Bioelectrical Impedance Analysis of Body Composition in Male Childhood Brain Tumor Survivors.

. Childhood brain tumor survivors (CCSs) are at high risk of developing metabolic syndrome (MetS) and sarcopenia. To date, a tool able to predict any body composition changes or detect them early and increased adiposity (and, therefore, increased likelihood of MetS onset) is still lacking in this population.

TransferGWAS of T1-weighted brain MRI data from UK Biobank.

Genome-wide association studies (GWAS) traditionally analyze single traits, e.g., disease diagnoses or biomarkers.

Copy-number dosage regulates telomere maintenance and disease-associated pathways in neuroblastoma.

Telomere maintenance in neuroblastoma is linked to poor outcome and caused by either telomerase reverse transcriptase (TERT) activation or through alternative lengthening of telomeres (ALT). In contrast to TERT activation, commonly caused by genomic rearrangements or MYCN amplification, ALT is less well understood. Alterations at the ATRX locus are key drivers of ALT but only present in ∼50% of ALT tumors.

Effect of Sport Activity on Uncomplicated Bicuspid Aortic Valve: Long-Term Longitudinal Echocardiographic Study.

Background: The bicuspid aortic valve (BAV) is a congenital heart defect that can lead to certain complications (aortic stenosis, regurgitation, dilatation and endocarditis), the diagnosis and clinical monitoring of which are effectively entrusted to transthoracic echocardiography (TTE). The impact of training on the natural history of the disease remains unclear.

Methods: A retrospective cohort of athletes with uncomplicated BAV aged 18-50 years, who underwent at least 2 TTEs with a minimum follow-up of 5 years, subdivided according to the level of physical activity during follow-up into ''untrained'' and ''trained'', was collected.

Metadata-guided feature disentanglement for functional genomics.

With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions.