Search

Background: The coronavirus disease 2019 (COVID-19) has been spreading astonishingly and caused catastrophic losses worldwide. The high mortality of severe COVID-19 patients is an serious problem that needs to be solved urgently. However, the biomarkers and fundamental pathological mechanisms of severe COVID-19 are poorly understood. The aims of this study was to explore key genes related to inflammasome in severe COVID-19 and their potential molecular mechanisms using random forest and artificial neural network modeling. Methods: Differentially expressed genes (DEGs) in severe COVID-19 were screened from GSE151764 and GSE183533 via comprehensive transcriptome Meta-analysis. Protein-protein interaction (PPI) networks and functional analyses were conducted to identify molecular mechanisms related to DEGs or DEGs associated with inflammasome (IADEGs), respectively. Five the most important IADEGs in severe COVID-19 were explored using random forest. Then, we put these five IADEGs into an artificial neural network to construct a novel diagnostic model for severe COVID-19 and verified its diagnostic efficacy in GSE205099. Results: Using combining P value < 0.05, we obtained 192 DEGs, 40 of which are IADEGs. The GO enrichment analysis results indicated that 192 DEGs were mainly involved in T cell activation, MHC protein complex and immune receptor activity. The KEGG enrichment analysis results indicated that 192 GEGs were mainly involved in Th17 cell differentiation, IL-17 signaling pathway, mTOR signaling pathway and NOD-like receptor signaling pathway. In addition, the top GO terms of 40 IADEGs were involved in T cell activation, immune response-activating signal transduction, external side of plasma membrane and phosphatase binding. The KEGG enrichment analysis results indicated that IADEGs were mainly involved in FoxO signaling pathway, Toll-like receptor, JAK-STAT signaling pathway and Apoptosis. Then, five important IADEGs (AXL, MKI67, CDKN3, BCL2 and PTGS2) for severe COVID-19 were screened by random forest analysis. By building an artificial neural network model, we found that the AUC values of 5 important IADEGs were 0.972 and 0.844 in the train group (GSE151764 and GSE183533) and test group (GSE205099), respectively. Conclusion: The five genes related to inflammasome, including AXL, MKI67, CDKN3, BCL2 and PTGS2, are important for severe COVID-19 patients, and these molecules are related to the activation of NLRP3 inflammasome. Furthermore, AXL, MKI67, CDKN3, BCL2 and PTGS2 as a marker combination could be used as potential markers to identify severe COVID- 19 patients. [ABSTRACT FROM AUTHOR]

Background: The antiviral activity and underlying mechanism of Patchouli alcohol remain unclear. Methods: This study evaluated the cytotoxicity, optimal methods for drug administration, anti-influenza A activity of Patchouli alcohol. The antiviral mechanism of Patchouli alcohol was also assessed via qRT-PCR, western blot, hemagglutination inhibiting (HAI) assay, and hemolysis inhibiting assay. Results: Patchouli alcohol was shown to have low cytotoxicity and its strongest antiviral effect was associated with premixed administration. Patchouli alcohol inhibited virus replication during the early lifecycle stages of influenza A virus infection and specifically prevented expression of the viral proteins, HA and NP. In both the HAI and hemolysis inhibiting assays, Patchouli alcohol was able to block HA2-mediated membrane fusion under low pH conditions. Patchouli alcohol had lower binding energy with HA2 than HA1. Conclusion: These findings suggest that Patchouli alcohol could be a promising membrane fusion inhibitor for the treatment of influenza A infection. [ABSTRACT FROM AUTHOR]

The Mediterranean diet (MED), a dietary pattern rich in fruits and vegetables, whole grains, legumes, nuts, fish, and olive oil, has anti-oxidative and anti-inflammatory e􀀀ects. Although some data suggest that MED adherence is associated with decreasedmanifestation of depressive symptoms, it remains necessary to further analyze this apparent non-linear association as well as the influence of di􀀀erent factors on the relationship betweenMED and depression. Here, we investigated associations between the alternate MED (aMED) score and depressive symptom via multivariate logistic regression, weighted generalized additive (GAM) and two-step linear regression models, analyzing data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES). The most important factor relevant to aMED score that contributed to the prevalence of depressive symptom was assessed using random forest. Furthermore, we examined whether the relationship between aMED score and depressive symptom di􀀀ers by age, race, sex, socioeconomic variables, lifestyle- and health-related variables, and chronic medical conditions, via subgroup analyses. A total of 19,477 participants (20-80 years of age) were included in this cross-sectional study. In crude and adjusted (1-5) multivariate logistic regression models, increased aMED score was noted to associate with non-depressive status, as defined using the Patient Health Questionnaire-9 (P < 0.05). Data analyses via GAM and two-piecewise linear regression revealed a non-linear association between aMED and depressive symptom, which had an inflection point of 3. Random forest results revealed that vegetable score contributes greatest to the relationship between aMED and depressive symptom. Subgroup analyses revealed that aMED score is significantly negatively related with depressive symptom in most di􀀀erent populations (P < 0.05) with the exception of high annual income, diabetes, borderline blood glucose level and Parkinson's disease (PD) (P > 0.05). In conclusion, we observed a non-linear association between aMED score and depressive symptom. Further studies are needed to validate our results. [ABSTRACT FROM AUTHOR]