Implementing Findable, Accessible, Interoperable, Reusable (FAIR) Principles in Child and Adolescent Mental Health Research: Mixed Methods Approach.

Background: The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are a guideline to improve the reusability of data. However, properly implementing these principles is challenging due to a wide range of barriers.

Objectives: To further the field of FAIR data, this study aimed to systematically identify barriers regarding implementing the FAIR principles in the area of child and adolescent mental health research, define the most challenging barriers, and provide recommendations for these barriers.

The Effect of Light on Wellbeing: A Systematic Review and Meta-analysis.

Unlabelled: Due to the dominant presence of studies and reviews exploring the impact of light on physical and mental illness, studies specifically investigating the effect of light on wellbeing are often overshadowed. The aim of this review is to give an overview of specifically these studies conducted on light and wellbeing, and to summarize the reported effects. After a literature search in , and , 74 studies were found eligible to be included in this systematic review, i.

Cytoadhesion of Plasmodium falciparum-Infected Red Blood Cells Changes the Expression of Cytokine-, Histone- and Antiviral Protein-Encoding Genes in Brain Endothelial Cells.

Malaria remains a significant global health problem, mainly due to Plasmodium falciparum, which is responsible for most fatal infections. Infected red blood cells (iRBCs) evade spleen clearance by adhering to endothelial cells (ECs), triggering capillary blockage, inflammation, endothelial dysfunction and altered vascular permeability, prompting an endothelial transcriptional response. The iRBC/HBEC-5i model, where iRBCs present IT4var04 (VAR2CSA) on their surface, was used to analyze the effects of iRBC binding on ECs at different temperature (37°C vs.

Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort.

Effective personalized well-being interventions require the ability to predict who will thrive or not, and the understanding of underlying mechanisms. Here, using longitudinal data of a large population cohort (the Netherlands Twin Register, collected 1991-2022), we aim to build machine learning prediction models for adult well-being from the exposome and genome, and identify the most predictive factors ( between 702 and 5874). The specific exposome was captured by parent and self-reports of psychosocial factors from childhood to adulthood, the genome was described by polygenic scores, and the general exposome was captured by linkage of participants' postal codes to objective, registry-based exposures.