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It has been confirmed that neuroendocrine regulation involves in the pathogenesis of psoriasis, but the association between neuroendocrine and skin immune is still unknown. The present study aimed to explore the relationship of Merkel cells in the skin lesions and the pathogenesis of psoriasis. The ultrastructure of Merkel cells was observed under an electron microscope and immunohistochemistry was performed to detect the expressions of CK20, CK19, CK18, S100A7, SP and CD13 in the skin lesions of psoriasis patients and normal controls. Our results showed the neuroendocrine granules in the Merkel cells increased significantly at the progressive stage of psoriasis; the expressions of CK20, CK19 and CK18 in the skin lesions were markedly higher than those in normal controls, the CK20 expression at the progressive stage was significantly higher than that at the initial stage and recovery stage; the expressions of S100A7, SP and CD13 in the skin lesions at the progressive stage were higher than those in the initial stage and recovery stage and positively related to the CK20 expression. Merkel cells may play an important neuroendocrine regulatory role in the pathogenesis of psoriasis. Key words: Merkel cell, psoriasis neuroendocrine, pathogenesis.

The aim of the present study was to identify key genes associated with atopic dermatitis (AD) using microarray data and bioinformatic analyses. The dataset GSE6012, downloaded from the Gene Expression Omnibus (GEO) database, contains gene expression data from 10 AD skin samples and 10 healthy skin samples. Following data preprocessing, differentially expressed genes (DEGs) were identified using the limma package of the R project. Interaction networks were constructed comprising DEGs that showed a degree of node of >3, >5 and >10, using the Osprey software. Functional enrichment and pathway enrichment analysis of the network comprising all DEGs and of the network comprising DEGs with a high degree of node, were performed with the DAVID and WebGestalt toolkits, respectively. A total of 337 DEGs were identified. The functional enrichment analysis revealed that the list of DEGs was significantly enriched for proteins related to epidermis development (P=2.95E-07), including loricrin (LOR), keratin 17 (KRT17), small proline-rich repeat proteins (SPRRs) and involucrin (IVL). The chemokine signaling pathway was the most significantly enriched pathway (P=0.0490978) in the network of all DEGs and in the network consisting of high degree‑node DEGs (>10), which comprised the genes coding for chemokine receptor 7 (CCR7), chemokine ligand (CCL19), signal transducer and activator of transcription 1 (STAT1), and phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1). In conclusion, the list of AD-associated proteins identified in this study, including LOR, KRT17, SPRRs, IVL, CCR7, CCL19, PIK3R1 and STAT1 may prove useful for the development of methods to treat AD. From these proteins, PIK3R1 and KRT17 are novel and promising targets for AD therapy.

In order to overcome the problems of slow rate of convergence, falling easily into local minimum, instability learning performance caused by initial value in BP algorithm, a new diagnosis method based on RBF neural networks was proposed. And the diagnosis method is applied to rotary machinery fault diagnosis. The result shows that the RBF network has very high learning convergence speed and better classifying performance. RBF network has good practicality in the field of equipment fault diagnosis.

Trajectory planning of robot is to control the Robot in order to accurately follow the target track. And the target trajectory is always high-order and nonlinear. But RBF neural network can be achieved from the input to the output of arbitrary nonlinear mapping, through network learning and training to achieve the nonlinear function. This paper establishes a RBF neural network model firstly, and carries on the simulation through software MATLAB. The result confirms that the RBF neural network can keep the control of the Robot’s nonlinear trajectory planning in real time.