition pathways. Although engineered EVs demonstrate excellent therapeutic efficacy, challenges such as low production yield and the complexity of engineering modifications constrain their further clinical applications. Bacteria have advantages such as rapid proliferation, diverse gene editing methods, mature cultivation techniques, and relatively easy preparation of bacterial EVs (BEVs), which can be used to effectively address the challenges currently encountered in the field of EVs. This review provides a description of the biogenesis and pathophysiological functions of BEVs, and strategies for optimizing BEVs preparation to attain efficiency and safety are discussed. An analysis of natural characteristics of BEVs is also conducted to explore how to leverage their advantages or mitigate their limitations, thereby overcoming constraints on the application of BEVs in RM. In summary, engineered BEVs possess characteristics such as high production yield, excellent stability, and high drug-delivering capabilities, laying the foundation for their application in RM. Highlights: Elucidated the advantages and limitations of naturally occurring bacterial extracellular vesicles for regenerative medicine. Provided an overview of the preparation, characterization, and engineering strategies of bacterial extracellular vesicles, and explored from these perspectives how to alleviate the application limitations of bacterial extracellular vesicles in regenerative medicine. Discussed the current challenges and potential applications of bacterial extracellular vesicles. Summarized the immense potential of engineered bacterial extracellular vesicles as innovative therapies in regenerative medicine, particularly from the perspectives of drug delivery, tissue regeneration, and antibacterial effects. [ABSTRACT FROM AUTHOR]

To accurately diagnose misfire faults in automotive engines, we propose a Channel Attention Convolutional Model, specifically the Squeeze-and-Excitation Networks (SENET), for classifying engine vibration signals and precisely pinpointing misfire faults. In the experiment, we established a total of 11 distinct states, encompassing the engine's normal state, single-cylinder misfire faults, and dual-cylinder misfire faults for different cylinders. Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840 Hz. The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets. The results revealed that, with a vibration acceleration sequence of 1000 time steps (approximately 50 ms) as input, the SENET model achieved a misfire fault detection accuracy of 99.8%. For comparison, we also trained and tested several commonly used models, including Long Short-Term Memory (LSTM), Transformer, and Multi-Scale Residual Networks (MSRESNET), yielding accuracy rates of 84%, 79%, and 95%, respectively. This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models. Furthermore, the F1 scores for each type of recognition in the SENET model surpassed 0.98, outperforming the baseline models. Our analysis indicated that the misclassified samples in the LSTM and Transformer models' predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios. To delve deeper, we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding (T-SNE) technology. The findings revealed that, in the LSTM model, data points of the same type tended to cluster together with significant overlap. Conversely, in the SENET model, data points of various types were more widely and evenly dispersed, demonstrating its effectiveness in distinguishing between different fault types. [ABSTRACT FROM AUTHOR]