Integration of multi-omics layers empowers precision diagnosis through unveiling pathogenic mechanisms on maple syrup urine disease.

Maple syrup urine disease (MSUD) is a rare inherited metabolic disorder characterized by deficient activity of the branched-chain alpha-ketoacid dehydrogenase (BCKDH) complex, required to metabolize the amino acids leucine, isoleucine, and valine. Despite its profound metabolic implications, the molecular alterations underlying this metabolic impairment had not yet been completely elucidated. We performed a comprehensive multi-omics integration analysis, including genomic, epigenomic, and transcriptomic data from fibroblasts derived from a cohort of MSUD patients and unaffected controls to genetically characterize an MSUD case and to unravel the MSUD pathophysiology.

Putative retina metal/metalloid-binding proteins: molecular functions, biological processes and retina disease associations.

The mammalian retina contains high amounts of metals/metalloid-selenium. Their dyshomeostases are associated with certain retinal diseases. We carried out this bioinformatics study to identify the relationships between putative retinal metal/selenium binding proteins, their molecular functions, and biological processes.

Body composition and resting energy expenditure in a group of children with achondroplasia.

Background: Persons with achondroplasia develop early obesity, which is a comorbidity associated with other complications. Currently, there are no validated specific predictive equations to estimate resting energy expenditure in achondroplasia.

Methods: We analyzed the influence of body composition on this parameter and determined whether predictive models used for children with standard height are adjusted to achondroplasia.

A scalable approach for short-term disease forecasting in high spatial resolution areal data.

Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatiotemporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed "divide-and-conquer" approach.