RESUMO
BACKGROUND: To evaluate the chemosensitivity to doxorubicin (DOX) in two primary cells derived from a tumor of FVB/N-Trp53tm1Hw1 knockout (KO) mice with TALEN-mediated Trp53 mutant gene, we evaluated the cell survivability, cell cycle distribution, apoptotic cell numbers and apoptotic protein expression in solid tumor cells and ascetic tumor cells treated with DOX. RESULTS: The primary tumor cells showed a significant (P < 0.05) defect for UV-induced upregulation of the Trp53 protein, and consisted of different ratios of leukocytes, fibroblasts, epithelial cells and mesenchymal cells. The IC50 level to DOX was lower in both primary cells (IC50 = 0.12 µM and 0.20 µM) as compared to the CT26 cells (IC50 = 0.32 µM), although the solid tumor was more sensitive. Also, the number of cells arrested at the G0/G1 stage was significantly decreased (24.7-23.1% in primary tumor cells treated with DOX, P < 0.05) while arrest at the G2 stage was enhanced to 296.8-254.3% in DOX-treated primary tumor cells compared with DOX-treated CT26 cells. Furthermore, apoptotic cells of early and late stage were greatly increased in the two primary cell-lines treated with DOX when compared to same conditions for CT26 cells. However, the Bax/Bcl-2 expression level was maintained constant in the primary tumor and CT26 cells. CONCLUSIONS: To the best of our knowledge, these results are the first to successfully detect an alteration in chemosensitivity to DOX in solid tumor cells and ascetic tumor cells derived from tumor of FVB/N-Trp53tm1Hw1 mice TALEN-mediated Trp53 mutant gene.
RESUMO
BACKGROUND AND OBJECTIVE: Cancer grading in pathology image analysis is a major task due to its importance in patient care, treatment, and management. The recent developments in artificial neural networks for computational pathology have demonstrated great potential to improve the accuracy and quality of cancer diagnosis. These improvements are generally ascribable to the advance in the architecture of the networks, often leading to increase in the computation and resources. In this work, we propose an efficient convolutional neural network that is designed to conduct multi-class cancer classification in an accurate and robust manner via metric learning. METHODS: We propose a centroid-aware metric learning network for an improved cancer grading in pathology images. The proposed network utilizes centroids of different classes within the feature embedding space to optimize the relative distances between pathology images, which manifest the innate similarities/dissimilarities between them. For improved optimization, we introduce a new loss function and a training strategy that are tailored to the proposed network and metric learning. RESULTS: We evaluated the proposed approach on multiple datasets of colorectal and gastric cancers. For the colorectal cancer, two different datasets were employed that were collected from different acquisition settings. the proposed method achieved an accuracy, F1-score, quadratic weighted kappa of 88.7%, 0.849, and 0.946 for the first dataset and 83.3%, 0.764, and 0.907 for the second dataset, respectively. For the gastric cancer, the proposed method obtained an accuracy of 85.9%, F1-score of 0.793, and quadratic weighted kappa of 0.939. We also found that the proposed method outperforms other competing models and is computationally efficient. CONCLUSIONS: The experimental results demonstrate that the prediction results by the proposed network are both accurate and reliable. The proposed network not only outperformed other related methods in cancer classification but also achieved superior computational efficiency during training and inference. The future study will entail further development of the proposed method and the application of the method to other problems and domains.