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The receptor-tyrosine-kinase-like orphan receptor 1 (ROR1) is a transmembrane protein belongs to receptor tyrosine kinase (RTK) family. This study aimed to examine the expression of ROR1 in human ovarian cancer and investigate the relationship between its expression and the prognosis of ovarian cancer patients. In this present study, one-step quantitative reverse transcription-polymerase chain reaction (15 ovarian cancer samples of high FIGO stage, 15 ovarian cancer samples of low FIGO stage and nine normal ovary tissue samples) and immunohistochemistry by tissue microarrays (100 ovarian cancer samples and 50 normal ovary samples) were performed to characterize expression of the ROR1 gene in ovarian cancer. Kaplan-Meier survival and Cox regression analyses were executed to evaluate the prognosis of ovarian cancer. The results of qPCR and IHC analysis showed that the expression of ROR1 in ovarian cancer was significantly higher than that in normal ovary tissues (all p < 0.05). Survival analysis showed that ROR1 protein expression was one of the independent prognostic factors for disease-free survival and overall survival (both p < 0.05). The data suggest that ROR1 expression is correlated with malignant attributes of ovarian cancer and it may serve as a novel prognostic marker in ovarian cancer.

Aims To explore the clinical characteristics and placental pathological changes of pregnant women with 2019 novel coronavirus (CoV) disease (COVID-19) in the third trimester, and to assess the possibility of vertical transmission. Methods and results The placenta tissues were evaluated by using immunohistochemistry for inflammatory cells and Hofbauer cells, and using severe acute respiratory syndrome (SARS) CoV-2 RNA Fluorescence In-Situ Hybridization (FISH) and SARS-CoV-2 spike protein immunofluorescence (IF) double staining. All eight placentas from the third trimester pregnancy women were studied. All patients were cured, no clinical or serological evidence pointed to vertical transmission of SARS-CoV-2. Features of maternal vascular malperfusion (MVM) such as increased syncytial knots were present in all 8 cases (8/8), and increased focal perivillous fibrin depositions were presented in 7 cases (7/8). No significate chronic histiocytic intervillositis was noted in the placenta. The number of macrophages and inflammatory cells such as T cells, B cells and plasma cells in the placental villous was not significantly increased in all cases. Moreover, all of eight cases demonstrated negative results by FISH using a SARS-CoV-2 virus RNA probe and by IF using a monoclonal antibody against SARS-CoV-2 spike protein. Conclusions We found no evidence of vertical transmission and adverse maternal-fetal outcomes in the placentas of third trimester COVID-19 pregnancy women, which provided further information for the clinical management of those women in the third trimester. However, further studies are still needed for patients with infections in different stage of gestation, especially in first and second trimester.

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In this paper, we propose a Dual-Agent Generative Adversarial Network (DA-GAN) model, which can improve realism of a face simulator's output using unlabeled real faces, while preserving identity information during realism refinement. The dual agents are specifically designed for distinguishing real v.s. fake and identities simultaneously. In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses. DA-GAN leverages a FCN as the generator and an auto-encoder as the discriminator with dual agents. Besides the novel architecture, we make several key modifications to the standard GAN to preserve pose and texture, preserve identity and stabilize training process: (i) a pose perception loss; (ii) an identity perception loss; (iii) an adversarial loss with a boundary equilibrium regularization term. Experimental results show that DA-GAN not only achieves outstanding perceptual results but also significantly outperforms state-of-the-arts on the challenging NIST IJB-A and CFP unconstrained face recognition benchmarks. In addition, the proposed DA-GAN is also a promising new approach for solving generic transfer learning problems more effectively. DA-GAN is the foundation of our winning in the NIST IJB-A face recognition competition in which we secured the 1st places on the tracks of verification and identification.