Association of body composition with neuroimaging biomarkers and cognitive function; a population-based study of 70-year-olds.

Background: A better understanding of body-brain links may provide insights on targets for preventing cognitive decline. The aim was to explore associations of body composition with neuroimaging biomarkers and cognitive function among dementia-free 70-year-olds.

Methods: Dual-energy X-ray absorptiometry body composition measures in relation to neuroimaging measures of cortical thickness, hippocampal volume, small vessel disease, predicted brain age, and cognitive performance were explored in a cross-sectional study of 674 dementia-free 70-year-olds from the Swedish Gothenburg H70 Birth Cohort study.

Biological brain age and resilience in cognitively unimpaired 70-year-old individuals.

Introduction: This study investigated the associations of brain age gap (BAG)-a biological marker of brain resilience-with life exposures, neuroimaging measures, biological processes, and cognitive function.

Methods: We derived BAG by subtracting predicted brain age from chronological age in 739 septuagenarians without dementia or neurological disorders. Robust linear regression models assessed BAG associations with life exposures, plasma inflammatory and metabolic biomarkers, magnetic resonance imaging, and cerebrospinal fluid biomarkers of neurodegeneration and vascular brain injury, and cognitive performance.

Severe hypereosinophilia in a patient treated with dupilumab and shift to mepolizumab: the importance of multidisciplinary management. A case report and literature review.

Type 2 inflammation is a heterogeneous condition due to the complex activation of different immunological pathways. Rapid progress in research to evaluate the efficacy of biologics for chronic rhinosinusitis with nasal polyps and asthma has led to the availability of effective therapeutic options. These drugs are safe, but temporary iatrogenic hypereosinophilia may sometimes be associated with clinical symptoms or organ damage.

Tezepelumab: patient selection and place in therapy in severe asthma.

Asthma is a disease characterised by heterogeneous and multifaceted airway inflammation. Despite the availability of effective treatments, a substantial percentage of patients with the type 2 (T2)-high, but mainly the T2-low, phenotype complain of persistent symptoms, airflow limitation, and poor response to treatments. Currently available biologicals target T2 cytokines, but no monoclonal antibodies or other specific therapeutic options are available for non-T2 asthma.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction.