The Interplay of Nutrition, the Gut Microbiota and Immunity and Its Contribution to Human Disease.

Nutrition, the gut microbiota and immunity are all important factors in the maintenance of health. However, there is a growing realization of the complex interplay between these elements coalescing in a nutrition-gut microbiota-immunity axis. This regulatory axis is critical for health with disruption being implicated in a broad range of diseases, including autoimmune disorders, allergies and mental health disorders.

Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review.

Background: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic review aimed to synthesise evidence from ML studies predicting CMDs, evaluate their performance, and establish the potential benefit of incorporating lifestyle data in ML models alongside biological and/or demographic-environmental factors.

Methods: This systematic review adheres to the PRISMA statement (Prospero CRD42023401194).

Early antibiotic exposure and risk of psychiatric and neurocognitive outcomes: systematic review and meta-analysis.

Background: The prenatal and early-life periods pose a crucial neurodevelopmental window whereby disruptions to the intestinal microbiota and the developing brain may have adverse impacts. As antibiotics affect the human intestinal microbiome, it follows that early-life antibiotic exposure may be associated with later-life psychiatric or neurocognitive outcomes.

Aims: To explore the association between early-life (in utero and early childhood (age 0-2 years)) antibiotic exposure and the subsequent risk of psychiatric and neurocognitive outcomes.

Clinically Practical Approach for Screening of Low Muscularity Using Electronic Linear Measures on Computed Tomography Images in Critically Ill Patients.

Background: Computed tomography (CT) scans performed during routine hospital care offer the opportunity to quantify skeletal muscle and predict mortality and morbidity in intensive care unit (ICU) patients. Existing methods of muscle cross-sectional area (CSA) quantification require specialized software, training, and time commitment that may not be feasible in a clinical setting. In this article, we explore a new screening method to identify patients with low muscle mass.