Phytochemical constituents and antioxidant activity of various fractions of Guazuma tomentosa root heartwood

Guazuma tomentosa is an important medicinal plant. The present investigation deals with GC-MS analysis of pet.ether, dichloromethane and ethyl acetate fractions of root heartwood of  G. tomentosa. In antioxidant activity of these fractions by employing DPPH free radical scavenging effect and FRAP total reduction capability method dichloromethane fraction was most effective exhibiting activity nearly equivalent to that of ascorbic acid (standard) at higher concentration, which could be attributed to the phenolic constituents in this fraction. Results indicated that dichloromethane fraction can be a potential source of natural antioxidant agents

Western blot analysis of chloroplast, embryoplast and cytoplasm using sucrose synthase antibodies [abstract]

Abstract only availablePlastids are functionally and structurally diverse organelles and include chloroplasts (found in leaves), leucoplasts (roots), chromoplasts (flower petals), and amyloplasts (tubers). Plant embryos also contain plastids and those present in oilseeds such as rapeseed (Brassica napus) have properties of both chloroplasts and leucoplasts, and are therefore termed embryoplasts. After isolation of plastids from developing embryos of oilseed rape (Brassica napus cv. Reston), embryoplast proteins were identified by liquid chromatography-mass spectrometry. One of the proteins identified was sucrose synthase, a sucrose cleaving enzyme principally located in the cytosol. To confirm that sucrose synthase is associated with isolated embryoplasts, we performed western blots on the protein using four different sucrose synthase antibodies. The western blot results will be presented

24-Ethyl-cholest-22-en-3ɑ-ol and other constituents from the roots of Holoptelea integrifolia

190-19

Saponins from the roots of Mimosa hamata Willd.

61-6

Manganese dioxide oxidation of 18- acetoxy-12, 19-dihydroxy geranyl nerol

1044-104

A novel synthesis of spiro[5H-1,4-diazepine-5,3'-[3H]indol]-2'(1'H)-ones and spiro[2H-1,5-benzodiazepine-2,3'-[3H]indol]-2'(1'H)-ones

1005-100

The response of Asterochloris erici (Ahmadjian) Skaloud et Peksa to desiccation: a proteomic approach

18 p.The study of desiccation tolerance of lichens, and of their chlorobionts in particular, has frequently focused on the anti-oxidant system that protects the cell against photo-oxidative stress during dehydration/rehydration cycles. In this study, we used proteomic and transcript analyses to assess the changes associated with desiccation in the isolated phycobiont Aste-rochloris erici. Algae were dried either slowly (5?6 h) or rapidly (<60 min), and rehydrated after 24 h in the desiccated state. To identify proteins that accumulated during the drying or rehydration processes, we employed two-dimensional (2D) difference gel electrophoresis (DIGE) coupled with individual protein identi?cation using trypsin digestion and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Proteomic analyses revealed that desiccation caused an increase in relative abundance of only 11?13 proteins, regard-less of drying rate, involved in glycolysis, cellular protection, cytoskeleton, cell cycle, and targeting and degradation. Tran-scripts of ?ve Hsp90 and two b-tubulin genes accumulated primarily at the end of the dehydration process. In addition, transmission electron microscopy (TEM) images indicate that ultrastructural cell injuries, perhaps resulting from physical or mechanical stress rather than metabolic damage, were more intense after rapid dehydration. This occurred with no major change in the proteome. These results suggest that desiccation tolerance of A. erici is achieved by constitu-tive mechanisms.Ministerio de Ciencia e InnovaciónGeneralitat ValencianaUnited States Department of Agricultur

RFFE – Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus

Diabetes is a category of metabolic disease commonly known as a chronic illness. It causes the body to generate less insulin and raises blood sugar levels, leading to various issues and disrupting the functioning of organs, including the retinal, kidney and nerves. To prevent this, people with chronic illnesses require lifetime access to treatment. As a result, early diabetes detection is essential and might save many lives. Diagnosis of people at high risk of developing diabetes is utilized for preventing the disease in various aspects. This article presents a chronic illness prediction prototype based on a person's risk feature data to provide an early prediction for diabetes with Fuzzy Entropy random vectors that regulate the development of each tree in the Random Forest. The proposed prototype consists of data imputation, data sampling, feature selection, and various techniques to predict the disease, such as Fuzzy Entropy, Synthetic Minority Oversampling Technique (SMOTE), Convolutional Neural Network (CNN) with Stochastic Gradient Descent with Momentum (SGDM), Support Vector Machines (SVM), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). This study uses the existing Pima Indian Diabetes (PID) dataset for diabetic disease prediction. The predictions' true/false positive/negative rate is investigated using the confusion matrix and the receiver operating characteristic area under the curve (ROCAUC). Findings on a PID dataset are compared with machine learning algorithms revealing that the proposed Random Forest Fuzzy Entropy (RFFE) is a valuable approach for diabetes prediction, with an accuracy of 98 percent