RESUMO
BACKGROUND: Systemic lupus erythematosus is a chronic autoimmune inflammatory disease characterized by multiple symptoms. The phenolic acids and other flavonoids in Nelumbo nucifera have anti-oxidants, anti-inflammatory, and immunomodulatory activities that are essential for managing SLE through natural sources. This study employs network pharmacology to unveil the multi-target and multi-pathway mechanisms of Nelumbo nucifera as a complementary therapy. The findings are validated through molecular modeling, which includes molecular docking followed by a molecular dynamics study. METHODS: Active compounds and targets of SLE were obtained from IMPPAT, KNApAcKFamily and SwissTargetPrediction databases. SLE-related targets were retrieved from GeneCards and OMIM databases. A protein-protein interaction (PPI) network was built to screen out the core targets using Cytoscape software. ShinyGO was used for GO and KEGG pathway enrichment analyses. Interactions between potential targets and active compounds were assessed by molecular docking and molecular dynamics simulation study. RESULTS: In total, 12 active compounds and 1190 targets of N. nucifera's were identified. A network analysis of the PPI network revealed 10 core targets. GO and KEGG pathway enrichment analyses indicated that the effects of N. nucifera are mediated mainly by AGE-RAGE and other associated signalling pathways. Molecular docking indicated favourable binding affinities, particularly leucocianidol exhibiting less than -4.5 kcal/mol for all 10 targets. Subsequent molecular dynamics simulations of the leucocianidol-ESR1 complex aimed to elucidate the optimal binding complex's stability and flexibility. CONCLUSIONS: Our study unveiled the potential therapeutic mechanism of N. nucifera in managing SLE. These findings provide insights for subsequent experimental validation and open up new avenues for further research in this field.
RESUMO
BACKGROUND AND AIM: The regular uptake of a high-fat diet (HFD) with changing lifestyle causes atherosclerosis leading to cardiovascular diseases and autonomic dysfunction. Therefore, the current study aimed to investigate the correlation of autonomic activity to lipid and atherosclerosis markers. Further, the study proposes a support vector machine (SVM) based model in the prediction of atherosclerosis severity. METHODS AND RESULTS: The Lead-II electrocardiogram and blood markers were measured from both the control and the experiment subjects each week for nine consecutive weeks. The time-domain heart rate variability (HRV) parameters were derived, and the significance level was tested using a one-way Analysis of Variance. The correlation analysis was performed to determine the relation between autonomic parameters and lipid and atherosclerosis markers. The statistically significant time-domain values were used as features of the SVM. The observed results demonstrated the reduced time domain HRV parameters with the increase in lipid and atherosclerosis index markers with the progressive atherosclerosis severity. The correlation analysis revealed a negative association between time-domain HRV parameters with lipid and atherosclerosis parameters. The percentage accuracy increases from 86.58% to 98.71% with the increase in atherosclerosis severity with regular consumption of HFD. CONCLUSIONS: Atherosclerosis causes autonomic dysfunction with reduced HRV. The negative correlation between autonomic parameters and lipid profile and atherosclerosis indexes marker revealed the potential role of vagal activity in the prognosis of atherosclerosis progression. The support vector machine presented a respectable accuracy in the prediction of atherosclerosis severity from the control group.