Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions.

The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e.

Cystic Fibrosis and Oxidative Stress: The Role of CFTR.

There is substantial evidence in the literature that patients with cystic fibrosis (CF) have higher oxidative stress than patients with other diseases or healthy subjects. This results in an increase in reactive oxygen species (ROS) and in a deficit of antioxidant molecules and plays a fundamental role in the progression of chronic lung damage. Although it is known that recurrent infection-inflammation cycles in CF patients generate a highly oxidative environment, numerous clinical and preclinical studies suggest that the airways of a patient with CF present an inherently abnormal proinflammatory milieu due to elevated oxidative stress and abnormal lipid metabolism even before they become infected.