Despite the existence of modern antidiabetic medications, diabetes still affects millions of individuals worldwide, with a high death and disability rate. There has been a concerted search for alternative natural medicinal agents; luteolin (LUT), a polyphenolic molecule, might be a good choice, both because of its efficacy and because of it having fewer side effects, compared to conventional medicines. This study aims to explore the antidiabetic potential of LUT in diabetic rats, induced by streptozotocin (STZ; 50 mg/kg b.w.), intraperitoneally. The level of blood glucose, oral glucose tolerance test (OGTT), body weight, glycated hemoglobin A1c (HbA1c), lipidemic status, antioxidant enzymes, and cytokines were assessed. Also, its action mechanism was explored through molecular docking and molecular dynamics simulations. Oral supplementation of LUT for 21 days resulted in a significant decrease in the blood glucose, oxidative stress, and proinflammatory cytokine levels, and modulated the hyperlipidemia profile. LUT also ameliorated the tested biomarkers of liver and kidney function. In addition, LUT markedly reversed the damage to the pancreas, liver, and kidney cells. Moreover, molecular docking and molecular dynamics simulations revealed excellent antidiabetic behavior of LUT. In conclusion, the current investigation revealed that LUT possesses antidiabetic activity, through the reversing of hyperlipidemia, oxidative stress, and proinflammatory status in diabetic groups. Therefore, LUT might be a good remedy for the management or treatment of diabetes.

Background: Diabetes is a metabolic disorder characterized by increased blood sugar levels. Early detection of diabetes could help individuals to manage and delay the progression of this disorder effectively. Machine learning (ML) methods are important in forecasting the progression and diagnosis of different medical problems with better accuracy. Although they cannot substitute the work of physicians in the prediction and diagnosis of disease, they can be of great help in identifying hidden patterns based on the results and outcome of disease. Methods: In this research, we retrieved the PIMA dataset from the Kaggle repository, the retrieved dataset was further processed for applied PCA, heatmap, and scatter plot for exploratory data analysis (EDA), which helps to find out the relationship between various features in the dataset using visual representation. Four different ML algorithms Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), and Logistic regression (LR) were implemented on Rattle using Python for the prediction of diabetes among the female population. Results: Results of our study showed that RF performs better in terms of accuracy of 80%, precision of 82%, error rate of 20%, and sensitivity of 88% as compared to other developed models DT, NB, and LR. Conclusions: Diabetes is a common problem prevailing across the globe, ML-based prediction models can help in the prediction of diabetes much earlier before the worsening of the condition.

Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural compounds from C. intybus, which is commonly known as chicory, were selected, as they have promising antidiabetic properties that can complement conventional diabetes treatments. A bioactive natural compound dataset was retrieved on the chicory plant using Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) public source information. This collected dataset was analyzed for its absorption, distribution, metabolism, and excretion (ADME) properties using the SwissADME online tool. Principal component analysis (PCA) and correlation analysis were performed using trial-version XLSTAT software 2014.5.03 and Python. The obtained dataset from SwissADME was subjected to cleaning, after that, it was used to develop machine learning models, such as support vacuum (SVM) ML, random forest (RF), Naive Bayes (NB), and decision tree (DT). The Lipinski rule of violation was chosen as the target variable. To improve the vitality of the created ADME dataset, PCA, a biplot graph, and correlation analysis were carried out. A large dataset of naturally occurring antidiabetic compounds was used to predict the drug-likeness of ML models that were effectively deployed on heterogeneous ADME datasets. Among all these ML models, DT performed better than the rest of the models.

The metabolism of soy isoflavones by gut microbiota is critical for the bioactivation and bioavailability of these compounds, particularly daidzein, which is further metabolized by gut bacteria to produce S-equol. S-equol, an exclusive gut bacterial metabolite, is associated with health benefits such as reduced blood pressure, cardiovascular disease prevention, and protection against hormone-related cancers due to its estrogen-mimicking structure and antioxidant properties. However, the limited availability of S-equol-producing bacteria has hindered its production and utilization. This study investigates the isolation and characterization of S-equol-producing microbes from albino Wistar rats and explores the impact of dietary interventions on S-equol production. Preliminary tests showed that both dietary groups excreted more S-equol in feces than urine, with rats on fermented soy feed showing higher S-equol levels due to the presence of daidzein, a precursor. In this study, we isolated four anaerobic S-equol-producing bacteria — MG1 (PX459562), MG2 (PX459563), MG3 (PX459564), and MG4 (PX459565) from the intestine and feces of albino Wistar rats. High-Performance Thin-Layer Chromatography (HPTLC) and High-Performance Liquid Chromatography (HPLC) confirmed the presence of S-equol, with concentrations ranging from 5.90 to 7.56 µg/g of fermented soybean across different strains. Phylogenetic analysis revealed that the isolates belonged to the Enterobacteriaceae and Enterococcaceae families, identifying MG1 as C. freundii strain ATCC 8090, MG2 as Escherichia fergusonii strain NBRC 102419, and both MG3 and MG4 as Enterococcus faecalis strain NBRC 100480. Our findings underscore the significant role of gut microbiota in metabolizing daidzein into S-equol, highlighting the potential for utilizing these bacterial strains in functional food development and therapeutic applications. While the pathogenic nature of E. fergusonii (MG2) precludes its therapeutic use, strains MG1, MG3, and MG4, which match common commensal bacteria, show promise for commercial S-equol production and may serve as valuable resources for further investigation and utilization in promoting health and preventing associated diseases. Key points: • Dietary intervention modulates gut microbiota in albino Wistar rats. • Soybean fermentation enables efficient conversion of daidzin to bioactive S-equol. • Novel S-equol–producing microbes were isolated and identified. Graphical