Classification and diagnostic prediction of prostate cancer using gene expression and artificial neural networks

Toxicity and therapeutic property of dioxopiperidin derivative SKT40 demonstrated in-vivo zebrafish model due to inflammatory bowel disease

Inflammatory bowel disease (IBD) encompasses chronic disorders that cause severe inflammation in the digestive tract. This study evaluates (E)-3-(3,4-dichlorophenyl)-N-(2,6-dioxopiperidin-3-yl) acrylamide (named SKT40), a derivative of dioxopiperidinamide, as a potential novel treatment for IBD. The pharmacological activity of SKT40 indicated positive interactions using network pharmacology and molecular docking in silico. In vivo, adult and larval zebrafish were tested to evaluate the effectiveness of SKT40 at different concentrations (7.5 μM, 10 μM, 15 μM) in preventing dextran sulfate sodium (DSS)-induced intestinal inflammation. The administration of SKT40 resulted in positive effects by reducing reactive oxygen species (ROS), lipid peroxidation, and cell apoptosis in zebrafish larvae. SKT40 demonstrated a significant reduction in intestinal damage in adult zebrafish by increasing antioxidant enzymes that combat the causes of IBD, such as superoxide dismutase (SOD), catalase (CAT), glutathione-S-transferase (GST), and glutathione peroxidase (GPx). It also reduces cellular damage and inflammation, as indicated by decreased levels of lactate dehydrogenase (LDH) and malondialdehyde (MDA). Gene expression analysis identified downregulation in gene expression of inflammatory mediators such as TNF-α, IL-1β, COX-2, and IL-6. Histopathological analysis showed tissue repair from DSS-induced damage and indicated reduced hyperplasia of goblet cells. These findings suggest that SKT40 effectively treats intestinal damage, highlighting its potential as a promising candidate for IBD therapy.

Fuzzy and Rough Set Theory Based Computational Framework for Mining Genetic Interaction Triplets From Gene Expression Profiles for Lung Adenocarcinoma

Genetic interactions are very helpful in understanding different disease and discovering drugs for it. Compared to the gene pairs that represent the genetic interactions between two genes, the gene triplets are more informative and useful. However, existing works on genetic interactions among gene triplets have primarily focused on detecting gene triplets from time series gene expression profiles. Generating the time series gene expression profiles for humans is quite impracticable but the labeled gene expression profiles are available for different diseases in case of humans. In this paper, a computational framework has been proposed to detect gene triplets from labeled gene expression profiles. First, it employs Rough Set Theory for extracting the key genes and then designs a fuzzy inference system for generating possible gene triplets. Further, Root Mean Squared Error measure has been used to prune out the irrelevant gene triplets. In the present work, the proposed computational framework has been applied to labeled lung adenocarcinoma dataset and can be applied to any other labeled gene expression dataset. The extracted gene triplets and their functionalities have been verified with existing biological literature and benchmark databases and the results of verification signify that the proposed framework is promising in terms of finding useful genetic triplets. Further, the proposed framework has been found more efficient as compared to an existing mutual information-based technique in terms of detecting known genetic interactions.