Nutrition and Growth of Preterm Neonates during Hospitalization: Impact on Childhood Outcomes.

The Special Issue has been completed with the publication of 13 review and research articles [...

Extrauterine Growth Restriction and Optimal Growth of Very Preterm Neonates: State of the Art.

Over the last few decades, there has been an ongoing debate over both the optimal feeding mode for very premature neonates (VPN) as well as what their optimal growth should be. Despite the American Academy of Pediatric declaring since 1997 that the growth of VPN should follow the trajectory of intrauterine fetal growth, differences of opinion persist, feeding policies keep changing, and the growth and development of VPN remains extremely variable not only between countries, but even between neighboring neonatal units. Even the appropriate terminology to express poor postnatal growth (extrauterine growth restriction (EGR) and postnatal growth failure (PGF)) remains a subject of ongoing discussion.

Single Nucleotide Polymorphisms' Causal Structure Robustness within Coronary Artery Disease Patients.

An ever-growing amount of accumulated data has materialized in several scientific fields, due to recent technological progress. New challenges emerge in exploiting these data and utilizing the valuable available information. Causal models are a powerful tool that can be employed towards this aim, by unveiling the structure of causal relationships between different variables.

Are the current feeding volumes adequate for the growth of very preterm neonates?

Postnatal growth failure, a common problem in very preterm neonates associated with adverse neurodevelopmental outcome, has recently been shown not to be inevitable. There is a wide discussion regarding feeding practices of very preterm neonates, specifically regarding feeding volumes and nutrients supply to avoid postnatal growth failure. Current guidelines recommend an energy intake of 115–140 kcal /kg per d with a considerably higher upper limit of 160 kcal/kg per d.

Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning.

Background: Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples.