SNHG12/NFYC-AS1 Acted as the Sponge for hsa-miR-199a-5p to Promote the Expression of S100A8/S100A7/XDH and was Involved in the Progression of Diabetic Foot Ulcers.

Traditional Chinese medicine has been used to treat diabetic foot ulcer (DFU) for a long time. However, the underlying mechanism of Radix arnebiae seu lithospermi ointment (RAS-ointment) has not been revealed. Effects of RAS-ointment treatment were observed in DFU patients. The endogenous competitive RNA mechanism was constructed based on micro-array sequencing and bioinformatics analysis. RT-PCR was used to detected the expression of genes in DFU ulcerated skins and non-ulcerated skins. Dual luciferase and RT-PCR experiments were used to investigate the endogenous competitive RNA mechanism. Based on micro-array sequencing and bioinformatics analysis, we found that SNHG12/NFYC-AS1, hsa-miR-199a-5p and S100A8/S100A7/XDH might form an endogenous competitive RNA mechanism. RT-PCR assay shown that SNHG12, NFYC-AS1, S100A8, S100A7 and XDH were significantly up-regulated, while hsa-miR-199a-5p was significantly down-regulated in DFU ulcerated skins (N = 10) compared with non-ulcerated skins (N = 10). Dual luciferase and RT-PCR experiments showed that SNHG12 or NFYC-AS1 up-regulated the expression of S100A8, S100A7 and XDH by inhibiting hsa-miR-199a-5p in a direct binding way. After 35 days of RAS-ointment treatment, the wound healing of DFU patients was substantially improved and the expression of S100A7 and XDH were reduced expression in DFU patients. In addition, the monomer composition of RAS-ointment, 49070_FLUKA or auraptenol inhibited the expression of S100A7 and XDH in Te317.sk cells. In conclusion, RAS-ointment may be used as an adjunctive therapy for DFU patients.

Neural networks prediction for seismic response of structure under the Levenberg-Marquardt algorithm / 西安交通大学学报·英文版

Objective: To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg-Marquardt (LM) algorithm. Results: Based on identification and prediction ability of neural networks for nonlinear systems, and combined with LM algorithm, a multi-layer forward networks is adopted to predict the seismic responses of structure. The networks is trained in batch by the shaking table test data of three-floor reinforced concrete structure firstly, then the seismic responses of structure are predicted under the unused excitation data, and the predict responses are compared with the experiment responses. The error curves between the prediction and the experimental results show the efficiency of the method. Conclusion: LM algorithm has very good convergence rate, and the neural networks can predict the seismic response of the structure well.