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.

Background: Insecticides are the chemical substances used to control pest in agriculture and domestic purpose, structural variations among insecticides leads to the change in the safety profile and application, in silico molecular docking studies are used to explain the mechanism of action of chemical substance on the specific target. Principal component analysis is a statistical method used to extract unique features from the large dataset. Methods: We made an investigation to find out correlation on the safety profile of insecticides in terms of acute toxicity, irritant, health hazard, and environmental hazard using principal component analysis (PCA), a dataset was prepared from available literature for 55 insecticides, their safety profile were retrieved from PubChem, PCA was performed on the prepared dataset using excel state software. ADME analysis was performed on the pyrethroids group of compounds using an online server-based docking as well as we had also performed molecular docking on the pyrethroids group of compounds cypermethrin, allethrin, resmethrin, and permethrin insecticides, with voltage-gated sodium ion channel and caspase-3 protein was retrieved from protein data bank PDB ID 2A79, and 1RE1, molecular docking was carried out using Auto Dock vina 4.2, discovery studio and open babel software. Results: Library of 55 insecticidal compounds was prepared their safety profile was obtained and PCA was performed on the dataset which shows that irritant and environmentFal hazard properties of insecticides largely affect the dataset. Server-based ADME analysis showed that cypermethrin is the least toxic compound among different pyrethroids. On the basis of In silico molecular docking permethrin was found to have maximum binding affinity -7.3 kcal/mol followed by cypermethrin, resmethrin, -7.2 kcal/mol, and the least binding affinity was observed with allethrin for 2a79 receptor -5.5 kcal/mol. Molecular docking on caspase-3 enzyme shows that maximum enzyme inhibition was achieved with cypermethrin and resmethrin -7.6 kcal/mol followed by permethrin -6.4and least with allethrin 6.1 kcal/mol. Conclusion: Multivariate PCA showed that the environmental hazard and irritant properties are predominantly present among different classes of insecticides, ADME analysis showed that cypermethrin is the least toxic compound to human as compared to other pyrethroids, and in silico studies showed maximum activity with permethrin for 2a79 receptor and cypermethrin and resmethrin for 1RE1 receptor.