Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging.

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning.

Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.

Background: Predicting outcomes of patients with COVID-19 at an early stage is crucial for optimised clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, because of their requirements for extensive data preprocessing and feature engineering, they have not been validated or implemented outside of their original study site. Therefore, we aimed to develop accurate and transferrable predictive models of outcomes on hospital admission for patients with COVID-19.

The structure-function relationship of a signaling-competent, dimeric Reelin fragment.

Reelin operates through canonical and non-canonical pathways that mediate several aspects of brain development and function. Reelin's dimeric central fragment (CF), generated through proteolytic cleavage, is required for the lipoprotein-receptor-dependent canonical pathway activation. Here, we analyze the signaling properties of a variety of Reelin fragments and measure the differential binding affinities of monomeric and dimeric CF fragments to lipoprotein receptors to investigate the mode of canonical signal activation.

Stroke-like migraine attacks after radiation therapy (SMART) syndrome-a case series and review.

Introduction: Cranial irradiation is used both prophylactically and for the treatment of brain tumors. There are various complications associated with it. The rare complication of stroke-like migraine attacks after radiation therapy (SMART) syndrome usually occurs several years after radiation therapy but is a reversible phenomenon.