Cigarette smoke- (CS-) induced oxidative stress and inflammation in the lung are serious health problems. Primary and reprocessed tea products contain multiple antioxidants that have been reported to protect the lung against CS-induced injury. However, the beneficial effects of Eurotium cristatum fermented loose dark tea (ECT) and Eurotium cristatum particle metabolites (ECP) on CS-induced lung injury and its potential hepatic metabolic detoxification are still unclear. Therefore, sixty mice were randomly divided into six equal groups. CS-exposed mice were prevented or treated with ECP or ECT infusions for 12 or 8 weeks to determine the antioxidative stress, anti-inflammatory and potential metabolic detoxification of ECT and ECP. Thirty-six mice were randomly divided into six equal groups to observe the effects on hepatic metabolic detoxification by replacing daily drinking water with ECT. Results showed that CS significantly decreased the activities of glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) and upregulated the expressions of malondialdehyde (MDA), tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), IL-8, and IL-1β in serum. These adverse effects were modulated by ECP and ECT. In addition, ECT upregulated the mRNA expression of pregnane X receptor (PXR) and cytochrome P450 (CYP450) in the liver on daily free drinking ECT mice group. Western blot analysis further revealed that in CS-exposed mice, ECP and ECT significantly decreased the phosphorylation of mitogen-activated protein kinase (MAPK) in the lung but upregulated the protein expressions of PXR and aryl hydrocarbon receptor (AhR) in the liver. Overall, our findings demonstrated that ECT and ECP protected against lung injury induced by CS via MAPK pathway and enhanced hepatic metabolic detoxification via PXR and AhR pathways. Therefore, daily intake of ECT and ECP can potentially protect against CS-induced oxidative and inflammatory injuries.

Electronic-cigarette smoke (eCS) has been shown to cause a degree of oxidative stress and inflammatory damage in lung tissue. The aim of this study was to evaluate the repair mechanism of Eurotium cristatum fermented loose dark tea (ECT) and Eurotium cristatum particle metabolites (ECP) sifted from ECT after eCS-induced injury in mice. Sixty C57BL/6 mice were randomly divided into a blank control group, an eCS model group, an eCS + 600 mg/kg ECP treatment group, an eCS + 600 mg/kg ECT treatment group, an eCS + 600 mg/kg ECP prevention group, and an eCS + 600 mg/kg ECT prevention group. The results show that ECP and ECT significantly reduced the eCS-induced oxidative stress and inflammation and improved histopathological changes in the lungs in mice with eCS-induced liver injury. Western blot analysis further revealed that ECP and ECT significantly inhibited the eCS-induced upregulation of the phosphorylation levels of the extracellular Regulated protein Kinases (ERK), c-Jun N-terminal kinase (JNK) and p38mitogen-activated protein kinases (p38MAPK) proteins, and significantly increased the eCS-induced downregulation of the expression levels of the pregnane X receptor (PXR) and aryl hydrocarbon receptor (AhR) proteins. Conclusively, these findings show that ECP and ECT have a significant repairing effect on the damage caused by eCS exposure through the MAPK and PXR/AhR signaling pathways; ECT has a better effect on preventing eCS-induced injury and is suitable as a daily healthcare drink; ECP has a better therapeutic effect after eCS-induced injury, and might be a potential therapeutic candidate for the treatment of eCS-induced injury.

Agriculture is a basic and pillar industry. With the integration and development of Internet+, platform economy, and various industries, the business model of agriculture-related platforms is also constantly innovating. In this context, it is necessary to recommend suitable business models for different types of agriculture-related platforms. Based on the characteristics of agriculture-related platforms and various business models, this paper proposes a business model recommendation algorithm based on radial basis function neural network (RBFNN). This method trains the RBFNN model with the goal of maximizing the correlation between agricultural-related platforms and business models. In the application stage, for a specific agriculture-related platform, after inputting its characteristic parameters, a suitable business model can be recommended. In the experiment, the proposed method is tested and verified with relevant data, and the results show the effectiveness of the method.