Bone is one of the most common sites of metastasis in solid malignancy. Contributing to this osteotropism are the dynamic interactions between tumor cells and the numerous cell types resident in the normal bone, particularly osteoclasts and osteoblasts, which create a tumor supporting microenvironment. However, disseminated cells are detected in the bone marrow long before evidence of metastatic outgrowth, and it is likely that prolonged survival is also reliant on immunoescape. Compared with other peripheral organs such as the lung and spleen, the bone marrow constitutes a unique immune cell compartment that likely provides an immune privileged niche for disseminated tumor cells. This includes the large proportions of immunosuppressive cells, including myeloid derived suppressor cells and regulatory T cells, that blunt the activity of cytotoxic lymphocytes involved in tumor immunosurveillance. This review highlights key aspects of the osteoimmune landscape and emerging mechanisms by which tumor cells create or co-opt an immunosuppressed niche to support their outgrowth in bone. Future studies in this field are likely to shed light on the differences in immunoregulation between the bone and other sites including the primary tumor, and the potential for immunotherapeutics in treating disseminated disease in the bone. However, more immunocompetent models, that recapitulate tumor heterogeneity and bone metastasis need to be developed to accelerate this field.

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This paper aims to predict whether a given news article is real or fake. We use the dataset available on Kaggle. We then implement several deep learning models (Long short-term Memory (LSTM), Multi-layerPerceptron (MLP), Convolution Neural Networks (CNN), Hybrid CNN-LSTM on this dataset. For these models we examine the effects of character-based vs. word-based models and pretrained embeddings vs. learned embeddings. We also compare the accuracies of various models and report the best accuracy which we would get from a particular model.