Functional and Oncological Outcomes of Very Large Prostate Sizes Post Robotic Radical Prostatectomy: A Propensity Score-Matched Analysis.

Background: Robotic-assisted radical prostatectomy (RARP) is widely used as the main surgical approach to treat prostate cancer in the United States. Prostate size is often described as a factor affecting the outcomes of RARP as shown by many studies. However, these studies are limited to a small number of patients.

Pregnancy and birth complications among women undergoing bariatric surgery: sleeve gastrectomy versus Roux-en-Y gastric bypass.

Background: Metabolic bariatric surgery is the most effective therapy for severe obesity, which affects the health of millions, most of whom are women of child-bearing age. Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) are the most common bariatric procedures and are associated with durable weight loss and comorbidity resolution. Although obstetric outcomes broadly improve, the safety profile comparing the impact of RYGB and SG on obstetric outcomes is underexplored.

Mechanism of enhancing chemotherapy efficacy in pancreatic ductal adenocarcinoma with paricalcitol and hydroxychloroquine.

Pancreatic ductal adenocarcinoma (PDAC) has a minimal (<15%) 5-year existence, in part due to resistance to chemoradiotherapy. Previous research reveals the impact of paricalcitol (P) and hydroxychloroquine (H) on altering the lysosomal fusion, decreasing stromal burden, and triggering PDAC to chemotherapies. This investigation aims to elucidate the molecular properties of the H and P combination and their potential in sensitizing PDAC to gemcitabine (G).

Multiomic molecular patterns of lipid dysregulation in a subphenotype of sepsis with higher shock incidence and mortality.

Background: Lipids play a critical role in defense against sepsis. We sought to investigate gene expression and lipidomic patterns of lipid dysregulation in sepsis.

Methods: Data from four adult sepsis studies were analyzed and findings were investigated in two external datasets.

Predicting adaptive immune receptor specificities by machine learning is a data generation problem.

Determining the specificity of adaptive immune receptors-B cell receptors (BCRs), their secreted form antibodies, and T cell receptors (TCRs)-is critical for understanding immune responses and advancing immunotherapy and drug discovery. Immune receptors exhibit extensive diversity in their variable domains, enabling them to interact with a plethora of antigens. Despite the significant progress made by AI tools such as AlphaFold in predicting protein structures, challenges remain in accurately modeling the structure and specificity of immune receptors, primarily due to the limited availability of high-quality crystal structures and the complexity of immune receptor-antigen interactions.