Antioxidant Capacity and fatty acid profile of Swinglea glutinosa (BLANCO) MERR, cultivated in Cuba

editorial reviewedThe small tropical tree Swinglea glutinosa (Blanco) Merr. is a member of the Rutaceae family. Originally brought to South America from Southeast Asia, it is used as an ornamental plant in Cuba and as a natural barrier in rural areas and gardens. Extracts from this tree have been assessed for cytotoxic and antimalarial activity in previous studies but never been evaluated its antioxidant activity. Material and Methods: The antioxidant capacity of the methanolic extracts and the fatty acid composition of leaves and fruits S. glutinosa (Blanco) Merr was investigated. Six different chemical methods were used to determine the antioxidant capacity. The fatty acid composition was analyzed using gas chromatography. Results: The IC50 value of the extracts was determined being 28.2 g/mL to leaf extract and 10 μg/mL to fruit extract (in the DPPH method. The concentration of the extracts resulted in increased in the ferric reducing antioxidant power to both extracts tested. The amount of total phenolic content was detected as 48.5 mg and 35.9 gallic acid equivalent (GAE)/g in the fruit and the leaf extract, respectively; meanwhile the total antioxidant capacity was 94.93 and 75.3 mg ascorbic acid equivalent (AE)/g for fruits and leaves, respectively. The peroxidation lipid assays (FTC and TBA methods) shows highest antioxidant effect for the leaf extract. Conclusions: The results permit to deduce that the fruit extract has highest anti-radical effect and the leaf extract has highest effect against the lipid peroxidation. The major fatty acid in the composition of Swinglea glutinosa was found to be the ω6 (linoleic) and ω9 (oleic) acids by GC analysis, with 0.5 % for both. This study reveals that Swinglea glutinosa is an attractive source of ω fatty acid components, especially the essential ones, as well as of effective natural antioxidants

Mass spectrometry based metabolomics of volume-restricted in-vivo brain samples: actual status and the way forward

Brain metabolomics is gaining interest because of the aging of the population, resulting in more central nervous system disorders such as Alzheimer's and Parkinson's disease. Most often these diseases are studied in vivo, such as for example by analysing cerebrospinal fluid or brain extracellular fluid. These sample types are often considered in pre-clinical studies using animal models. However, the scarce availability of both matrices results in some challenges related to sampling, sample preparation and normalization. Much effort has been made towards the development of alternative, less invasive sampling techniques for collecting small sample volumes (pL till mid mL range) over the past years. Despite recent advances, the analysis of low volumes is still a tremendous challenge. Therefore, proper pre-concentration and sample pretreatment strategies are necessary together with sensitive analysis and detection techniques suitable for low-volume samples. In this review, an overview is given of the stateof-the-art mass spectrometry-based analytical workflows for probing (endogenous) metabolites in volume-restricted in-vivo brain samples. In this context, special attention is devoted to challenges related to sampling, sample preparation and preconcentration strategies. Finally, some general conclusions and perspectives are provided. (C) 2021 The Author(s). Published by Elsevier B.V.Analytical BioScience

Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c

Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29–39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination

Contains fulltext : 187506.pdf (publisher's version ) (Open Access

Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity

Contains fulltext : 91924.pdf (publisher's version ) (Closed access)14 p

Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs

In general, linear modelling techniques such as multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS), are used to model QSAR data. This type of data can be very complex and linear modelling techniques often model only a limited part of the information captured in the data. In this study, it was tried to combine linear techniques with the flexible non-linear technique multivariate adaptive regression splines (MARS). Models were built using an MLR model, combined with either a stepwise procedure or a genetic algorithm for variable selection, a PCR model or a PLS model as starting points for the MARS algorithm. The descriptive and predictive power of the models was evaluated in a QSAR context and compared to the performances of the individual linear models and the single MARS model.\ud \ud In general, the combined methods resulted in significant improvements compared to the linear models and can be considered valuable techniques in modelling complex QSAR data. For the used data set the best model was obtained using a combination of PLS and MARS. This combination resulted in a model with a Pearson correlation coefficient of 0.90 and a cross-validation error, evaluated with 10-fold cross-validation of 9.9%, pointing at good descriptive and high predictive properties

Strategy for reduced calibration sets to develop quantitative structure-retention relationships in high-performance liquid chromatography

Contains fulltext : 75806.pdf (publisher's version ) (Closed access)9 p