BACKGROUND AND OBJECTIVE: Radiomics has been widely used in quantitative analysis of medical images for disease diagnosis and prognosis assessment. The objective of this study is to test a machine-learning (ML) method based on radiomics features extracted from chest CT images for screening COVID-19 cases. METHODS: The study is carried out on two groups of patients, including 138 patients with confirmed and 140 patients with suspected COVID-19. We focus on distinguishing pneumonia caused by COVID-19 from the suspected cases by segmentation of whole lung volume and extraction of 86 radiomics features. Followed by feature extraction, nine feature-selection procedures are used to identify valuable features. Then, ten ML classifiers are applied to classify and predict COVID-19 cases. Each ML models is trained and tested using a ten-fold cross-validation method. The predictive performance of each ML model is evaluated using the area under the curve (AUC) and accuracy. RESULTS: The range of accuracy and AUC is from 0.32 (recursive feature elimination [RFE]+Multinomial Naive Bayes [MNB] classifier) to 0.984 (RFE+bagging [BAG], RFE+decision tree [DT] classifiers) and 0.27 (mutual information [MI]+MNB classifier) to 0.997 (RFE+k-nearest neighborhood [KNN] classifier), respectively. There is no direct correlation among the number of the selected features, accuracy, and AUC, however, with changes in the number of the selected features, the accuracy and AUC values will change. Feature selection procedure RFE+BAG classifier and RFE+DT classifier achieve the highest prediction accuracy (accuracy: 0.984), followed by MI+Gaussian Naive Bayes (GNB) and logistic regression (LGR)+DT classifiers (accuracy: 0.976). RFE+KNN classifier as a feature selection procedure achieve the highest AUC (AUC: 0.997), followed by RFE+BAG classifier (AUC: 0.991) and RFE+gradient boosting decision tree (GBDT) classifier (AUC: 0.99). CONCLUSION: This study demonstrates that the ML model based on RFE+KNN classifier achieves the highest performance to differentiate patients with a confirmed infection caused by COVID-19 from the suspected cases.

Background: This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. Results: The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the DenseNet201 model and KNN algorithm have received the best performance with an accuracy of 100%. Created map by t-SNE in the DenseNet201 model showed not any points clustered with the wrong class. Conclusions: The mentioned models can be used in remote places, in low- and middle-income countries, and laboratory equipment with limited resources to overcome a shortage of radiologists.

Background. This study was aimed at determining the effects of alpha-lipoic acid on ionizing irradiation-induced oxidative damage and apoptosis in the brain of rats. Methods. The animals were exposed to whole-brain X-radiation with a 15 Gy single dose in the absence or presence of alpha-lipoic acid (200 mg/kg body weight) pretreatment for one week. The rats were divided into four groups (5 rats in each group): vehicle control, alpha-lipoic acid alone (ALA), radiation alone (RAD), and radiation plus alpha-lipoic acid (RAD+ALA). In the next stage, malondialdehyde (MDA), nitric oxide, catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx) in the brain tissue of the rats were measured. Furthermore, the Western blot analysis technique was performed to assess the NOX2, NOX4, and caspase-3 protein expression levels. Results. Twenty-four hours after the irradiation, MDA and nitric oxide levels in the irradiated rats were significantly higher than those in the control group ( ); however, the pretreatment with alpha-lipoic acid resulted in a significant reduction in these stress oxidative markers ( ). Moreover, a significant decrease in CAT, SOD, and GPx levels was observed in the radiation group alone compared to the control group ( ); in contrast, the activities of these antioxidant enzymes significantly increased in the radiation plus alpha-lipoic acid group in comparison to the radiation group alone ( ). The results of Western blot analysis revealed that NOX2, NOX4, and caspase-3 protein expressions significantly elevated in the irradiated rats compared to the control group ( ). The pretreatment with alpha-lipoic acid could significantly decrease the expression levels of NOX2, NOX4, and caspase-3 in comparison with the radiation group alone ( ). Conclusion. According to the obtained findings, it can be mentioned that the alpha-lipoic acid pretreatment could mitigate the ionizing irradiation-induced oxidative damage and apoptosis in the brain of the rats.