ABSTRACT
Fructus Aurantii (FA) is the dry and immature fruit of Citrus aurantium L. and its rutaceous cultivars. FA has been widely used to treat digestive system diseases since ancient China, and it promotes gastrointestinal (GI) motility in functional dyspepsia (FD), but its potential therapeutic mechanisms remain unclear. We examined the effects of FA ethanol extracts in an iodoacetamide (IA)-induced FD rat model. Firstly, key FA therapy targets for FD were gathered using systematic pharmacology. Combined with systemic pharmacological analyses, plasma metabolomics based on UPLC-QTOF-MS were conducted. Then, MetaboAnalyst was used to jointly analyze systemic pharmacology targets and metabolomic metabolites to select key metabolic pathways. Finally, the key path is verified by experiments. FA exerted distinct therapeutic effects in anti-inflammation and promoting gastrointestinal motility in our IA-induced FD rat model. When compared with the model group, FA down-regulated the inflammatory factors interleukin 1beta and tumor necrosis factor-a. At the same time, FA up-regulated tight junction proteins in the intestinal epithelial barrier. Through the integrated analysis of metabolomics and systemic pharmacology, we conducted experimental verification on Fc epsilon RI signaling pathway. When compared with the model group, FA down-regulatedphospho-mitogen activated protein kinase, phospho-extracellular signal regulated kinase1/2, myosin light chain kinase, and phospho-myosin regulatory light chain protein levels. Thus, FA ameliorated FD by regulating the Fc epsilon RI signaling pathway. Our integrated strategy identified underlying FA mechanisms toward FD treatment and provided a foundation for FA development as a clinical agent for FD.
ABSTRACT
The rapid dissemination of misinformation in social media during the COVID-19 pandemic triggers panic and threatens the pandemic preparedness and control. Correction is a crucial countermeasure to debunk misperceptions. However, the effective mechanism of correction on social media is not fully verified. Previous works focus on psychological theories and experimental studies, while the applicability of conclusions to the actual social media is unclear. This study explores determinants governing the effectiveness of misinformation corrections on social media with a combination of a data-driven approach and related theories on psychology and communication. Specifically, referring to the Backfire Effect, Source Credibility, and Audience's role in dissemination theories, we propose five hypotheses containing seven potential factors (regarding correction content and publishers' influence), e.g., the proportion of original misinformation and warnings of misinformation. Then, we obtain 1487 significant COVID-19 related corrections on Microblog between January 1st, 2020 and April 30th, 2020, and conduct annotations, which characterize each piece of correction based on the aforementioned factors. We demonstrate several promising conclusions through a comprehensive analysis of the dataset. For example, mentioning excessive original misinformation in corrections would not undermine people's believability within a short period after reading; warnings of misinformation in a demanding tone make correction worse; determinants of correction effectiveness vary among different topics of misinformation. Finally, we build a regression model to predict correction effectiveness. These results provide practical suggestions on misinformation correction on social media, and a tool to guide practitioners to revise corrections before publishing, leading to ideal efficacies.
ABSTRACT
The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China's epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country's total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.