Survival prediction of heart failure patients using motion-based analysis method.

BACKGROUND AND OBJECTIVE: Survival prediction of heart failure patients is critical to improve the prognostic management of the cardiovascular disease. The existing survival prediction methods focus on the clinical information while lacking the cardiac motion information. we propose a motion-based analysis method to predict the survival risk of heart failure patients for aiding clinical diagnosis and treatment. METHODS: We propose a motion-based analysis method for survival prediction of heart failure patients. First, our method proposes the hierarchical spatial-temporal structure to capture the myocardial border. It promotes the model discrimination on border features. Second, our method explores the dense optical flow structure to capture motion fields. It improves the tracking capability on cardiac images. The cardiac motion information is obtained by fusing boundary information and motion fields of cardiac images. Finally, our method proposes the multi-modality deep-cox structure to predict the survival risk of heart failure patients. It improves the survival probability of heart failure patients. RESULTS: The motion-based analysis method is confirmed to be able to improve the survival prediction of heart failure patients. The precision, recall, F1-score, and C-index are 0.8519, 0.8333, 0.8425, and 0.8478, respectively, which is superior to other state-of-the-art methods. CONCLUSIONS: The experimental results show that the proposed model can effectively predict survival risk of heart failure patients. It facilitates the application of robust clinical treatment strategies.

AZD3409 inhibits the growth of breast cancer cells with intrinsic resistance to the EGFR tyrosine kinase inhibitor gefitinib.

AKT and MAPK signaling are involved in the resistance of breast cancer cells to the EGFR tyrosine kinase inhibitor gefitinib. RAS proteins are upstream mediators that transfer messages from surface receptors to intracellular signal transducers including MAPK and AKT pathways. AZD3409 is a novel prenyl inhibitor that has shown activity against both farnesyl transferase and geranylgeranyl transferase in isolated enzyme studies. We explored the activity of AZD3409 on breast cancer cell lines with high (SK-Br-3), intermediate (MDA-MB-361) or low (MDA-MB-468) sensitivity to gefitinib. We found that AZD3409 inhibits the growth of breast cancer cells in a dose-dependent manner, with the MDA-MB-468 and MDA-MB-361 cell lines showing higher sensitivity as compared with SK-Br-3 cells. Treatment with AZD3409 produced a significant reduction in the levels of activation of AKT in the three cell lines. AZD3409 also induced an increase in the expression of p27kip-1 and of hypophosphorylated forms of pRb2 in MDA-MB-468 cells that was associated with accumulation of cells in G0/G1 and the appearance of a sub-G1 peak suggestive of apoptosis. In contrast, AZD3409 produced a G2 arrest associated with reduced expression of pRb2 in MDA-MB-361 cells. A synergistic anti-tumor effect was observed when MDA-MB-468 or MDA-MB-361 cells were treated with both AZD3409 and gefitinib, whereas this combination was only additive in SK-Br-3 cells. However, treatment of breast cancer cells with AZD3409 and gefitinib did not produce a more significant blockade of AKT signaling as compared with gefitinib alone. These data suggest that AZD3409 might be active in gefitinib-resistant breast carcinoma.