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 to healthy controls, we were able to identify differentially expressed genes (DEGs). Differentially expressed genes were used to build the amoebiasis protein interaction network and calculated its network topological properties. We discovered that nine key hub genes(KHGs) are 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 key hub genes. These KHGs were primarily enriched in the IL-17 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, Toll-like receptor signaling pathway. miRNAs were grouped in various pathways, focusing on the TGF−beta signaling pathway, Human immunodeficiency virus 1 infection, Insulin signaling pathway, Signaling pathways regulating pluripotency of stem cells, etc. Amoebiasis KHGs (JUN, PTGS2, CCL2, 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.

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Abstract. The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or outliers in the data. Although FSVM has had some success in avoiding the negative effects of noise, it uses hinge loss, which maximizes the shortest distance between two classes and is ineffective in dealing with feature noise near the decision boundary. Furthermore, whereas FSVM concentrates on misclassification errors, it neglects to consider the critical within-class scatter minimization. We present a Fuzzy support vector machine with pinball loss (FPin-SVM), which is a fuzzy extension of a reformulation of a recently proposed support vector machine with pinball loss (Pin-SVM) with several significant improvements, to improve the performance of FSVM. First, because we used the squared L2- norm of errors variables instead of the L1 norm, our FPin-SVM is a strongly convex minimization problem; second, to speed up the training procedure, solutions of the proposed FPin-SVM, as an unconstrained minimization problem, are obtained using the functional iterative and Newton methods. Third, it is proposed to solve the minimization problem directly in primal. Unlike FSVM and Pin-SVM, our FPin-SVM does not require a toolbox for optimization. We dig deeper into the features of FPin-SVM, such as noise insensitivity and within-class scatter minimization. We conducted experiments on synthetic and real-world datasets with various sounds to validate the usefulness of the suggested approach. Compared to the SVM, FSVM, and Pin-SVM, the presented approaches demonstrate equivalent or superior generalization performance in less training time.

The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or outliers in the data. Although FSVM has had some success in avoiding the negative effects of noise, it uses hinge loss, which maximizes the shortest distance between two classes and is ineffective in dealing with feature noise near the decision boundary. Furthermore, whereas FSVM concentrates on misclassification errors, it neglects to consider the critical within-class scatter minimization. We present a Fuzzy support vector machine with pinball loss (FPin-SVM), which is a fuzzy extension of a reformulation of a recently proposed support vector machine with pinball loss (Pin-SVM) with several significant improvements, to improve the performance of FSVM. First, because we used the squared L2- norm of errors variables instead of the L1 norm, our FPin-SVM is a strongly convex minimization problem; second, to speed up the training procedure, solutions of the proposed FPin-SVM, as an unconstrained minimization problem, are obtained using the functional iterative and Newton methods. Third, it is proposed to solve the minimization problem directly in primal. Unlike FSVM and Pin-SVM, our FPin-SVM does not require a toolbox for optimization. We dig deeper into the features of FPin-SVM, such as noise insensitivity and within-class scatter minimization. We conducted experiments on synthetic and real-world datasets with various sounds to validate the usefulness of the suggested approach. Compared to the SVM, FSVM, and Pin-SVM, the presented approaches demonstrate equivalent or superior generalization performance in less training time.

Leishmania donovani, a kinetoplastid parasite causing leishmaniasis, is an opportunistic parasitic pathogen that affects immunocompromised individuals and is a common cause of Kala-azar. Specific parasite molecules can be delivered into host epithelial cells and may act as effector molecules for intracellular parasite development. So, there is a need to develop new approaches to understanding the interaction between the host and the pathogen. In our study, we built a weighted gene co-expression network using differentially expressed genes obtained through analysis of leishmaniasis-infected patients. Our goal was to identify key signature genes and pathways associated with visceral leishmaniasis infection by network biology analysis which can identify the most influential genes in the gene co-expression interaction network. We identified five prominent genes, IFNG, SC5D, LSM1, CMC2, and SAR1B, with higher interamodular connectivity, as the key signature genes. A deep neural network model- variational autoencoder was utilized to create new features, and a support vector machine validated the key signature genes. These key signature genes are involved in various biological processes like cytokine-cytokine receptor interaction, TGF-beta signaling pathway, antigen processing and presentation, IL-17 signaling pathway, Th1 and Th2 cell differentiation, and T-cell receptor signaling pathway. Besides, we also identified 04 significant miRNAs targeted with key signature genes, including hsa-miR-340-5p, hsa-miR-325-3p, hsa-miR-182-5p, hsa-miR-1271-5p/hsa-miR-96-5p. Further, analysis of the differentially expressed genes revealed that many critical cellular responses were triggered by visceral leishmaniasis infection, including immune responses and inflammatory and cell apoptosis. We get FDA-approved anti-inflammatory agents Emapalumab and Methylprednisolone as a re-proposed drug for leishmaniasis cure. Our study can enhance the understanding of the molecular pathogenesis of visceral leishmaniasis infection and have implications for the plan and execution of mRNA expression tools to support early diagnostics and treatment of visceral leishmaniasis infection.