EPR investigations of model neuromelanins

The aim of this work was to compare free radical properties of some model neuromelanins obtained from dopamine and its mixture with 5-S-cysteinyldopamine in various molar ratios. The EPR method was used in these investigations; the samples were detected at X and K bands. The parameters of EPR spectra, free radicals concentrations in air and in vacuum, the influence of microwave power and temperature on intensity of resonance signals were measured

Eisosome proteins assemble into a membrane scaffold

Membrane organization by eisosomes is mediated by self-assembly of its main components into a membrane-bound protein scaffold with lipid-binding specificity

Physical activity levels as a quantifier in police officers and cadets

Objectives: The aim of the present study was to determine the physical activity levels of active duty police officers and police academy cadets in different life domains and intensities. These parameters were treated as potential quantifiers that could be used when assessing individuals preparing for work as future police officers. Material and Methods: The study recruited 153 active police officers and 176 cadets attending a police academy and administered a diagnostic survey, the long-form version of the International Physical Activity Questionnaire, while in the statistical analysis the Student's t-test for independent groups was applied. Results: It was determined that police officers present high physical activity levels within the work domain, which are developed from initial training at a police academy and then throughout their police career. Conclusions: Such data are important in the light of the role police officers play in public safety as well as the prominence of physical activity within a particular profession and how it can be targeted and tailored to their needs

A microfluidic device with fluorimetric detection for intracellular components analysis

An integrated microfluidic system that coupled lysis of two cell lines: L929 fibroblasts and A549 epithelial cells, with fluorescence-based enzyme assay was developed to determine β-glucocerebrosidase activity. The microdevice fabricated in poly(dimethylsiloxane) consists of three main parts: a chemical cell lysis zone based on the sheath flow geometry, a micromeander and an optical fibers detection zone. Unlike many methods described in literature that are designed to analyse intracellular components, the presented system enables to perform enzyme assays just after cell lysis process. It reduces the effect of proteases released in lysis process on determined enzymes. Glucocerebrosidase activity, the diagnostic marker for Gaucher’s disease, is the most commonly measured in leukocytes and fibroblasts using 4-methylumbelliferyl-β-D-glucopyranoside as synthetic β-glucoside. The enzyme cleavage releases the fluorescent product, i.e. 4-methylumbelliferone, and its fluorescence is measured as a function of time. The method of enzyme activity determination described in this paper was adapted for flow measurements in the microdevice. The curve of the enzymatic reaction advancement was prepared for three reaction times obtained from application of different flow rates of solutions introduced to the microsystem. Afterwards, determined β-glucocerebrosidase activity was recalculated with regard to 105 cells present in samples used for the tests. The obtained results were compared with a cuvette-based measurements. The lysosomal β-glucosidase activities determined in the microsystem were in good correlation with the values determined during macro-scale measurements

The role of the electrocardiographic phenotype in risk stratification for sudden cardiac death in childhood hypertrophic cardiomyopathy.

AIMS: The 12-lead electrocardiogram (ECG) is routinely performed in children with hypertrophic cardiomyopathy (HCM). An ECG risk score has been suggested as a useful tool for risk stratification, but this has not been independently validated. This aim of this study was to describe the ECG phenotype of childhood HCM in a large, international, multi-centre cohort and investigate its role in risk prediction for arrhythmic events. METHODS AND RESULTS: Data from 356 childhood HCM patients with a mean age of 10.1 years (±4.5) were collected from a retrospective, multi-centre international cohort. Three hundred and forty-seven (97.5%) patients had ECG abnormalities at baseline, most commonly repolarization abnormalities (n = 277, 77.8%); left ventricular hypertrophy (n = 240, 67.7%); abnormal QRS axis (n = 126, 35.4%); or QT prolongation (n = 131, 36.8%). Over a median follow-up of 3.9 years (interquartile range 2.0-7.7), 25 (7%) had an arrhythmic event, with an overall annual event rate of 1.38 (95% CI 0.93-2.04). No ECG variables were associated with 5-year arrhythmic event on univariable or multivariable analysis. The ECG risk score threshold of >5 had modest discriminatory ability [C-index 0.60 (95% CI 0.484-0.715)], with corresponding negative and positive predictive values of 96.7% and 6.7. CONCLUSION: In a large, international, multi-centre cohort of childhood HCM, ECG abnormalities were common and varied. No ECG characteristic, either in isolation or combined in the previously described ECG risk score, was associated with 5-year sudden cardiac death risk. This suggests that the role of baseline ECG phenotype in improving risk stratification in childhood HCM is limited

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping