Mouse Model to Explore the Therapeutic Effect of Nano-Doxorubicin Drug Delivery System on Bladder Cancer.

To study the therapeutic effect of nano-dosin-loaded drug system in mouse bladder cancer, a luciferase-labeled mouse bladder cancer cell line and a mouse bladder cancer model were constructed. In vivo imaging monitors tumor growth and uses a combination of photothermal, immune, and chemotherapy to treat the mouse model. With doxorubicin as an antitumor drug carrier, the drug loading, in vitro drug release, cytotoxicity and behavior in cells of mesoporous nano particle-targeted drug delivery system were studied. The cells were injected into the bladder through the urethra, and the mouse bladder cancer subcutaneous model was treated with gelatin-coated single-walled carbon nanotube-encapsulated mouse granulocytes-macrophage colony-stimulating factor and doxorubicin. In the process of using, the use of near-infrared light for irradiation, thereby achieving the combined effect of photothermal, immune and chemotherapy. The experimental results show that the prepared doxorubicin prodrug delivery system can enhance the targeted therapeutic effect and reduce the toxicity and side effects of the drug. Especially for those cancer cells or tissues with overexpression of folate receptors, it has a better therapeutic effect and provides reference for the treatment of subsequent bladder cancer.

Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.

BACKGROUND AND OBJECTIVE: Among all malignant tumors, lung cancer ranks in the top in mortality rate. Pulmonary nodule is the early manifestation of lung cancer, and plays an important role in its discovery, diagnosis and treatment. The technology of medical imaging has encountered a rapid development in recent years, thus the amount of pulmonary nodules can be discovered are on the raise, which means even tiny or minor changes in lung can be recorded by the CT images. This paper proposes a pulmonary nodule computer aided diagnosis (CAD) based on semi-supervised extreme learning machine(SS-ELM). METHODS: First, the feature model based on the pulmonary nodules regions of lung CT images is established. After that, the same feature data sets have been put into ELM, support vector machine (SVM) methods, probabilistic neural network (PNN) and multilayer perceptron (MLP) so as to compare the performance of the methods. ELM turned out to have better performance in training time and testing accuracy compared with SVM, PNN and MLP. Then, we propose a pulmonary nodules computer aided diagnosis algorithm based on semi-supervised ELM (SS-ELM), which enables both certain class feature sets with labels and unlabeled feature sets to be input for training and computer aided diagnosing. This algorithm has provided a solution for the using of uncertain class data and improve the testing accuracy of benign and malignant diagnosis. RESULTS: 1018 sets of thoracic CT images from the Lung Database Consortium and Image Database Resource Initiative (LIDC-IDRI) have been used in experiment in order to test the effectiveness of the algorithm. Compared with ELM, the pulmonary nodules CAD based on SS-ELM has better testing accuracy performance. CONCLUSIONS: We have proposed a pulmonary nodule CAD system based on SS-ELM, which achieving better generalization performance at faster learning speed and higher testing accuracy than ELM, SVM, PNN and MLP. The SS-ELM based pulmonary nodules CAD has been proposed to solve the problem of uncertain class data using.