Novel oxicam nonsteroidal compound XK01 attenuates inflammation by suppressing the NF-κB and MAPK pathway in RAW264.7 macrophages.

Traditional non-steroidal anti-inflammatory drugs (NSAIDs) show serious adverse effects during clinical use, which limits their usage. Oxicams (e.g., piroxicam, meloxicam) are widely used as NSAIDs. However, selectivity to cyclooxygenase (COX) 2 may cause cardiovascular problems considering the long-term use of the drugs. Therefore, it is important to develop new non-steroidal compounds as anti-inflammatory drugs. In the present study, we evaluated the anti-inflammatory activity of a newly developed nonsteroidal drug XK01. Our data showed that XK01 reduced the contents of nitric oxide (NO) and reactive oxygen species (ROS)and inhibited the transcription levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-6, and IL-1ß in a dose-dependent manner in lipopolysaccharide (LPS)-stimulated mouse RAW264.7 macrophages. XK01 showed no significant inhibitory effect on COX-1, but inhibited the expression of COX-2. At molecular level, XK01 prevented the translocation of p65 protein from the cytoplasm to the nucleus and inhibited the phosphorylation of p65, IκB, and MAPKs proteins. And high concentration of XK01 also inhibited the phosphorylation of JNK, p38 and ERK, showing stronger effect than that of meloxicam. In addition, the anti-inflammatory activity of XK01 was further validated in Xylene-induced mouse ear swelling model. Thus, this study verified that XK01 inhibits the expression of inflammatory mediators and COX-2, and exhibits potential anti-inflammatory effects via suppressing the NF-κB and MAPK pathway.

Study on the mechanism of efficient extracellular expression of toxic streptomyces phospholipase D in Brevibacillus choshinensis under Mg2+ stress.

BACKGROUND: Phospholipase D (PLD) has significant advantages in the food and medicine industries due to its unique transphosphatidylation. However, the high heterologous expression of PLD is limited by its cytotoxicity. The present study sought to develop an efficient and extracellular expression system of PLD in the non-pathogenic Brevibacillus choshinensis (B. choshinensis). RESULTS: The extracellular PLD was effectively expressed by the strong promoter (P2) under Mg2+ stress, with the highest activity of 10 U/mL. The inductively coupled plasma-mass spectrometry (ICP-MS) results elucidated that the over-expression of PLD by P2 promoter without Mg2+ stress induced the ionic homeostasis perturbation caused by the highly enhanced Ca2+ influx, leading to cell injury or death. Under Mg2+ stress, Ca2+ influx was significantly inhibited, and the strengths of P2 promoter and HWP gene expression were weakened. The study results revealed that the mechanism of Mg2+ induced cell growth protection and PLD expression might be related to the lowered strength of PLD expression by P2 promoter repression to meet with the secretion efficiency of B. choshinensis, and the redistribution of intracellular ions accompanied by decreased Ca2+ influx. CONCLUSIONS: The PLD production was highly improved under Mg2+ stress. By ICP-MS and qPCR analysis combined with other results, the mechanism of the efficient extracellular PLD expression under Mg2+ stress was demonstrated. The relatively low-speed PLD expression during cell growth alleviated cell growth inhibition and profoundly improved PLD production. These results provided a potential approach for the large-scale production of extracellular PLD and novel insights into PLD function.

Data-driven three-dimensional super-resolution imaging of a turbulent jet flame using a generative adversarial network.

Three-dimensional (3D) computed tomography (CT) is becoming a well-established tool for turbulent combustion diagnostics. However, the 3D CT technique suffers from contradictory demands of spatial resolution and domain size. This work therefore reports a data-driven 3D super-resolution approach to enhance the spatial resolution by two times along each spatial direction. The approach, named 3D super-resolution generative adversarial network (3D-SR-GAN), builds a generator and a discriminator network to learn the topographic information and infer high-resolution 3D turbulent flame structure with a given low-resolution counterpart. This work uses numerically simulated 3D turbulent jet flame structures as training data to update model parameters of the GAN network. Extensive performance evaluations are then conducted to show the superiority of the proposed 3D-SR-GAN network, compared with other direct interpolation methods. The results show that a convincing super-resolution (SR) operation with the overall error of ∼4% and the peak signal-to-noise ratio of 37 dB can be reached with an upscaling factor of 2, representing an eight times enhancement of the total voxel number. Moreover, the trained network can predict the SR structure of the jet flame with a different Reynolds number without retraining the network parameters.