Radix Arnebiae oil (RAO) is a clinically useful traditional Chinese medical formula with outstanding curative effects on burns. However, the mechanism of the effect of RAO on wound healing remains unclear. The present study investigated the molecular mechanisms of the potential curative effect of RAO on wound healing. The concentrations of the main constituents shikonin, imperatorin and ferulic acid in RAO detected by HPLC were 24.57, 3.15 and 0.13 mg/mL, respectively. A rat burn model was established, and macroscopic and histopathological studies were performed. RAO significantly accelerated wound closure and repair scarring, increased superoxide dismutase activities, and reduced malondialdehyde. RAO also downregulated interleukin (IL)-6, IL-1β and tumor necrosis factor-α in wound tissues, and increased secretion of vascular endothelial growth factor, epidermal growth factor and transforming growth factor (TGF)-β1. RAO increased the gene expression of TGF-β1, type Ⅰ and Ⅲ collagen, and increased the protein expression of TGF-β1, phosphorylation of PI3K and Akt. In conclusion, RAO likely promoted wound healing via antioxidant and anti-inflammatory activities, and increased re-epithelization. Activation of the TGF-β1/PI3K/Akt pathway may play an important role in the healing efficacy of RAO. These findings suggested that RAO could be a promising alternative local treatment for burn wound healing.
The long-term statistical rule is one of the important questions for stochastic pollution-population dynamical models, thus it would be worth looking for the stationary distribution as an indicator in analysing the effects of toxicant and noises on the variation of population in evolution process. In present paper, we investigate a stochastic single-species model under regime switching in a polluted environment. By use of the ergodic of Markov chain and constructing Lyapunov function, the sufficient conditions for the positive recurrence and ergodic property are established, which imply the existence of stationary distribution of the model. Moreover, the mean and variance of marginal stationary distribution are estimated. Our analysis indicates that the coloured noise and toxicant may play an important role in determining the shape of stationary distribution and its statistics characteristics. Finally, numerical simulations are carried out to support our theoretical results.
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