Alcoholic liver disease (ALD) is linked to a broad spectrum of diseases, including diabetes, hypertension, atherosclerosis, and even liver carcinoma. The ALD spectrum includes alcoholic fatty liver disease (AFLD), alcoholic hepatitis, and cirrhosis. Most recently, some reports demonstrated that the pathogenesis of ALD is strongly associated with metabolites of human microbiota. AFLD was the onset of disease among ALDs, the initial cause of which is alcohol consumption. Thus, we analyzed the significant metabolites of microbiota against AFLD via the network pharmacology concept. The metabolites from microbiota were retrieved by the gutMGene database; sequentially, AFLD targets were identified by public databases (DisGeNET, OMIM). The final targets were utilized for protein–protein interaction (PPI) networks and signaling pathway analyses. Then, we performed a molecular docking test (MDT) to verify the affinity between metabolite(s) and target(s) utilizing the Autodock 1.5.6 tool. From a holistic viewpoint, we integrated the relationships of microbiota-signaling pathways-targets-metabolites (MSTM) using the R Package. We identified the uppermost six key targets (TLR4, RELA, IL6, PPARG, COX-2, and CYP1A2) against AFLD. The PPI network analysis revealed that TLR4, RELA, IL6, PPARG, and COX-2 had equivalent degrees of value (4); however, CYP1A2 had no associations with the other targets. The bubble chart showed that the PI3K-Akt signaling pathway in nine signaling pathways might be the most significant mechanism with antagonistic functions in the treatment of AFLD. The MDT confirmed that Icaritin is a promising agent to bind stably to RELA (known as NF-Κb). In parallel, Bacterium MRG-PMF-1, the PI3K-Akt signaling pathway, RELA, and Icaritin were the most significant components against AFLD in MSTM networks. In conclusion, we showed that the Icaritin–RELA complex on the PI3K-Akt signaling pathway by bacterial MRG-PMF-1 might have promising therapeutic effects against AFLD, providing crucial evidence for further research.

Background and objective: The urban heat island (UHI) effect is recognized as a representative environmental problem that occurs in cities in summer. This study aimed to quantitatively determine the surface temperature (ST) of UGSs using high-resolution images taken by an unmanned aerial vehicle (UAV), and analyze time-series changes in ST according to spatial characteristics (conifers, deciduous trees, shrubs, grass, metal sculptures, pavements).Methods: In this study, ST data of UGSs were established and acquired through UAV flight and filming, and orthoimages of such data were produced using the Pix4D program. In addition, by comparing RGB orthoimages and green space status data (location of trees and facilities) obtained from field surveys, a green-space type map was prepared using ArcGIS (v10.3.1) software to classify land cover types in green spaces (GSs). ST distribution by GS type was analyzed and statistical significance was verified through one-way ANOVA.Results: As a result, the ST of conifers, deciduous trees, shrubs, and grass, which are vegetation, was found to be lower than that of paved roads: for conifers, 4.1-12.5℃ lower than paved roads; for deciduous trees, 3.0-10.8℃; for shrubs 3.4-11.2℃; and for grass 1.7-8.1℃. In addition, the variations in ST over time were greatest for metal sculptures (28.1℃), followed by pavement (20.4℃), grass (19.4℃), shrubs (14.0℃), conifers (13.3℃), and deciduous trees (13.0℃).Conclusion: Based on the results of this study, it is necessary to consider the components of GS for the efficient planning and management of UGS in terms of improving the urban thermal environment. Insufficient and unsystematic planning and management of UGSs may deteriorate the function of GSs. Therefore, it is necessary to determine and evaluate the ST characteristics of GSs in terms of improving the urban thermal environment.