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. The cut-off value of risk probability of COVID-19 was 30%; values higher than 30% were classified as COVID-19 High Risk, and values below 30% as COVID-19 Low Risk. The statistical analysis included the Mann-Whitney U test (significance threshold at p < 0.05) and receiver operating characteristic (ROC) curve, with fitting performed using the maximum likelihood fit of a binormal model. (3) Results: Most patients presenting RP (66.7%) were classified by the algorithm as COVID-19 Low Risk. The algorithm showed high sensitivity but low specificity in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, area under the curve (AUC = 0.72). The specificity increased when an estimated COVID-19 risk probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). (4) Conclusions: The deep learning algorithm was able to discriminate RP from COVID-19 pneumonia, classifying most RP cases as COVID-19 Low Risk.

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 (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann-Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). RESULTS: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). CONCLUSIONS: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

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. For that, in this study, red and white wine, provided by an Italian vinery (Marche region), have been analyzed. Chromatographic and morphofunctional analyses have been carried out for polyphenol extraction and to evaluate their protective effect on human myeloid U937 cells exposed to hydrogen peroxide. Both types of wines contained a mix of phenolic compounds with antioxidant properties and their content decreased, as expected, in white wine. Ultrastructural observations evidenced that wines, in particular red wine, strongly prevent mitochondrial damage and apoptotic cell death. In conclusion, the considered extracts show a relevant polyphenol content with strong antioxidant properties and abilities to prevent apoptosis. These findings suggest, for these compounds, a potential role in all pathological conditions where the body antioxidant system is overwhelmed.

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. The protein conformational details are studied by far- and near-UV circular dichroism (CD), and steady state fluorescence from tryptophan and from 1-anilino-8-naphthalene sulfonate (ANS). At low TFE concentrations, our results show that protein-protein interaction is dominated by steric repulsion accompanied by a consistent protein solvation. Minor local conformational changes also occur, but they do not affect the stability of BSA. At TFE concentrations above the threshold of 16% v/v, attractive interactions become prevalent, along with conformational changes related to a loosening of BSA tertiary structure. The onset of thermodynamic instability is triggered by the enhancement of hydrophobic attraction over repulsion, due to minor local changes of protein conformation and hydration. In the present context, TFE acts as a conformational effector, since it affects the intermolecular interaction and the activity of the proteins in solution through a direct mechanism.