Advanced glycation end products and breast cancer risk in a sample of the ORDET cohort.

Introduction: Breast cancer is the most common cancer among women, and metabolic syndrome (MetS) is a risk factor for breast cancer, especially postmenopausal breast cancer. We evaluated the role of the advanced glycated end products (AGEs) levels contributing to the association between MetS and breast cancer risk.

Methods: Plasma AGEs were measured in a case-control study nested within the Hormones and Diet in the Etiology of Breast Tumors (ORDET) cohort, including 40 incident postmenopausal breast cancer cases (20 with MetS and 20 without) and 40 postmenopausal controls (20 with MetS and 20 without).

Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population.

Background: Understanding the risks and effects of gestational weight gain (GWG) is a prominent area of perinatal research but approaches for quantifying GWG are evolving and remain underdeveloped, especially in clinical settings for underserved demographic subgroups. To fill this gap, we demonstrated and compared six GWG metrics across pre-pregnancy BMI classifications: total GWG, trimester-specific linear rate of GWG, adherence to total and trimester-specific recommendations, area under the curve, and GWG for gestational age z-scores.

Methods: We used clinical data on 44,801 pregnant people from community-based health care organizations with extensive longitudinal measures and substantial representation of understudied subgroups.

Predicting cell morphological responses to perturbations using generative modeling.

Advancements in high-throughput screenings enable the exploration of rich phenotypic readouts through high-content microscopy, expediting the development of phenotype-based drug discovery. However, analyzing large and complex high-content imaging screenings remains challenging due to incomplete sampling of perturbations and the presence of technical variations between experiments. To tackle these shortcomings, we present IMage Perturbation Autoencoder (IMPA), a generative style-transfer model predicting morphological changes of perturbations across genetic and chemical interventions.

Effects of brexpiprazole on patient life engagement in schizophrenia: analysis of Positive and Negative Syndrome Scale data.

Objective: Patients with schizophrenia value improved life engagement, a term that describes positive health aspects across emotional, physical, social, and cognitive domains. This analysis of clinical trial data aimed to investigate the effect of brexpiprazole on patient life engagement in schizophrenia over the short and long term.

Methods: Data were pooled from three 6-week, randomized, double-blind, placebo-controlled clinical trials (ClinicalTrials.

PI-RADS in Predicting csPCa: A Comparison Between Academic and Nonacademic Centers.

Introduction: The introduction of multiparametric prostate magnetic resonance imaging (mpMRI) has revolutionized prostate cancer (PCa) diagnosis, enhancing the localization of clinically significant prostate cancer (csPCa) and guiding targeted biopsies. However, significant disparities in the execution, interpretation, and reporting of prostate MRI examinations across centers necessitate greater standardization and accuracy. This study compares the diagnostic efficacy of mpMRI from academic and nonacademic centers in detecting csPCa and identifies factors associated with csPCa detection.