Generation of too many reactive oxygen species (ROS) in relation to available antioxidants in living cells can cause oxidative stress, which is involved in the development and progression of several serious diseases. 2',7'-Dichlorodihydrofluorescein (DCFH) and its diacetate form, DCFH-DA, are widely used probes for monitoring general oxidative stress in cells, but DCFH oxidation is not always related to ROS. We report here a new method for quantifying cellular oxidative stress using a 2,2,6,6-tetramethyl- piperidine-1-oxyl (TEMPO)-based probe. We tested and verified the probe both in cell-free solutions and in living cells under conditions of increased or reduced oxidative stress. The probe revealed the oxidative stress status in living cells and may be a useful complement to DCFH fluorescent probes.

Objectives. This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods. This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), k -nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results. Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions. Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.