Radiation-Induced Pneumonitis in the Era of the COVID-19 Pandemic: Artificial Intelligence for Differential Diagnosis.

(1) Aim: To test the performance of a deep learning algorithm in discriminating radiation therapy-related pneumonitis (RP) from COVID-19 pneumonia. (2) Methods: In this retrospective study, we enrolled three groups of subjects: pneumonia-free (control group), COVID-19 pneumonia and RP patients. CT images were analyzed by mean of an artificial intelligence (AI) algorithm based on a novel deep convolutional neural network structure.

Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis.

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis.

Methods: We enrolled three groups: a pneumonia-free group ( = 30), a COVID-19 group ( = 34), and a group of patients with ICI therapy-related pneumonitis ( = 21).

Further Highlighting on the Prevention of Oxidative Damage by Polyphenol-Rich Wine Extracts.

Wine contains various polyphenols such as flavonoids, anthocyanins, and tannins. These molecules are responsible for the quality of wines, influencing their astringency, bitterness, and color and they are considered to have antioxidant activity. Polyphenols, extracted from grapes during the processes of vinification, could protect the body cells against reactive oxygen species level increase and could be useful to rescue several pathologies where oxidative stress represents the main cause.

Protein stability modulated by a conformational effector: effects of trifluoroethanol on bovine serum albumin.

The link between the thermodynamic properties of a solution and the conformational space explored by a protein is of fundamental importance to understand and control solubility, misfolding and aggregation processes. Here, we study the thermodynamic and conformational stability of a model protein, bovine serum albumin (BSA), by addition of trifluoroethanol (TFE), which is known to affect both the solvent properties and the protein structure. The solvent-mediated pair-wise interactions are investigated by static and dynamic light scattering, and by small angle X-ray scattering.