DrivR-Base: a feature extraction toolkit for variant effect prediction model construction.

Motivation: Recent advancements in sequencing technologies have led to the discovery of numerous variants in the human genome. However, understanding their precise roles in diseases remains challenging due to their complex functional mechanisms. Various methodologies have emerged to predict the pathogenic significance of these genetic variants.

The tumor multi-omic landscape of endometrial cancers developed on a germline genetic background of adiposity.

High body mass index (BMI) is a causal risk factor for endometrial cancer but the tumor molecular mechanisms affected by adiposity and their therapeutic relevance remain poorly understood. Here we characterize the tumor multi-omic landscape of endometrial cancers that have developed on a background of lifelong germline genetic exposure to elevated BMI. We built a polygenic score (PGS) for BMI in women using data on independent, genome-wide significant variants associated with adult BMI in 434,794 women.

In what ways might group clinical supervision affect the development of resilience in hospice nurses?

Background: Resilience is important to sustain hospice nurses through a challenging career. Clinical supervision is a commonly cited support strategy, but there is limited evidence which focuses on its influence on the development of resilience in hospice nurses.

Aims: To explore how group clinical supervision might affect the development of resilience in hospice nurses.

Children with autism spectrum disorder and epilepsy.

Autism spectrum disorder (ASD) is a biologically based neurodevelopmental disability characterized by qualitative and persistent deficits in social communication and social interaction and by the presence of restricted, repetitive, and stereotyped patterns of behavior. Symptoms must be present in early childhood and they must limit and impair everyday functioning. There is an increased prevalence of epilepsy and/or epileptiform electroencephalography (EEG) abnormalities in children with ASD.