Process development for isolation of dietary eugenol from leaves of basil (Ocimum sanctum) in combination of optimization of process variables and modeling by artificial neural network

An Update on the Therapeutic Anticancer Potential of Ocimum sanctum L.: "Elixir of Life"

In most cases, cancer develops due to abnormal cell growth and subsequent tumour formation. Due to significant constraints with current treatments, natural compounds are being explored as potential alternatives. There are now around 30 natural compounds under clinical trials for the treatment of cancer. Tulsi, or Holy Basil, of the genus Ocimum, is one of the most widely available and cost-effective medicinal plants. In India, the tulsi plant has deep religious and medicinal significance. Tulsi essential oil contains a valuable source of bioactive compounds, such as camphor, eucalyptol, eugenol, alpha-bisabolene, beta-bisabolene, and beta-caryophyllene. These compounds are proposed to be responsible for the antimicrobial properties of the leaf extracts. The anticancer effects of tulsi (Ocimum sanctum L.) have earned it the title of "queen of herbs" and "Elixir of Life" in Ayurvedic treatment. Tulsi leaves, which have high concentrations of eugenol, have been shown to have anticancer properties. In a various cancers, eugenol exerts its antitumour effects through a number of different mechanisms. In light of this, the current review focuses on the anticancer benefits of tulsi and its primary phytoconstituent, eugenol, as apotential therapeutic agent against a wide range of cancer types. In recent years, tulsi has gained popularity due to its anticancer properties. In ongoing clinical trials, a number of tulsi plant compounds are being evaluated for their potential anticancer effects. This article discusses anticancer, chemopreventive, and antioxidant effects of tulsi.

Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression

The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (T = 12-30 °C and aw = 0.75-0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted aw, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (R2 = 0.978; RMSE = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems.