Evaluation for substitution of stem bark with small branches of Myrica esculenta for medicinal use – A comparative phytochemical study

AbstractBackgroundOver exploitation of many traditional medicinal plants like Myrica esculenta has become a threat and in the near future, many medicinal plants may be unavailable for use of industry.ObjectivePresent study outlines the concept of plant part substitution. Stem bark and small branches of M. esculenta are compared on the basis of physicochemical analysis, phytochemical analysis, total phenolic contents, total flavonoid contents and high performance thin layer chromatography (HPTLC) to evaluate the possibilities of using small branches in place of stem bark.Material and methodsPhysicochemical parameters and preliminary phytochemical screening were carried out using standard methods. Total phenolic and total flavonoid contents were estimated spectrophotometrically using Folin-Ciocalteu and aluminum chloride method, respectively. CAMAG HPTLC system equipped with semi-automatic applicator was used for HPTLC profiling. n-Hexane, ethyl acetate and ethanol extracts of stem bark and small branches were developed in suitable mobile phase using standard procedures and visualized in UV 254 and 366 nm and in white light after derivatization within anisaldehyde-sulphuric acid reagent.ResultsPhytochemical analysis and HPTLC profile of different extracts showed the presence of almost similar phytochemicals in both stem bark and small branches.ConclusionSimilarities in phytochemical analysis and HPTLC profile of various extracts suggests that small branches may be used in place of stem bark. The study provides the base for further study to use small branches as a substitute of stem bark of M. esculenta

A Set of EEG Parameters to Predict Clinically Anesthetized State in Humans for Halothane Anesthesia

This article evaluates all the EEG parameters suggested in the literature that undergo changes due to anaesthetic dose, and suggests a set of EEG parameters that act as best signatures of anaesthetic state of a patient. This set of EEG parameters is validated by an artificial neural network