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Abstract Background Nonalcoholic Steatohepatitis (NASH) results from complex liver conditions involving metabolic, inflammatory, and fibrogenic processes. Despite its burden, there has been a lack of any approved food-and-drug administration therapy up till now. Purpose Utilizing machine learning (ML) algorithms, the study aims to identify reliable potential genes to accurately predict the treatment response in the NASH animal model using biochemical and molecular markers retrieved using bioinformatics techniques. Methods The NASH-induced rat models were administered various microbiome-targeted therapies and herbal drugs for 12 weeks, these drugs resulted in reducing hepatic lipid accumulation, liver inflammation, and histopathological changes. The ML model was trained and tested based on the Histopathological NASH score (HPS); while (0–4) HPS considered Improved NASH and (5–8) considered non-improved, confirmed through rats’ liver histopathological examination, incorporates 34 features comprising 20 molecular markers (mRNAs-microRNAs-Long non-coding-RNAs) and 14 biochemical markers that are highly enriched in NASH pathogenesis. Six different ML models were used in the proposed model for the prediction of NASH improvement, with Gradient Boosting demonstrating the highest accuracy of 98% in predicting NASH drug response. Findings Following a gradual reduction in features, the outcomes demonstrated superior performance when employing the Random Forest classifier, yielding an accuracy of 98.4%. The principal selected molecular features included YAP1, LATS1, NF2, SRD5A3-AS1, FOXA2, TEAD2, miR-650, MMP14, ITGB1, and miR-6881-5P, while the biochemical markers comprised triglycerides (TG), ALT, ALP, total bilirubin (T. Bilirubin), alpha-fetoprotein (AFP), and low-density lipoprotein cholesterol (LDL-C). Conclusion This study introduced an ML model incorporating 16 noninvasive features, including molecular and biochemical signatures, which achieved high performance and accuracy in detecting NASH improvement. This model could potentially be used as diagnostic tools and to identify target therapies.

Abstract Background Nonalcoholic fatty pancreatitis (NAFP) presents a pressing challenge within the domain of metabolic disorders, necessitating further exploration to unveil its molecular intricacies and discover effective treatments. Our focus was to delve into the potential therapeutic impact of ZBiotic, a specially engineered strain of probiotic B. subtilis, in managing NAFP by targeting specific genes linked with necroptosis and the TNF signaling pathway, including TNF, ZBP1, HSPA1B, and MAPK3, along with their upstream epigenetic regulator, miR-5192, identified through bioinformatics. Methods Rats were subjected to either a standard or high-fat, high-sucrose diet (HFHS) for eight weeks. Subsequently, they were divided into groups: NAFP model, and two additional groups receiving daily doses of ZBiotic (0.5 ml and 1 ml/kg), and the original B. subtilis strain group (1 ml/kg) for four weeks, alongside the HFHS diet. Results ZBiotic exhibited remarkable efficacy in modulating gene expression, leading to the downregulation of miR-5192 and its target mRNAs (p

Abstract Background Nonalcoholic fatty pancreatitis (NAFP) is one of the metabolic syndrome manifestations that need further studies to determine its molecular determinants and find effective medications. We aimed to investigate the potential effect of benzyl propylene glycoside on NAFP management via targeting the pancreatic cGAS-STING pathway-related genes (DDX58, NFκB1 & CHUK) and their upstream regulator miRNA (miR-1976) that were retrieved from bioinformatics analysis. Methods The rats were fed either normal chow or a high-fat high-sucrose diet (HFHS), as a nutritional model for NAFP. After 8 weeks, the HFHS-fed rats were subdivided randomly into 4 groups; untreated HFHS group (NAFP model group) and three treated groups which received 3 doses of benzyl propylene glycoside (10, 20, and 30 mg/kg) daily for 4 weeks, parallel with HFHS feeding. Results The molecular analysis revealed that benzyl propylene glycoside could modulate the expression of the pancreatic cGAS-STING pathway-related through the downregulation of the expression of DDX58, NFκB1, and CHUK mRNAs and upregulation of miR-1976 expression. Moreover, the applied treatment reversed insulin resistance, inflammation, and fibrosis observed in the untreated NAFP group, as evidenced by improved lipid panel, decreased body weight and the serum level of lipase and amylase, reduced protein levels of NFκB1 and caspase-3 with a significant reduction in area % of collagen fibers in the pancreatic sections of treated animals. Conclusion benzyl propylene glycoside showed a potential ability to attenuate NAFP development, inhibit pancreatic inflammation and fibrosis and reduce the pathological and metabolic disturbances monitored in the applied NAFP animal model. The detected effect was correlated with modulation of the expression of pancreatic (DDX58, NFκB1, and CHUK mRNAs and miR-1976) panel.