Effect of Digoxin vs Beta-Blockers on Left Atrial Strain for Heart Rate-Controlled Atrial Fibrillation: The DIGOBET-AF Randomized Clinical Trial.

Unlabelled: BACKGROUND AND OBJECTIVE: Left atrial strain (LAS) has prognostic value in patients with atrial fibrillation (AF). Consequently, therapies that improve LAS may help reduce AF-related adverse cardiac events. We aimed to compare how digoxin and bisoprolol modulate LAS in patients with AF being treated with rate control.

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determinants of tumor entities and sub-entities. This is a task where artificial intelligence (AI)/machine learning (ML) systems could significantly contribute to help with tumor outcome prediction and the search for new diagnostic or treatment stratification biomarkers. AI systems are increasingly integrated into routine pathology workflows to improve accuracy, reproducibility, productivity and to reveal difficult-to-see features in complicated histological slides, including the quantification of important markers for tumor grading and staging.

Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models.

Sensitivity analyses are important components of physiologically based pharmacokinetic (PBPK) model development and are required by regulatory agencies for PBPK submissions. They assess the impact of parametric uncertainty and variability on model estimates, aid model optimization by identifying parameters requiring calibration, and enable the testing of assumptions within PBPK models. One-at-a-time (OAT) sensitivity analyses quantify the impact on a model output in response to changes in a single parameter while holding others fixed.

Development and validation of PBPK models for genistein and daidzein for use in a next-generation risk assessment.

Introduction: All cosmetic ingredients must be evaluated for their safety to consumers. In the absence of data, systemic concentrations of ingredients can be predicted using Physiologically based Pharmacokinetic (PBPK) models. However, more examples are needed to demonstrate how they can be validated and applied in Next-Generation Risk Assessments (NGRA) of cosmetic ingredients.