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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.

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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.

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.

Melanoma-associated antigens (MAGE)-A9 has been reported to play important roles in the development of human cancers. However, the association between MAGE-A9 expression and the clinicopathological characteristics of hepatocellular carcinoma (HCC) is not well understood. The study was to detect the expression of MAGE-A9 in human HCC and investigate the association between its expression and the clinicopathological characteristics of HCC. Reverse transcription-polymerase chain reaction (RT-PCR), one-step quantitative -PCR (qPCR) and immunohistochemistry (IHC) analyses were performed to characterize the expression of MAGE-A9 in HCC cell lines and tissues. Kaplan-Meier survival and Cox regression analyses were employed to evaluate the prognosis of 100 HCC patients. The results showed that the expression of MAGE-A9 in HCC was significantly higher than that in non-cancerous cells and tissues. Moreover, the expression level of the MAGE-A9 protein in HCC was related to the pathological grade (p = 0.003), portal vein invasion (p = 0.001), distant metastasis (p = 0.022) and TNM stage (p = 0.005). Cox regression analysis further revealed that MAGE-A9 expression is an independent prognostic factor for disease-free survival (p = 0.006) and overall survival (p = 0.022). These data are the first to indicate that MAGE-A9 expression is a valuable prognostic biomarker for HCC and that high MAGE-A9 expression suggests unfavorable survival outcomes in HCC patients.

BackgroundAlpha B-crystallin (αB-crystallin) has been suggested to play an important role in the development of solid tumors. However, the association between αB-crystallin expression and clinicopathological characteristics of human laryngeal carcinoma is not well defined. This study aimed to examine the expression of αB-crystallin in human laryngeal squamous cell carcinoma (LSCC) and investigate the relationship between its expression and the prognosis of LSCC.MethodsReal-time polymerase chain reaction (six LSCC samples, six tumor-adjacent normal samples) and immunohistochemistry by tissue microarrays (109 LSCC samples and 28 tumor-adjacent normal samples) were performed to characterize expression of the αB-crystallin gene in LSCC. Kaplan-Meier survival and Cox regression analyses were carried out to evaluate the prognosis of LSCC.ResultsReal-time polymerase chain reaction and immunohistochemistry analysis showed that the expression of αB-crystallin in LSCC was significantly higher than that in tumor-adjacent normal tissues. Moreover, the expression level of αB-crystallin protein in LSCC was significantly related to alcohol consumption (P = 0.022), tumor differentiation (P = 0.007), pTNM stage (P = 0.041) and 5 years’ survival (P =0.030). COX multi-factor analysis showed that αB-crystallin (P = 0.013), as well as pTNM stage (P =0.027) and lymphatic metastasis (P = 0.015) were independent prognosis factors for LSCC.ConclusionsThe data suggest that αB-crystallin expression is correlated with malignant phenotypes of LSCC and it may serve as a novel prognostic factor for LSCC.

Learning from synthetic faces, though perhaps appealing for high data efficiency, may not bring satisfactory performance due to the distribution discrepancy of the synthetic and real face images. To mitigate this gap, we propose a 3D-Aided Deep Pose-Invariant Face Recognition Model (3D-PIM), which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel way. Specifically, 3D-PIM incorporates a simulator with the aid of a 3D Morphable Model (3D MM) to obtain shape and appearance prior for accelerating face normalization learning, requiring less training data. It further leverages a global-local Generative Adversarial Network (GAN) with multiple critical improvements as a refiner to enhance the realism of both global structures and local details of the face simulator’s output using unlabelled real data only, while preserving the identity information. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks clearly demonstrate superiority of the proposed model over state-of-the-arts.