In vitro and in vivo antiseptic activities of caffeoylquinic acid

Purpose: To evaluate the antiseptic effect of caffeoylquinic acid (CA) in in vivo and in vitro models.Methods: In vivo sepsis was produced in rats via cecal ligation and puncture (CLP) method. Four groups of rats were used: control group, untreated CLP group, and two CA groups treated with caffeoylquinic acid (50 and 100 mg/kg, p.o.) for 30 days before the induction of sepsis. Following the induction of sepsis, histological assessment of lung tissue was carried out using hematoxylin and eosin, and isolectin B4 staining. In addition, in vitro tests were performed on RAW264.7 cells in which inflammation and oxidative stress were induced by lipopolysaccharide (LPS).Results: Treatment with CA significantly (p < 0.05) enhanced the survival of lung cells, relative to the CLP group. Lung histopathology revealed that pretreatment with CA did not attenuate the increased infiltration of macrophages in the alveoli. Results from in vitro studies showed that CA attenuated LPS-induced nitric oxide (NO) levels, but had no significant effect on the level of LPS-induced pro-inflammatory cytokines in RAW264.7 cells (p < 0.05).Conclusion: These results reveal that CA attenuates NO and TNF-α levels in LPS-stimulated macrophages, thereby decreasing inflammation-associated sepsis. Thus, CA may have beneficial effects on lung injury as a result of its antioxidant and anti-inflammatory activities.Keywords: Caffeoylquinic acid, Sepsis, Oxidative stress, Cytokines, Cecalligation, Punctur

Multi-wavelet residual dense convolutional neural network for image denoising

Networks with large receptive field (RF) have shown advanced fitting ability in recent years. In this work, we utilize the short-term residual learning method to improve the performance and robustness of networks for image denoising tasks. Here, we choose a multi-wavelet convolutional neural network (MWCNN), one of the state-of-art networks with large RF, as the backbone, and insert residual dense blocks (RDBs) in its each layer. We call this scheme multi-wavelet residual dense convolutional neural network (MWRDCNN). Compared with other RDB-based networks, it can extract more features of the object from adjacent layers, preserve the large RF, and boost the computing efficiency. Meanwhile, this approach also provides a possibility of absorbing advantages of multiple architectures in a single network without conflicts. The performance of the proposed method has been demonstrated in extensive experiments with a comparison with existing techniques.Comment: 9 pages, 9 figure