Emotional well-being in transition-age adults with pediatric-onset disabilities: A cross-sectional study.

Purpose/objective: The transition from childhood to adulthood often involves emotional challenges. These problems may be especially prominent for transition-age adults (TAA) with pediatric-onset disabilities, although there are currently few studies that speak to this. The aim of this study is to characterize depressive symptoms and the association with family functioning in a sample of TAA with pediatric-onset disabilities.

Association between meningioma consistency and surgical outcomes.

Objective: Tumor consistency, or fibrosity, affects the ability to optimally resect meningiomas, especially with recent trends evolving toward minimally invasive approaches. The authors' team previously validated a practical 5-point scale for intraoperative grading of meningioma consistency. The impact of meningioma consistency on surgical management and outcomes, however, has yet to be explored.

Unidirectional and bidirectional causation between smoking and blood DNA methylation: evidence from twin-based Mendelian randomisation.

Cigarette smoking is associated with numerous differentially-methylated genomic loci in multiple human tissues. These associations are often assumed to reflect the causal effects of smoking on DNA methylation (DNAm), which may underpin some of the adverse health sequelae of smoking. However, prior causal analyses with Mendelian Randomisation (MR) have found limited support for such effects.

Evaluating the Equivalency of Teletesting and the Impact of the COVID-19 Pandemic on Measures of Reading Achievement in a Clinically Referred Sample.

Objective: The COVID-19 pandemic highlighted the need to build an evidence base to support teletesting as an equivalent modality for standardized neuropsychological assessment. As such, the purpose of this study was twofold. First, this study evaluated teletesting equivalency of standardized reading achievement measures during COVID-19 in children ages 6-16.

Machine Learning Approaches to Predict Alcohol Consumption from Biomarkers in the UK Biobank.

Background: Measuring and estimating alcohol consumption (AC) is important for individual health, public health, and Societal benefits. While self-report and diagnostic interviews are commonly used, incorporating biological-based indices can offer a complementary approach.

Methods: We evaluate machine learning (ML) based predictions of AC using blood and urine-derived biomarkers.