Synthesis and Biological Evaluation of Novel Piperidine-3-Carboxamide Derivatives as Anti-Osteoporosis Agents Targeting Cathepsin K.

A series of novel piperidamide-3-carboxamide derivatives were synthesized and evaluated for their inhibitory activities against cathepsin K. Among these derivatives, compound H-9 exhibited the most potent inhibition, with an IC50 value of 0.08 µM. Molecular docking studies revealed that H-9 formed several hydrogen bonds and hydrophobic interactions with key active-site residues of cathepsin K. In vitro, H-9 demonstrated anti-bone resorption effects that were comparable to those of MIV-711, a cathepsin K inhibitor currently in phase 2a clinical trials for the treatment of bone metabolic disease. Western blot analysis confirmed that H-9 effectively downregulated cathepsin K expression in RANKL-reduced RAW264.7 cells. Moreover, in vivo experiments showed that H-9 increased the bone mineral density of OVX-induced osteoporosis mice. These results suggest that H-9 is a potent anti-bone resorption agent targeting cathepsin K and warrants further investigation for its potential anti-osteoporosis values.

Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition.

As a key technology for the non-invasive human-machine interface that has received much attention in the industry and academia, surface EMG (sEMG) signals display great potential and advantages in the field of human-machine collaboration. Currently, gesture recognition based on sEMG signals suffers from inadequate feature extraction, difficulty in distinguishing similar gestures, and low accuracy of multi-gesture recognition. To solve these problems a new sEMG gesture recognition network called Multi-stream Convolutional Block Attention Module-Gate Recurrent Unit (MCBAM-GRU) is proposed, which is based on sEMG signals. The network is a multi-stream attention network formed by embedding a GRU module based on CBAM. Fusing sEMG and ACC signals further improves the accuracy of gesture action recognition. The experimental results show that the proposed method obtains excellent performance on dataset collected in this paper with the recognition accuracies of 94.1%, achieving advanced performance with accuracy of 89.7% on the Ninapro DB1 dataset. The system has high accuracy in classifying 52 kinds of different gestures, and the delay is less than 300 ms, showing excellent performance in terms of real-time human-computer interaction and flexibility of manipulator control.