Strength of association of classical vascular risk factors in young patients with ischaemic stroke: a case-control study.

Introduction: Recent studies have reported an increasing incidence of ischaemic stroke among young adults. However, the strength of the association between traditional vascular risk factors has not been fully established.

Methods: We compared 120 patients with a first ischaemic stroke before the age of 55 years admitted to the stroke unit of our centre with 600 healthy non-stroke controls from a population-based cohort study (HERMEX), matched for sex.

Increasing Self-Efficacy and Bystander CPR Rates:A Train-the-Trainer Program.

Purpose: Increase the number of community members within underserved areas trained in bystander cardiopulmonary resuscitation (BCPR), increase their self-efficacy levels to perform BCPR, and implement a train-the-trainer program for community leaders.

Design: Quantitative, before-and-after design.

Methods: Participants completed BCPR training with automatic feedback device mannequins and completed pre and post-training surveys to measure self-efficacy levels.

Proteins from pseudocereal seeds: solubility, extraction, and modifications of the physicochemical and techno-functional properties.

Pseudocereals (amaranth, buckwheat and quinoa) are emerging as popular gluten-free crops. This may be attributed to their wide-ranging health benefits, including antioxidant, hypoglycemic and serum-cholesterol reducing properties. Proteins of these crops have a high nutritional quality as a result of the presence of essential amino acids.

NCYPred: A Bidirectional LSTM Network With Attention for Y RNA and Short Non-Coding RNA Classification.

Short non-coding RNAs (sncRNAs) are involved in multiple cellular processes and can be divided into dozens of classes. Among such classes, Y RNAs have been gaining attention, being essential factors for the initiation of DNA replication on vertebrates, as well as potential tumor biomarkers. Homologs have also been described in nematodes and insects, as well as related sequences in bacteria.

Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network.

Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS).