Neurophysiological studies suggest that clozapine may facilitate γ-aminobutyric acid (GABAergic) neurotransmission. Therefore, we studied the interaction between clozapine and the GABAB receptor (GABABR). We showed that clozapine, and not N-desmethylclozapine, which is a metabolite of clozapine, increased the binding of the GABABR antagonist, [³H]-CGP54626A, at GABABRs. Linear regression analysis showed that the correlation between the dose of clozapine and the increase of [³H]-CGP54626A binding was significant. The curve of specific [³H]-CGP54626A binding in competition with different concentrations of GABA was left shifted in the presence of clozapine. With HEK293 cells overexpressing GABABR, we showed that clozapine had a significant increase of [³H]-CGP54626A binding at GABABR1 subunit, which provided a clue of the potential therapeutic target of clozapine.

A machine vision system based on a convolutional neural network (CNN) was proposed to sort Amomum villosum using X-ray non-destructive testing technology in this study. The Amomum villosum fruit network (AFNet) algorithm was developed to identify the internal structure for quality classification and origin identification in this manuscript. This network model is composed of experimental features of Amomum villosum. In this study, we adopted a binary classification method twice consecutive to identify the origin and quality of Amomum villosum. The results show that the accuracy, precision, and specificity of the AFNet for quality classification were 96.33%, 96.27%, and 100.0%, respectively, achieving higher accuracy than traditional CNN under the condition of faster operation speed. In addition, the model can also achieve an accuracy of 90.60% for the identification of places of origin. The accuracy of multi-category classification performed later with the consistent network structure is lower than that of the cascaded CNNs solution. With this intelligent feature recognition model, the internal structure information of Amomum villosum can be determined based on X-ray technology. Its application will play a positive role to improve industrial production efficiency.

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