Search

[Display omitted] • Reduction of Sp1 restrains microglial M1 polarization and inflammatory response. • Sp1 affects the M1 polarization of microglia through the HuR/NF-κB axis. • Sp1 and HuR as potential therapeutic targets in treating spinal cord injuries. The M1 polarization of microglia, followed by the production of pro-inflammatory mediators, hinders functional recovery after spinal cord injury (SCI). Our previous study has illuminated that specificity protein 1 (Sp1) expression is increased following SCI, whereas the function and regulatory mechanism of Sp1 during M1 polarization of microglia following SCI remain unknown. RNA binding protein, HuR, has been shown to be up-regulated in the injured spinal cord through analysis of the GEO database. Further investigation using Chip-Atlas data suggests a binding between Sp1 and HuR. Emerging evidence indicates that HuR plays a pivotal role in neuroinflammation after SCI. In this research, Sp1 and HuR levels in mice with SCI and BV2 cells treated with lipopolysaccharide (LPS) was determined by using quantitative real-time polymerase chain reaction and Western blotting techniques. A series of in vitro assays were performed to investigate the function of Sp1 during M1 polarization of microglia. The association between Sp1 and its target gene HuR was confirmed through gene transfection and luciferase reporter assay. Enhanced expression of HuR was observed in both SCI mice and LPS-treated BV2 cells, while Sp1 knockdown restrained M1 polarization of microglia and its associated inflammation by inhibiting the NF-κB signaling pathway. Silencing Sp1 also suppressed microglia activation and its mediated inflammatory response, which could be reversed by overexpression of HuR. In conclusion, silencing Sp1 restrains M1 polarization of microglia through the HuR/NF-κB axis, leading to neuroprotection, and thus promotes functional restoration following SCI. [ABSTRACT FROM AUTHOR]

• Surface electromyography is predictive. This paper proposes an sEMG-based gait recognition method LDA-PSO-LSTM. • Moderate combinations of time domain, frequency domain, and time–frequency domain features are beneficial for identifying the seven gait phases. • In the case of medium speed, the accuracy of gait recognition using the proposed method is the highest. • Different dimensionality reduction methods and classifiers are used for comparison to verify the proposed algorithm effect. Gait phases are widely used in exoskeleton movement control. Surface electromyography (sEMG) is predictive and plays an important role in gait phase recognition. The purpose of this study is to improve the stability and accuracy of gait recognition methods based on the sEMG signals of lower limbs. First, we presented a LDA-PSO-LSTM algorithm based on feature combination selection and verified its recognition accuracy through experiments. LDA-PSO-LSTM had an average recognition rate of 94.89% and a maximum accuracy of 97.02%. Second, we tested and compared the recognition accuracy of LDA-LSTM (92.17%). Experiments showed that the PSO optimization model had good recognition performance. Finally, we compared LDA-LSTM with all classifier combinations and concluded that the LDA-LSTM method has the highest recognition rate among a series of method combinations. The results indicated that LDA-PSO-LSTM as a classification model has apparent advantages in gait recognition. LDA-PSO-LSTM provides more accurate gait phase results for lower limb exoskeleton control. This method is beneficial to the development of the exoskeleton gait recognition system. [ABSTRACT FROM AUTHOR]