Objective: “Three formulas and three medicines,” namely, Jinhua Qinggan Granule, Lianhua Qingwen Capsule, Xuebijing Injection, Qingfei Paidu Decoction, HuaShi BaiDu Formula, and XuanFei BaiDu Granule, were proven to be effective for coronavirus disease 2019 (COVID-19) treatment. This study aimed to identify the active chemical constituents of this traditional Chinese medicine (TCM) and investigate their mechanisms through interleukin-6 (IL-6) integrating network pharmacological approaches.Methods: We collected the compounds from all herbal ingredients of the previously mentioned TCM, but those that could downregulate IL-6 were screened through the network pharmacology approach. Then, we modeled molecular docking to evaluate the binding affinity between compounds and IL-6. Furthermore, we analyzed the biological processes and pathways of compounds. Lastly, we screened out the core genes of compounds through the construction of the protein-protein interaction network and the excavation of gene clusters of compounds.Results: The network pharmacology research showed that TCM could decrease IL-6 using several compounds, such as quercetin, ursolic acid, luteolin, and rutin. Molecular docking results showed that the molecular binding affinity with IL-6 of all compounds except γ-aminobutyric acid was < −5.0 kJ/mol, indicating the potential of numerous active compounds in TCM to directly interact with IL-6, leading to an anti-inflammation effect. Finally, Cytoscape 3.7.2 was used to topologize the biological processes and pathways of compounds, revealing potential mechanisms for COVID-19 treatment.Conclusion: These results indicated the positive effect of TCM on the prevention and rehabilitation of COVID-19 in at-risk people. Quercetin, ursolic acid, luteolin, and rutin could inhibit COVID-19 by downregulating IL-6.

Pain modulates rhythmic neuronal activity recorded by Electroencephalography (EEG) in humans. Our laboratory previously showed that rat models of acute and neuropathic pain manifest increased power in primary somatosensory cortex (S1) recorded by electrocorticography (ECoG). In this study, we hypothesized that pain increases EEG power and corticocortical coherence in different rat models of pain, whereas treatments with clinically effective analgesics reverse these changes. Our results show increased cortical power over S1 and prefrontal cortex (PFC) in awake, freely behaving rat models of acute, inflammatory and neuropathic pain. Coherence between PFC and S1 is increased at a late, but not early, time point during the development of neuropathic pain. Electroencephalography power is not affected by ibuprofen in the acute pain model. However, pregabalin and mexiletine reverse the changes in power and S1-PFC coherence in the inflammatory and neuropathic pain models. These data suggest that quantitative EEG might be a valuable predictor of pain and analgesia in rodents.