Background: Lung cancer is the second most common cancer worldwide. Eucalyptus plant extract has been shown to have anti-neoplastic effects. We investigated the antitumor effect of ethanolic and aqueous extracts of Eucalyptus camaldulensis collected at different altitudes on A549. In addition, we evaluated the additive effect of its combination with low-dose cisplatin (CDDP). Methods: Qualitative and quantitative analyses of secondary metabolites present in the plants were carried out. The antioxidant and cytotoxic activities of the different extracts on A549 were evaluated using the 2.2-diphenyl-1-picrylhydrazyl radical scavenging activity and neutral red assay, respectively. The cytotoxic effect of the combination of certain extract concentrations with low-dose CDDP on A549 cells was studied. Results: In the Ethanoic extract, a higher number of active substances and antioxidant activities were observed. The four E. camaldulensis extracts showed cytotoxic activity on A549 cells, with a higher cytotoxicity for the Ethanoic extract and the sea-level altitude species. Moreover, the dual exposure of cells to both E. camaldulensis extracts and a low dose of Cisplatin showed an additional cytotoxic effect on A549 cells in certain concentrations. Conclusions: This study opens novel therapeutic options in combinational therapies of Eucalyptus camaldulensis with low-dose CDDP for the treatment of adenocarcinoma cells of human lungs.

Ultrasound (US) remains the main modality for the differential diagnosis of changes revealed by mammography. However, the US images themselves are subject to various types of noise and artifacts from reflections, which can worsen the quality of their analysis. Deep learning methods have a number of disadvantages, including the often insufficient substantiation of the model, and the complexity of collecting a representative training database. Therefore, it is necessary to develop effective algorithms for the segmentation, classification, and analysis of US images. The aim of the work is to develop a method for the automated detection of pathological lesions in breast US images and their segmentation. A method is proposed that includes two stages of video image processing: (1) searching for a region of interest using a random forest classifier, which classifies normal tissues, (2) selecting the contour of the lesion based on the difference in brightness of image pixels. The test set included 52 ultrasound videos which contained histologically proven suspicious lesions. The average frequency of lesion detection per frame was 91.89%, and the average accuracy of contour selection according to the IoU metric was 0.871. The proposed method can be used to segment a suspicious lesion.