Experimental investigation for nonalcoholic fatty pancreas management using probiotics.

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 < 0.001). Treatment resulted in the reversal of fibrosis, inflammation, and insulin resistance, evidenced by reductions in body weight, serum amylase, and lipase levels (p < 0.001), and decreased percentages of Caspase and Nuclear Factor Kappa-positive cells in pancreatic sections (p < 0.01). Notably, high-dose ZBiotic displayed superior efficacy compared to the original B. subtilis strain, highlighting its potential in mitigating NAFP progression by regulating pivotal pancreatic genes. CONCLUSION: ZBiotic holds promise in curbing NAFP advancement, curbing fibrosis and inflammation while alleviating metabolic and pathological irregularities observed in the NAFP animal model. This impact was intricately linked to the modulation of necroptosis/TNF-mediated pathway-related signatures.

Machine-Learning-Based Identification of Key Feature RNA-Signature Linked to Diagnosis of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the third prime cause of malignancy-related mortality worldwide. Early and accurate identification of HCC is crucial for good prognosis, efficacy of therapy, and survival rates of the patients. We aimed to develop a machine-learning model incorporating differentially expressed RNA signatures with laboratory parameters to construct an RNA signature-based diagnostic model for HCC. Methods: We have used five classifiers (KNN, RF, SVM, LGBM, and DNNs) to predict the liver disease (HCC). The classifiers were trained on 187 samples and then tested on 80 samples. The model included 22 features (age, sex, smoking, cirrhosis, non-cirrhosis, albumin, ALT, AST bilirubin (total and direct), INR, AFP, HBV Ag, HCV Abs, RQmiR-1298, RQmiR-1262, RQmiR-106b-3p, RQmRNARAB11A, and RQSTAT1, RQmRNAATG12, RQLnc-WRAP53, RQLncRNA- RP11-513I15.6). Results: LGBM achieved the highest accuracy of 98.75% in predicting HCC among all models surpassing Random Forest (96.25%), DNN (91.25%), SVC (88.75%), and KNN (87.50%). Conclusion: Our machine-learning model incorporating the expression data of RAB11A/STAT1/ATG12/miR-1262/miR-1298/miR-106b-3p/lncRNA-RP11-513I15.6/lncRNA-WRAP53 signature and clinical data represents a potential novel diagnostic model for HCC.

Amitriptyline and cholecalciferol amend hippocampal histological structure and myelination during stress in Wistar rats via regulating miR200/BMP4/Olig-2 signaling.

Chronic stress is a universal condition commonly associated with many psychiatric diseases. An extensive body of evidence discussed hippocampal affection upon chronic stress exposure, however, the underlying molecular pathways still need to be identified. We investigated the impact of chronic stress on miR200/BMP/Olig-2 signaling and hippocampal myelination. We also compared the effects of chronic administration of amitriptyline and cholecalciferol on chronically stressed hippocampi. Both amitriptyline and cholecalciferol significantly decreased serum cortisol levels, reduced immobility time in the forced swim test, increased the number of crossed squares in open field test, decreased the hippocampal expression of bone morphogenetic protein 4 (BMP4) and its messenger RNA (mRNA) levels, reduced miR200 expression as compared to untreated chronically stressed rats. Also, both drugs amended the hippocampal neuronal damage, enhanced the surviving cell count, and increased the pyramidal layer thickness of Cornu Ammonis subregion 1 (CA1) and granule cell layer of the dentate gyrus. Cholecalciferol was more effective in increasing the area percentage of myelin basic protein (MBP) and Olig-2 positive cells count in hippocampi of chronic stress-exposed rats than amitriptyline, thus enhancing myelination. We also found a negative correlation between the expression of BMP4, its mRNA, miR200, and the immunoexpression of MBP and Olig-2 proteins. This work underscores the amelioration of the stress-induced behavioral changes, inhibition of miR200/BMP4 signaling, and enhancement of hippocampal myelination following chronic administration of either amitriptyline or cholecalciferol, though cholecalciferol seemed more effective in brain remyelination.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Results and discussion: Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.

Rosavin improves insulin resistance and alleviates hepatic and kidney damage via modulating the cGAS-STING pathway and autophagy signaling in HFD/STZ-induced T2DM animals.

Background: Inflammation-mediated insulin resistance in type 2 diabetes mellitus (T2DM) increases complications, necessitating investigation of its mechanism to find new safe therapies. This study investigated the effect of rosavin on the autophagy and the cGAS-STING pathway-related signatures (ZBP1, STING1, DDX58, LC3B, TNF-α) and on their epigenetic modifiers (miR-1976 and lncRNA AC074117.2) that were identified from in silico analysis in T2DM animals. Methods: A T2DM rat model was established by combining a high-fat diet (HFD) and streptozotocin (STZ). After four weeks from T2DM induction, HFD/STZ-induced T2DM rats were subdivided into an untreated group (T2DM group) and three treated groups which received 10, 20, or 30 mg per kg of R. rosea daily for 4 weeks. Results: The study found that rosavin can affect the cGAS-STING pathway-related RNA signatures by decreasing the expressions of ZBP1, STING1, DDX58, and miR-1976 while increasing the lncRNA AC074117.2 level in the liver, kidney, and adipose tissues. Rosavin prevented further weight loss, reduced serum insulin and glucose, improved insulin resistance and the lipid panel, and mitigated liver and kidney damage compared to the untreated T2DM group. The treatment also resulted in reduced inflammation levels and improved autophagy manifested by decreased immunostaining of TNF-α and increased immunostaining of LC3B in the liver and kidneys of the treated T2DM rats. Conclusion: Rosavin has shown potential in attenuating T2DM, inhibiting inflammation in the liver and kidneys, and improving metabolic disturbances in a T2DM animal model. The observed effect was linked to the activation of autophagy and suppression of the cGAS-STING pathway.

Discriminatory power of a circulating multi-noncoding RNA panel in acute coronary syndrome subtypes: Towards precision detection.

BACKGROUND: Acute Coronary Syndrome (ACS) stands as a significant contributor to cardiovascular mortality, necessitating improved diagnostic tools for early detection and tailored therapeutic interventions. Current diagnostic modalities, exhibit limitations in sensitivity and specificity, urging the quest for novel biomarkers to enhance discrimination of the different stages of ACS including unstable angina, Non-ST-segment Elevation Myocardial Infarction (NSTEMI), and ST-segment Elevation Myocardial Infarction (STEMI). METHODS: This study investigated the potential of a plasma-circulating multi-noncoding RNA (ncRNA) panel, comprising four miRNAs (miR-182-5p, miR-23a-3p, miR-146a-5p, and miR-183-5p) and three lncRNAs (SNHG15, SNHG5, and RMRP), selected based on their intricate involvement in ACS pathogenesis and signaling pathways regulating post-myocardial infarction (MI) processes. The differential expression of these ncRNAs was validated in sera of ACS patients and healthy controls via real-time polymerase chain reaction (RT-PCR). RESULTS: Analysis revealed a marked upregulation of the multi-ncRNAs panel in ACS patients. Notably, miRNA-182-5p and lncRNA-RMRP exhibited exceptional discriminatory power, indicated by the high area under the curve (AUC) values (0.990 and 0.980, respectively). Importantly, this panel displayed superior efficacy in discriminating between STEMI and NSTEMI, outperforming conventional biomarkers like creatine kinase-MB and cardiac troponins. Additionally, the four miRNAs and lncRNA RMRP showcased remarkable proficiency in distinguishing between STEMI and unstable angina. CONCLUSION: The findings underscore the promising potential of the multi-ncRNA panel as a robust tool for early ACS detection, and precise differentiation among ACS subtypes, and as a potential therapeutic target.