Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat Manuscript

Chronic hepatitis B virus (HBV) infection remains the most common risk factor for hepatocellular carcinoma (HCC). High HBV surface antigen (HBsAg) levels are highly correlated with hepatocarcinogenesis and HBV‐associated HCC development. However, the role and detailed mechanisms associated with HBsAg in HCC development remain elusive. In this study, we designed specific single guide RNAs (sgRNAs) targeting the open reading frames, preS1/preS2/S, of the HBV genome and established HBsAg knockout HCC cell lines using the CRISPR/Cas9 system. We showed that knockout of HBsAg in HCC cell lines decreased HBsAg expression and significantly attenuated HCC proliferation in vitro, as well as tumorigenicity in vivo. We also found that overexpression of HBsAg, including the large (LHBs), middle (MHBs), and small (SHBs) surface proteins promoted proliferation and tumor formation in HCC cells. Moreover, we demonstrated that knockout of HBsAg in HCC cells decreased interleukin (IL)‐6 production and inhibited signal transducer and activator of transcription 3 (STAT3) signaling, while overexpression of HBsAg induced a substantial accumulation of pY‐STAT3. Collectively, these results highlighted the tumorigenic role of HBsAg and implied that the IL‐6‐STAT3 pathway may be implicated in the HBsAg‐mediated malignant potential of HBV‐associated HCC.

Background: Immune cell infiltration in the tumor microenvironment (TME) affects tumor initiation, patients' prognosis and immunotherapy strategies. However, their roles and interactions with genomics and molecular processes in hepatocellular carcinoma (HCC) still have not been systematically evaluated. Methods: We performed unsupervised clustering of total 1000 HCC samples including discovery and validation group from available public datasets. Immune heterogeneity of each subtype was explored by multi-dimension analysis. And a support vector machine (SVM) model based on multi-omics signatures was trained and tested. Finally, we performed immunohistochemistry to verify the immune role of signatures. Results: We defined three immune subtypes in HCC, with diverse clinical, molecular, and genomic characteristics. Cluster1 had worse prognosis, better anti-tumor characteristics and highest immune scores, but also accompanied by immunosuppression and T cell dysfunction. Meanwhile, a better anti-PD1/CTLA4 immunotherapeutic response was predicted in cluster1. Cluster2 was enriched in TAM-M2 and stromal cells, indicating immunosuppression. Cluster3, with better prognosis, had lowest CD8 T cell but highest immune resting cells. Further, based on genomic signatures, we developed an SVM classifier to identify the patient's immunological status, which was divided into Type A and Type B, in which Type A had poorer prognosis, higher T cell dysfunction despite higher T cell infiltration, and had better immunotherapeutic response. At the same time, MMP9 may be a potential predictor of the immune characteristics and immunotherapeutic response in HCC. Conclusions: Our work demonstrated 3 immune clusters with different features. More importantly, multi-omics signatures, such as MMP9 was identified based on three clusters to help us recognize patients with different prognosis and responses to immunotherapy in HCC. This study could further reveal the immune status of HCC and provide potential predictors for immune checkpoint treatment response.

Deep feedforward neural networks with piecewise linear activations are currently producing the state-of-the-art results in several public datasets (e.g., CIFAR-10, CIFAR-100, MNIST, and SVHN). The combination of deep learning models and piecewise linear activation functions allows for the estimation of exponentially complex functions with the use of a large number of subnetworks specialized in the classification of similar input examples. During the training process, these subnetworks avoid overfitting with an implicit regularization scheme based on the fact that they must share their parameters with other subnetworks. Using this framework, we have made an empirical observation that can improve even more the performance of such models. We notice that these models assume a balanced initial distribution of data points with respect to the domain of the piecewise linear activation function. If that assumption is violated, then the piecewise linear activation units can degenerate into purely linear activation units, which can result in a significant reduction of their capacity to learn complex functions. Furthermore, as the number of model layers increases, this unbalanced initial distribution makes the model ill-conditioned. Therefore, we propose the introduction of batch normalisation units into deep feedforward neural networks with piecewise linear activations, which drives a more balanced use of these activation units, where each region of the activation function is trained with a relatively large proportion of training samples. Also, this batch normalisation promotes the pre-conditioning of very deep learning models. We show that by introducing maxout and batch normalisation units to the network in network model results in a model that produces classification results that are better than or comparable to the current state of the art in CIFAR-10, CIFAR-100, MNIST, and SVHN datasets.

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Epigenetic modification plays a crucial regulatory role in the biological processes of eukaryotic cells. The recent characterization of DNA and RNA methylation is still ongoing. Tumor metastasis has long been an unconquerable feature in the fight against cancer. As an inevitable component of the epigenetic regulatory network, 5-methylcytosine is associated with multifarious cellular processes and systemic diseases, including cell migration and cancer metastasis. Recently, gratifying progress has been achieved in determining the molecular interactions between m 5 C writers (DNMTs and NSUNs), demethylases (TETs), readers (YTHDF2, ALYREF and YBX1) and RNAs. However, the underlying mechanism of RNA m 5 C methylation in cell mobility and metastasis remains unclear. The functions of m 5 C writers and readers are believed to regulate gene expression at the post-transcription level and are involved in cellular metabolism and movement. In this review, we emphatically summarize the recent updates on m 5 C components and related regulatory networks. The content will be focused on writers and readers of the RNA m 5 C modification and potential mechanisms in diseases. We will discuss relevant upstream and downstream interacting molecules and their associations with cell migration and metastasis.

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Background and Aims Transforming growth factor beta (TGF‐β) suppresses early stages of tumorigenesis, but contributes to the migration and metastasis of cancer cells. However, the role of TGF‐β signaling in invasive prometastatic hepatocellular carcinoma (HCC) is poorly understood. In this study, we investigated the roles of canonical TGF‐β/mothers against decapentaplegic homolog 3 (SMAD3) signaling and identified downstream effectors on HCC migration and metastasis. Approach and Results By using in vitro trans‐well migration and invasion assays and in vivo metastasis models, we demonstrated that SMAD3 and protein tyrosine phosphatase receptor epsilon (PTPRε) promote migration, invasion, and metastasis of HCC cells in vitro and in vivo. Further mechanistic studies revealed that, following TGF‐β stimulation, SMAD3 binds directly to PTPRε promoters to activate its expression. PTPRε interacts with TGFBR1/SMAD3 and facilitates recruitment of SMAD3 to TGFBR1, resulting in a sustained SMAD3 activation status. The tyrosine phosphatase activity of PTPRε is important for binding with TGFBR1, recruitment and activation of SMAD3, and its prometastatic role in vitro. A positive correlation between pSMAD3/SMAD3 and PTPRε expression was determined in HCC samples, and high expression of SMAD3 or PTPRε was associated with poor prognosis of patients with HCC. Conclusions PTPRε positive feedback regulates TGF‐β/SMAD3 signaling to promote HCC metastasis.