Background: Alcoholic liver disease (ALD) is a common chronic liver disorder worldwide, which is detrimental to human health. A preliminary study showed that the total flavonoids within Citrus grandis “Tomentosa” exerted a remarkable effect on the treatment of experimental ALD. However, the active substances of Citrus grandis “Tomentosa” were not elucidated. Rhoifolin (ROF) is a flavonoid component present in high levels. Therefore, this research aimed to evaluate the hepatoprotective effects of ROF and its possible mechanisms.Methods: Molecular docking was performed to analyze the binding energy of ROF to the main target proteins related to ALD. Subsequently, mice were fed ethanol (ETH) for 49 days to establish the chronic alcoholic liver injury models. The liver pathological injury, serum aminotransferase levels, and oxidative stress levels in the liver tissue were measured. Human normal hepatocytes (LO2 cells) were incubated with ETH to construct the alcoholic liver cell model. The inflammatory markers and apoptosis factors were evaluated using real-time PCR and flow cytometry. Finally, the effects of ROF on the CYP2E1 and NF-κB signaling pathways were tested in vitro and in vivo.Results: Molecular docking results demonstrated that ROF was able to successfully dock with the target proteins associated with ALD. In animal studies, ROF attenuated ETH-induced liver damage in mice by decreasing the serum concentrations of AST and ALT, reducing the expression of inflammatory cytokines, and maintaining antioxidant balance in the liver tissue. The in vitro experiments demonstrated that ROF suppressed ETH-induced apoptosis in LO2 cells by promoting Bcl-2 mRNA and inhibiting Bax mRNA and caspase 3 protein expression. ROF decreased the level of LDH, ALT, AST, ROS, and MDA in the supernatant; induced the activity of GSH and SOD; and inhibited TNF-α, IL-6, and IL-1β expression levels. Mechanistically, ROF could significantly downregulate the expression levels of CYP2E1, TLR4, and NF-κB phosphorylation.Conclusion: This study indicates that ROF is the active component within the total flavonoids, which may alleviate ETH-induced liver injury by inhibiting NF-κB phosphorylation. Therefore, ROF may serve as a promising compound for treating ALD.
IntroductionFacial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's performance depends largely on its hyperparameters, which is a challenge for optimization.MethodsIn this paper, a Northern Goshawk optimization (NGO) algorithm is proposed to optimize the hyperparameters of BILSTM network for facial expression recognition. The proposed methods were evaluated and compared with other methods on the FER2013, FERplus and RAF-DB datasets, taking into account factors such as cultural background, race and gender.ResultsThe results show that the recognition accuracy of the model on FER2013 and FERPlus data sets is much higher than that of the traditional VGG16 network. The recognition accuracy is 89.72% on the RAF-DB dataset, which is 5.45, 9.63, 7.36, and 3.18% higher than that of the proposed facial expression recognition algorithms DLP-CNN, gACNN, pACNN, and LDL-ALSG in recent 2 years, respectively.DiscussionIn conclusion, NGO algorithm effectively optimized the hyperparameters of BILSTM network, improved the performance of facial expression recognition, and provided a new method for the hyperparameter optimization of BILSTM network for facial expression recognition.
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