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Background: Melastoma dodecandrum Lour. (MD) is a component used in traditional Chinese medicine that is widely distributed in southern China. MD has long been used clinically to treat various diseases, such as inflammation. However, the potential anti-inflammatory mechanism of MD remains to be elucidated.Objective: In this study, network pharmacology and experimental validation have been used to explore the underlying mechanism of MD in inflammation.Methods: The chemical composition of MD was determined using ultra-high performance liquid chromatography-electrospray ionization-high resolution mass spectrometry (UHPLC-ESI-HRMS). The effects of MD on pro-inflammatory cytokines, such as NO, i-NOS, IL-1β, and TNF-α, in RAW264.7 cells stimulated by lipopolysaccharide (LPS) were determined by ELISA and QRT-PCR. Through the analysis of multiple databases, targets for the treatment of inflammation with MD were identified. Other extensive analyses included PPI, GO, and KEGG pathway enrichment, which were completed through the use of the STRING database, Cytoscape software, and the DAVID database. Key targets and key components have been selected for molecular docking.Results: A total of 33 active components were identified in MD, and 134 common targets were obtained and used to construct the networks. Of these, 10 core components and 10 core targets of MD in the treatment of inflammation were identified. The GO and KEGG enrichment analyses revealed that the common targets were involved in multiple signaling pathways, including the PI3K-Akt signaling pathway, NOD-like receptor signaling pathway, chemokine signaling pathway, and IL-17 signaling pathway. The molecular docking methods confirmed the high affinity between bioactive molecules of MD and their targets in inflammation. Two core targets (PIK3CA and AKT) and three core components (asiatic acid, apigenin, and kaempferol) were found to be closely related to MD in the treatment of inflammation. In vitro, MD exerted a significant effect on LPS-stimulated NO, IL- 1β, and TNF-α secretion, and iNOS, IL-1β, and TNF-α expressions in macrophages.Conclusion: This study has demonstrated the bioactive constituents and mechanisms of MD in inhibiting the secretion of inflammatory factors and the multicomponent, multitarget, and multipathway treatment characteristics involved in inflammation, but this still needs further in vivo/in vitro experiments.

One-bit feedback systems generate binary data as their output and the system performance is usually measured by the success rate with a fixed parameter combination. Traditional methods need many executions for parameter optimization. Hence, it is impractical to utilize these methods in Expensive One-Bit Feedback Systems (EOBFSs), where a single system execution is costly in terms of time or money. In this paper, we propose a novel algorithm, named Iterative Regression and Optimization (IRO), for parameter optimization and its corresponding scheme based on the Maximum Likelihood Estimation (MLE) method and Particle Swarm Optimization (PSO) method, named MLEPSO-IRO, for parameter optimization in EOBFSs. The IRO algorithm is an iterative algorithm, with each iteration comprising two parts: regression and optimization. Considering the structure of IRO and the Bernoulli distribution property of the output of EOBFSs, MLE and a modified PSO are selected to implement the regression and optimization sections, respectively, in MLEPSO-IRO. We also provide a theoretical analysis for the convergence of MLEPSO-IRO and provide numerical experiments on hypothesized EOBFSs and one real EOBFS in comparison to traditional methods. The results indicate that MLEPSO-IRO can provide a much better result with only a small amount of system executions. [ABSTRACT FROM AUTHOR]