Yoda1 pretreated BMSC derived exosomes accelerate osteogenesis by activating phospho-ErK signaling via Yoda1-mediated signal transmission.

Segmental bone defects, arising from factors such as trauma, tumor resection, and congenital malformations, present significant clinical challenges that often necessitate complex reconstruction strategies. Hydrogels loaded with multiple osteogenesis-promoting components have emerged as promising tools for bone defect repair. While the osteogenic potential of the Piezo1 agonist Yoda1 has been demonstrated previously, its hydrophobic nature poses challenges for effective loading onto hydrogel matrices.In this study, we address this challenge by employing Yoda1-pretreated bone marrow-derived mesenchymal stem cell (BMSCs) exosomes (Exo-Yoda1) alongside exosomes derived from BMSCs (Exo-MSC). Comparatively, Exo-Yoda1-treated BMSCs exhibited enhanced osteogenic capabilities compared to both control groups and Exo-MSC-treated counterparts. Notably, Exo-Yoda1-treated cells demonstrated similar functionality to Yoda1 itself. Transcriptome analysis revealed activation of osteogenesis-associated signaling pathways, indicating the potential transduction of Yoda1-mediated signals such as ErK, a finding validated in this study. Furthermore, we successfully integrated Exo-Yoda1 into gelatin methacryloyl (GelMA)/methacrylated sodium alginate (SAMA)/ß-tricalcium phosphate (ß-TCP) hydrogels. These Exo-Yoda1-loaded hydrogels demonstrated augmented osteogenesis in subcutaneous ectopic osteogenesis nude mice models and in rat skull bone defect model. In conclusion, our study introduces Exo-Yoda1-loaded GELMA/SAMA/ß-TCP hydrogels as a promising approach to promoting osteogenesis. This innovative strategy holds significant promise for future widespread clinical applications in the realm of bone defect reconstruction.

A New Assistance Navigation Method for Substation Inspection Robots to Safely Cross Grass Areas.

With the development of intelligent substations, inspection robots are widely used to ensure the safe and stable operation of substations. Due to the prevalence of grass around the substation in the external environment, the inspection robot will be affected by grass when performing the inspection task, which can easily lead to the interruption of the inspection task. At present, inspection robots based on LiDAR sensors regard grass as hard obstacles such as stones, resulting in interruption of inspection tasks and decreased inspection efficiency. Moreover, there are inaccurate multiple object-detection boxes in grass recognition. To address these issues, this paper proposes a new assistance navigation method for substation inspection robots to cross grass areas safely. First, an assistant navigation algorithm is designed to enable the substation inspection robot to recognize grass and to cross the grass obstacles on the route of movement to continue the inspection work. Second, a three-layer convolutional structure of the Faster-RCNN network in the assistant navigation algorithm is improved instead of the original full connection structure for optimizing the object-detection boxes. Finally, compared with several Faster-RCNN networks with different convolutional kernel dimensions, the experimental results show that at the convolutional kernel dimension of 1024, the proposed method in this paper improves the mAP by 4.13% and the mAP is 91.25% at IoU threshold 0.5 in the range of IoU thresholds from 0.5 to 0.9 with respect to the basic network. In addition, the assistant navigation algorithm designed in this paper fuses the ultrasonic radar signals with the object recognition results and then performs the safety judgment to make the inspection robot safely cross the grass area, which improves the inspection efficiency.