Biomarker evaluation and toxicological study of essential oils of Foeniculum vulgare and Calamintha nepeta

The aim of this study was to evaluate the toxicological properties of EOs of F. vulgare and C. nepeta, widespread in Mediterranean agrosilvopastoral systems and often used as food condiments in Alentejo. EOs were obtained from aerial part of plants by hydrodistillation and chemical composition was evaluated by GC-FID. Toxicity of essential oils was evaluated by the estimation of LC50 in brine shrimp and LD50 in mice. Oral toxicity assays were performed in mice. Histological analyses and quantification of biomarkers aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma glutamyltransferase (γ-GT), bilirubin, creatinine and urea were performed for monitoring liver and kidney functions. the EOs of C. nepeta and F. vulgare showed very low toxicity suggesting their potential use as food supplement. Additionally, our studies point out the importance of the integration of C. nepeta and F. vugare in silvopastoral agroforestry systems, contributing to the animal health and profitability of livestock.

Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition

Subtle structural differencescan be observed in the islets of Langer-hans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic(glucose intolerant)situa-tions. This paper proposes a way to automatically segment the islets of Langer-hans region fromthe histological image of rat's pancreas cell and on the basis of some morphological feature extracted from the segmented region the images are classified as normal and pre-diabetic.The experiment is done on a set of 134 images of which 56 are of normal type and the rests 78 are of pre-diabetictype. The work has two stages: primarily,segmentationof theregion of interest (roi)i.e. islets of Langerhansfrom the pancreatic cell and secondly, the extrac-tion of the morphological featuresfrom the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentationof the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM etc.are used for evaluation among which MLP performed the best.