Panax Notoginseng Saponins Regulate Transforming Growth Factor-ß1 through MAPK and Snail/TWIST1 Signaling Pathway to Inhibit Epithelial-Mesenchymal Transition of Pulmonary Fibrosis in A549 Cells.

Panaxnotoginseng saponins (PNS) is one of the active components of traditional Chinese medicine Panax notoginseng which has the function of reducing oxygen consumption, expansion of the cerebrovascular system, and is antithrombotic. PNS also plays a role in the treatment of pulmonary fibrosis. In this study, we found that PNS suppresses fibroblast-like changes in A549 cells through epithelial-mesenchymal transition (EMT). PNS promoted E-cadherin (E-cad) in epithelial cells and decreased Fibronectin (FN) and Vimentin (Vim) expression in myofibroblasts in a dose-dependent manner. Further mechanism studies have shown that PNS inhibits the EMT process by regulating p38, JNK, and Erk signaling factors in the MAPK signaling pathway and then blocking Snail and TWIST1 transcription factors from entering the nucleus. This indicates that PNS can regulate epithelial-mesenchymal transition through MAPK and the Snail/TWIST1 signaling pathway, thereby exerting its antipulmonary fibrosis effect.

Interfering effects on the bioactivities of several key proteins of COVID-19/variants in diabetes by compounds from Lianqiao leaves: In silico and in vitro analyses.

Diabetes is considered to be one of the diseases most associated with COVID-19. In this study, interfering effects and potential mechanisms of several compounds from Lianqiao (Forsythia suspensa (Thunb.) Vahl) leaves on the bioactivities of some key proteins of COVID-19 and its variants, as well as diabetic endothelial dysfunctions were illuminated through in vitro and in silico analyses. Results showed that, among the main ingredients in the leaves, forsythoside A showed the strongest docking affinities with the proteins SARS-CoV-2-RBD-hACE2 of COVID-19 and its variants (Alpha (B.1.1.7), Beta (B.1.351), and Delta (B.1.617)), as well as neuropilin-1 (NRP1), and SARS-CoV-2 main protease (MPro) to interfere coronavirus entering into the human body. Moreover, forsythoside A was the most stable in binding to receptors in Delta (B.1.617) system. It also has good antiviral activities and drug properties and has the strongest binding force to the RBD domain of COVID-19. In addition, forsythoside A reduced ROS production in AGEs-induced EA.hy926 cells, maintained endothelial integrity, and bound closely to protein profilin-1 (PFN1) receptor. This work may provide useful knowledge for further understanding the interfering effects and potential mechanisms of compounds, especially forsythoside A, from Lianqiao leaves on the bioactivities of key proteins of COVID-19/variants in diabetes.

A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics.

BACKGROUND: Currently, the need to prevent and control the spread of the 2019 novel coronavirus disease (COVID-19) outside of Hubei province in China and internationally has become increasingly critical. We developed and validated a diagnostic model that does not rely on computed tomography (CT) images to aid in the early identification of suspected COVID-19 pneumonia (S-COVID-19-P) patients admitted to adult fever clinics and made the validated model available via an online triage calculator. METHODS: Patients admitted from January 14 to February 26, 2020 with an epidemiological history of exposure to COVID-19 were included in the study [model development group (n=132) and validation group (n=32)]. Candidate features included clinical symptoms, routine laboratory tests, and other clinical information on admission. The features selection and model development were based on the least absolute shrinkage and selection operator (LASSO) regression. The primary outcome was the development and validation of a diagnostic aid model for the early identification of S-COVID-19-P on admission. RESULTS: The development cohort contained 26 cases of S-COVID-19-P and seven cases of confirmed COVID-19 pneumonia (C-COVID-19-P). The final selected features included one demographic variable, four vital signs, five routine blood values, seven clinical signs and symptoms, and one infection-related biomarker. The model's performance in the testing set and the validation group resulted in area under the receiver operating characteristic (ROC) curves (AUCs) of 0.841 and 0.938, F1 scores of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and precision of 0.400 and 0.500, respectively. The top five most important features were age, interleukin-6 (IL-6), systolic blood pressure (SYS_BP), monocyte ratio (MONO%), and fever classification (FC). Based on this model, an optimized strategy for the early identification of S-COVID-19-P in fever clinics has also been designed. CONCLUSIONS: A machine-learning model based solely on clinical information and not on CT images was able to perform the early identification of S-COVID-19-P on admission in fever clinics with a 100% recall score. This high-performing and validated model has been deployed as an online triage tool, which is available at https://intensivecare.shinyapps.io/COVID19/.