Long-Term Safety of Ixekizumab Treatment in Patients with Psoriasis, Psoriatic Arthritis, or Axial Spondyloarthritis: a Post Hoc Analysis of Cerebro-Cardiovascular Events.

Introduction: Psoriasis (PsO), psoriatic arthritis (PsA), and axial spondyloarthritis (axSpA) may confer an increased risk for cardiovascular (CV) disease, including major adverse cerebro-cardiovascular events (MACE), deep vein thrombosis (DVT), and pulmonary embolism (PE). Patients with these conditions are often exposed for extended time periods to biologics, such as ixekizumab (IXE). Therefore, understanding the risk of CV events, especially MACE, in patients with PsO, PsA, and axSpA exposed to IXE is important.

Controlling vaccine kinetics using tannic acid for enhanced humoral immunity.

Despite the success of global vaccination campaigns, vaccine access in low-resource settings is an ongoing challenge. Subunit vaccines are a well-established and clinically scalable intervention, yet they have achieved limited success for poorly immunogenic antigens such as those associated with SARS-CoV-2. Delivery strategies that promote gradual release of subunit vaccines from the injection site offer the potential to improve humoral immunity by enhancing lymph node exposure, however, clinical implementation of this strategy is challenging due to poor scalability and high costs.

The association between obesity and efficacy of psoriasis therapies: An expert consensus panel.

Background: Psoriasis is a chronic inflammatory skin disease often associated with obesity. Psoriasis therapies may be less effective in patients with obese. The purpose of this expert consensus panel is to evaluate the relationship between obesity and efficacy of psoriasis therapies, thereby optimizing patient care.

Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.

Brain imaging is a high-content modality that offers dense insights into the structure and pathology of the brain. Existing genetic association studies of brain imaging, typically focusing on a number of individual image-derived phenotypes (IDPs), have successfully identified many genetic loci. Previously, we have created a 128-dimensional Unsupervised Deep learning derived Imaging Phenotypes (UDIPs), and identified multiple loci from single-phenotype genome-wide association studies (GWAS) for individual UDIP dimensions, using data from the UK Biobank (UKB).