Background The traditional Chinese medicine (TCM) formula Qing-Fei-Pai-Du decoction (QFPDD) was the most widely used prescription in China's campaign to contain COVID-19, which has exhibited positive effects. However, the underlying mode of action is largely unknown. Purpose A systems pharmacology strategy was proposed to investigate the mechanisms of QFPDD against COVID-19 from molecule, pathway and network levels. Study design and methods The systems pharmacological approach consisted of text mining, target prediction, data integration, network study, bioinformatics analysis, molecular docking, and pharmacological validation. Especially, we proposed a scoring method to measure the confidence of targets identified by prediction and text mining, while a novel scheme was used to identify important targets from 4 aspects. Results 623 high-confidence targets of QFPDD's 12 active compounds were identified, 88 of which were overlapped with genes affected by SARS-CoV-2 infection. These targets were found to be involved in biological processes related with the development of COVID-19, such as pattern recognition receptor signaling, interleukin signaling, cell growth and death, hemostasis, and injuries of the nervous, sensory, circulatory, and digestive systems. Comprehensive network and pathway analysis were used to identify 55 important targets, which regulated 5 functional modules corresponding to QFPDD's effects in immune regulation, anti-infection, anti-inflammation, and multi-organ protection, respectively. Four compounds (baicalin, glycyrrhizic acid, hesperidin, and hyperoside) and 7 targets (AKT1, TNF-α, IL6, PTGS2, HMOX1, IL10, and TP53) were key molecules related to QFPDD's effects. Molecular docking verified that QFPDD's compounds may bind to 6 host proteins that interact with SARS-CoV-2 proteins, further supported the anti-virus effect of QFPDD. At last, in intro experiments validated QFPDD's important effects, including the inhibition of IL6, CCL2, TNF-α, NF-κB, PTGS1/2, CYP1A1, CYP3A4 activity, the up-regulation of IL10 expression, and repressing platelet aggregation. Conclusion This work illustrated that QFPDD could exhibit immune regulation, anti-infection, anti-inflammation, and multi-organ protection. It may strengthen the understanding of QFPDD and facilitate more application of this formula in the campaign to SARS-CoV-2.

Link prediction aims to uncover missing links or predict the emergence of future relationships from the current network structure. Plenty of algorithms have been developed for link prediction in unweighted networks, but only a few have been extended to weighted networks. In this paper, we present what we call a “reliable-route method” to extend unweighted local similarity indices to weighted ones. Using these indices, we can predict both the existence of links and their weights. Experiments on various real-world networks suggest that our reliable-route weighted resource-allocation index performs noticeably better than others with respect to weight prediction. For existence prediction it is either the highest or very close to the highest. Further analysis shows a strong positive correlation between the clustering coefficient and prediction accuracy. Finally, we apply our method to the prediction of missing protein-protein interactions and their confidence scores from known PPI networks. Once again, our reliable-route method shows the highest accuracy.

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The advertising industry has recently witnessed proliferation in native ads, which are inserted into a web stream (e.g., a list of news articles or social media posts) and look like the surrounding nonsponsored contents. This study is among the first to examine native ads and unveil how their effectiveness changes across serial positions by analyzing a large-scale data set with 120 ads. For each ad, the authors use separate “natural experiment” studies to compare the ad’s performance as its serial position varies. Subsequently, they conduct a meta-analysis to generalize the results across all studies. The results reveal vastly asymmetric effects of native ad serial position on publishers’ metrics (click-based) versus advertisers’ metrics (conversion-based). As serial position lowers (i.e., from rank 1 to a lower rank), there are only modest changes in publishers’ metrics, but drastic reductions in advertisers’. This pattern is unique to native ads and has not been indicated by prior research on ad serial position. Moreover, the authors show the moderating effects of audience gender and age. The findings provide new and timely implications for researchers and marketers.