miR-603 promotes cell proliferation and differentiation by targeting TrkB in acute promyelocytic leukemia.

Arsenic trioxide (ATO) treatment effectively prolongs the overall survival of patients with acute promyelocytic leukemia (APL). Mutations in the oncogene PML::RARA were found in patients with ATO-resistant and relapsed APL. However, some relapsed patients do not have such mutations. Here, we performed microarray analysis of samples from newly diagnosed and relapsed APL, and found different microRNA (miRNA) expression patterns between these two groups. Among the differentially expressed miRNAs, miR-603 was expressed at the lowest level in relapsed patients. The expression of miR-603 and its predicted target tropomyosin-related kinase B (TrkB) were determined by PCR and Western blot. Proliferation was measured using an MTT assay, while apoptosis, cell cycle and CD11b expression were analyzed using flow cytometry. In APL patients, the expression of miR-603 was negatively correlated with that of TrkB. miR-603 directly targeted TrkB and downregulated TrkB expression in the APL cell line NB4. miR-603 increased cell proliferation by promoting the differentiation and inhibiting the apoptosis of NB4 cells. This study shows that the miR-603/ TrkB axis may be a potent therapeutic target for relapsed APL.

A Concurrent Multiscale Detector for End-to-End Image Matching.

This article focuses on end-to-end image matching through joint key-point detection and descriptor extraction. To find repeatable and high discrimination key points, we improve the deep matching network from the perspectives of network structure and network optimization. First, we propose a concurrent multiscale detector (CS-det) network, which consists of several parallel convolutional networks to extract multiscale features and multilevel discriminative information for key-point detection. Moreover, we introduce an attention module to fuse the response maps of various features adaptively. Importantly, we propose two novel rank consistent losses (RC-losses) for network optimization, significantly improving image matching performances. On the one hand, we propose a score rank consistent loss (RC-S-loss) to ensure that the key points have high repeatability. Different from the score difference loss merely focusing on the absolute score of an individual key point, our proposed RC-S-loss pays more attention to the relative score of key points in the image. On the other hand, we propose a score-discrimination RC-loss to ensure that the key point has high discrimination, which can reduce the confusion from other key points in subsequent matching and then further enhance the accuracy of image matching. Extensive experimental results demonstrate that the proposed CS-det improves the mean matching result of deep detector by 1.4%-2.1%, and the proposed RC-losses can boost the matching performances by 2.7%-3.4% than score difference loss. Our source codes are available at https://github.com/iquandou/CS-Net.