Genetic and functional insights into the fractal structure of the heart.

The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a remnant of embryonic development1,2. The function of these trabeculae in adults and their genetic architecture are unknown. Here we performed a genome-wide association study to investigate image-derived phenotypes of trabeculae using the fractal analysis of trabecular morphology in 18,096 participants of the UK Biobank. We identified 16 significant loci that contain genes associated with haemodynamic phenotypes and regulation of cytoskeletal arborization3,4. Using biomechanical simulations and observational data from human participants, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Through genetic association studies with cardiac disease phenotypes and Mendelian randomization, we find a causal relationship between trabecular morphology and risk of cardiovascular disease. These findings suggest a previously unknown role for myocardial trabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity and reveal the influence of the myocardial trabeculae on susceptibility to cardiovascular disease.

How to extract clinically useful information from large amount of dialysis related stored data.

The basic storage infrastructure used to gather data from the technological evolution also in the healthcare field was leading to the storing into public or private repository of even higher quantities of data related to patients and their pathological evolution. Big data techniques are spreading also in medical research. By these techniques is possible extract information from complex heterogeneous sources, realizing longitudinal studies focused to correlate the patient status with biometric parameters. In our work we develop a common data infrastructure involving 4 clinical dialysis centers between Lombardy and Switzerland. The common platform has been build to store large amount of clinical data related to 716 dialysis session of 70 patient. The platform is made up by a combination of a MySQL(®) database (Dialysis Database) and a MATLAB-based mining library (Dialysis MATlib). A statistical analysis of these data has been performed on the data gathered. These analyses led to the development of two clinical indexes, representing an example of transformation of big data into clinical information.