Resveratrol (REV) is a naturally occurring phytoalexin that inhibits neuronal K⁺ channels; however, the molecular mechanisms behind the effects of REV and the relevant α-subunit are not well defined. With the use of patch-clamp technique, cultured cerebellar granule cells, and HEK-293 cells transfected with the K(v)2.1 and K(v)2.2 α-subunits, we investigated the effect of REV on K(v)2.1 and K(v)2.2 α-subunits. Our data demonstrated that REV significantly suppressed Kv2.2 but not Kv2.1 currents with a fast, reversible, and mildly concentration-dependent manner and shifted the activation or inactivation curve of Kv2.2 channels. Activating or inhibiting the cAMP/PKA pathway did not abolish the inhibition of K(v)2.2 current by REV. In contrast, activation of PKC with phorbol 12-myristate 13-acetate mimicked the inhibitory effect of REV on K(v)2.2 by modifying the activation or inactivation properties of Kv2.2 channels and eliminated any further inhibition by REV. PKC and PKC-α inhibitor completely eliminated the REV-induced inhibition of K(v)2.2. Moreover, the effect of REV on K(v)2.2 was reduced by preincubation with antagonists of GPR30 receptor and shRNA for GPR30 receptor. Western blotting results indicated that the levels of PKC-α and PKC-β were significantly increased in response to REV application. Our data reveal, for the first time, that REV inhibited K(v)2.2 currents through PKC-dependent pathways and a nongenomic action of the oestrogen receptor GPR30.
The study developed a promising implantation system for therapy of severe spinal cord injury and provided the first understanding of Screenfect® A about its functions on stem cell therapy for nerve tissue repair as well as three-dimentional gene expression.
The COVID-19 pandemic caused colleges and universities to rely heavily on online learning to continue knowledge dissemination to learners. This study used the second-generation model of unified theory of acceptance and use of technology (UTAUT2) to comprehensively analyze the mediating effects of self-efficacy, which affects learners’ effective use of online tools for learning, and capability of metacognition and self-regulation, which can independently adjust learning progress into the UTAUT2 model, on the learner’s willingness to continue online learning [i.e., their behavioral intention (BI)] by constructing a UTAUT2-based e-learning model. This study administered questionnaires to undergraduates in universities in East China to collect data. The effects of performance expectancy, effort expectancy (EE), social influence (SI), and facilitating conditions (FCs), hedonic motivation (HM), price value (PV), and habits on BI (directly or through mediators) were analyzed through data analysis and structural equation modeling, and the UTAUT2-based e-learning model was accordingly modified. The results indicated that the self-efficacy enhanced the effects of EE, SI, FCs, HM, and PV on learners’ BI; that metacognition and self-regulation (MS) capabilities enhanced the effects of EE on learners’ BI; and that habits had a direct and strong effect on BI. This study also provided some suggestions to enhance higher education learners’ willingness to continue online learning, such as improving social recognition and support, careful design of teaching content, easy-to-use technology, financial support. These results and suggestions may guide colleges and universities in conducting, continuing, or enhancing online education, particularly as the pandemic continues.
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