Entamoeba histolytica HM-1: IMSS gene expression profiling identifies key hub genes, potential biomarkers, and pathways in Amoebiasis infection: a systematic network meta-analysis.

Entamoeba histolytica (E. histolytica) is an anaerobic parasite that causes Amoebiasis in the intestine or extraintestinal, with immunology, genetics, and environmental variables all playing a part in the disease's development, but its molecular mechanism is unknown. One of the primary obstacles in understanding the etiology of Amoebiasis will be identifying the genetics profiling that controls the Amoebiasis network. By examining the gene expression profile of Amoebiasis and comparing it with healthy controls, we could identify differentially expressed genes (DEGs). DEGs were used to build the Amoebiasis protein interaction network and calculated its network topological properties. We discovered nine key hub genes (KHGs): JUN, PTGS2, FCGR3A, MNDA, CYBB, EGR1, CCL2, TLR8, and LRRK2 genes. The genes JUN and EGR1 were transcriptional factors (TFs) and up-regulated, others down-regulated. hsa-miR-155-5p, hsa-miR-101-3p, hsa-miR-124-3p, hsa-miR-26b-5p, and hsa-miR-16-5p are also among the essential miRNAs that have been demonstrated to be targeted by KHGs. These KHGs were primarily enriched in the IL-17 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, and Toll-like receptor signaling pathway. miRNAs were grouped in various pathways, focusing on the TGF-ß signaling pathway, human immunodeficiency virus 1 infection, insulin signaling pathway, signaling pathways regulating pluripotency of stem cells, etc. Amoebiasis KHGs (JUN, PTGS2, CCL2, and MNDA) and their associated miRNAs are the primary targets for therapeutic methods and possible biomarkers. Furthermore, we identified drugs for genes JUN, PTGS2, FCGR3A, CCL2, and LRRK2. KHGs, on the other hand, required experimental validation to prove their efficacy.

Impact of Information based Classification on Network Epidemics.

Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification. This is the first such attempt to add such a classification into the existing epidemic framework. The model is incorporated into a five class system called the DifEpGoss architecture. Analysis reveals an epidemic threshold, based on which the long-term behavior of the system is analyzed. In this work three real network datasets with 22002, 22469 and 22607 undirected edges respectively, are used. The datasets show that classification based prevention given in the model can have a good role in containing network epidemics. Further simulation based experiments are used with a three category classification of attack and defense strengths, which allows us to consider 27 different possibilities. These experiments further corroborate the utility of the proposed model. The paper concludes with several interesting results.