Genome-wide association study of varenicline-aided smoking cessation.

Introduction: Varenicline is an α4β2 nicotinic acetylcholine receptor partial agonist with the highest therapeutic efficacy of any pharmacological smoking cessation aid and a 12-month cessation rate of 26%. Genetic variation may be associated with varenicline response, but to date no genome-wide association studies of varenicline response have been published.

Methods: In this study, we investigated the genetic contribution to varenicline effectiveness using two electronic health record-derived phenotypes.

Characterization of Radionuclide Resuspension via Aeolian Processes at a Uranium Mill Tailings Site.

A former uranium recovery facility located in northwestern New Mexico currently serves as a uranium mill tailings site undergoing reclamation and decommissioning. High velocity winds are common in the area, causing soil erosion via aeolian processes. Strong winds may carry soil for several kilometers, which is redeposited downwind.

Genomic determinants of biological age estimated by deep learning applied to retinal images.

With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between chronological age and predicted retinal age has been established previously to predict the age-related disease. In this study, we performed discovery genome-wide association analysis (GWAS) on the RAG using the 31,271 UK Biobank participants and replicated our findings in 8034 GoDARTS participants.

Education Research: The Neurohumanities in Training: Integrating a Humanities Curriculum Within Neurology Residency Programs.

Background And Objectives: Perhaps stemming from the central role of detailed examinations and a focus on the subjective sphere that grounds their clinical practice, neurologists have frequently opined on experiences traditionally a province of humanities. The increasingly technological focus on medical education and care can be seen to devalue the subjective aspects of medicine. As a counter to this, we report on the existence of neurohumanities curricula within neurology residency training.

Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.

Background: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score.

Methods: We included 6127 individuals with type 2 diabetes without myocardial infarction or stroke prior to study entry.