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As a chronic inflammatory disease, diabetes mellitus creates a proinflammatory microenvironment around implants, resulting in a high rate of implant loosening or failure in osteological therapies. In this study, macroporous silk gel scaffolds are injected at the bone–implant interface for in situ release of sitagliptin that can regulate macrophage response to create a prohealing microenvironment in diabetes mellitus disease. Notably, it is discovered that sitagliptin induces macrophage polarization to the M2 phenotype and alleviates the impaired behaviors of osteoblasts on titanium (Ti) implants under diabetic conditions in a dose‐dependent manner. The silk gel scaffolds loaded with sitagliptin elicite a stronger recruitment of M2 macrophages to the sites of Ti implants and a significant promotion of osteointegration, as compared to oral sitagliptin administration. The results suggest that injectable silk/sitagliptin gel scaffolds can be utilized to modulate the immune responses at the bone–implant interface, thus enhancing bone regeneration required for successful implantation of orthopedic and dental devices in diabetic patients. [ABSTRACT FROM AUTHOR]

In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches. [ABSTRACT FROM AUTHOR]