Prediction of chronic severe intestinal failure-associated liver disease by current criteria in adults: A descriptive cohort study.

Introduction: Intestinal failure-associated liver disease covers a spectrum of conditions from mild to end-stage disease. Currently, there are 9 diagnostic criteria divided to four categories: cholestasis, steatosis, fibrosis, and unclassified. Our aim was to evaluate the application of these criteria to patients with chronic severe liver disease in patients with intestinal failure.

Towards Precision Sports Nutrition for Endurance Athletes: A Scoping Review of Application of Omics and Wearables Technologies.

Background: Endurance athletes require tailored nutrition strategies to optimize performance, recovery, and training adaptations. While traditional sports nutrition guidelines provide a foundational framework, individual variability in metabolic responses underscores the need for precision nutrition, informed by genetic, biological, and environmental factors. This scoping review evaluates the application of systems biology-driven sports nutrition for endurance athletes, focusing on 'omics' and wearable technologies.

Case specific: Addressing co-digestion of wastewater sludge, cheese whey and cow manure: Kinetic modeling.

The study investigated the methane production efficiency in a semi-continuous laboratory experiment with periodic feeding of wastewater sludge (WWS) as primary substrate and addition of whey (CW) and cow manure (CM). The short-term behavior of a real-scale anaerobic digester with WWS and the methane production improvements with different feeding mixtures of WWS, CW and CM were addressed. Gradual addition of CW to WWS (WWS:CW:CM = 70:20:0 to 70:55:0) increased the average daily methane production to 48.

Predictive modeling of colorectal cancer using exhaustive analysis of microbiome information layers available from public metagenomic data.

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC,  = 380) and colorectal adenomas (CRA,  = 110) against generally healthy participants ( = 2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline.