Carbon dot unravels accumulation of triterpenoid in Evolvulus alsinoides hairy roots culture by stimulating growth, redox reactions and ANN machine learning model prediction of metabolic stress response.

Evolvulus alsinoides, a therapeutically valuable shrub can provide consistent supply of secondary metabolites (SM) with pharmaceutical significance. Nonetheless, because of its short life cycle, fresh plant material for research and medicinal diagnostics is severely scarce throughout the year. The effects of exogenous carbon quantum dot (CD) application on metabolic profiles, machine learning (ML) prediction of metabolic stress response, and SM yields in hairy root cultures of E. alsinoides were investigated and quantified. The range of the particle size distribution of the CDs was between 3 and 7 nm. The CDs EPR signal and spin trapping experiments demonstrated the formation of O2-•spin-adducts at (g = 2.0023). Carbon dot treatment increased the levels of hydrogen peroxide and malondialdehyde concentrations as well as increased antioxidant enzyme activity. CD treatments (6 µg mL-1) significantly enhanced the accumulation of squalene and stigmasterol (7 and 5-fold respectively). The multilayer perceptron (MLP) algorithm demonstrated remarkable prediction accuracy (MSE value = 1.99E-03 and R2 = 0.99939) in both the training and testing sets for modelling. Based on the prediction, the maximum oxidative stress index and enzymatic activities were highest in the medium supplemented with 10 µg mL-1 CDs. The outcome of this study indicated that, for the first time, using CD could serve as a novel elicitor for the production of valuable SM. MLP may also be used as a forward-thinking tool to optimize and predict SM with high pharmaceutical significance. This study would be a touchstone for understanding the use of ML and luminescent nanomaterials in the production and commercialization of important SM.

Deciphering the Potential Therapeutic Effects of Hydnocarpus wightianus Seed Extracts using in vitro and in silico approaches.

Phytocompounds possess the potential to treat a broad spectrum of disorders due to their remarkable bioactivity. Naturally occurring compounds possess lower toxicity profiles, which making them attractive targets for drug development. Hydnocarpus wightianus seeds were extracted using ethanol, acetone, and hexane solvents. The extracts were evaluated for phytochemicals screening and other therapeutic characteristics, such as free radicals scavenging, anti α-amylase, anti α-glucosidase, and anti-bacterial activities. The ethanolic extract exhibited noteworthy antibacterial characteristics and demonstrated considerable antioxidant, and anti-diabetic effects. The IC50 value of the ethanolic extract for Dpph, α-amylase, and α-glucosidase were found to be 77.299 ± 3.381 µg/mL, 165.56 2.56 µg/mL, and 136.58 ± 5.82 µg/mL, respectively. The ethanolic extract was effective against Methicillin resistant Staphylococcus aureus (26 mm zone of inhibition at 100 µL concentration). Molecular docking investigations revealed the phytoconstituent's inhibitory mechanisms against diabetic, free radicals, and bacterial activity. Docking score for phytocompounds against targeted protein varies from -7.2 to -5.1 kcal/mol. The bioactive compounds present in the ethanolic extract were identified by Gas chromatography/Mass spectrometry analysis, followed by molecular docking and molecular dynamic simulation studies to further explore the phytoconstituent's inhibitory mechanism of α-glucosidase, ∝-amylase, radical scavenging, and bacterial activity. The electronic structure and possible pharmacological actions of the phytocompound were revealed through the use of Density Functional Theory (DFT) analysis. Computational and in vitro studies revealed that these identified compounds have anti-diabetic, anti-oxidant, and anti-bacterial activities against antibiotic-resistant strain of Staphylococcus aureus.