Japanese Encephalitis—A Pathological and Clinical Perspective

Japanese encephalitis (JE) is the leading form of viral encephalitis in Asia. It is caused by the JE virus (JEV), which belongs to the family Flaviviridae. JEV is endemic to many parts of Asia, where periodic outbreaks take hundreds of lives. Despite the catastrophes it causes, JE has remained a tropical disease uncommon in the West. With rapid globalization and climatic shift, JEV has started to emerge in areas where the threat was previously unknown. Scientific evidence predicts that JEV will soon become a global pathogen and cause of worldwide pandemics. Although some research documents JEV pathogenesis and drug discovery, worldwide awareness of the need for extensive research to deal with JE is still lacking. This review focuses on the exigency of developing a worldwide effort to acknowledge the prime importance of performing an extensive study of this thus far neglected tropical viral disease. This review also outlines the pathogenesis, the scientific efforts channeled into develop a therapy, and the outlook for a possible future breakthrough addressing this killer disease

A Distinct Macrophage Population Mediates Metastatic Breast Cancer Cell Extravasation, Establishment and Growth

Background: The stromal microenvironment and particularly the macrophage component of primary tumors influence their malignant potential. However, at the metastatic site the role of these cells and their mechanism of actions for establishment and growth of metastases remain largely unknown. Methodology/Principal Findings: Using animal models of breast cancer metastasis, we show that a population of host macrophages displaying a distinct phenotype is recruited to extravasating pulmonary metastatic cells regardless of species of origin. Ablation of this macrophage population through three independent means (genetic and chemical) showed that these macrophages are required for efficient metastatic seeding and growth. Importantly, even after metastatic growth is established, ablation of this macrophage population inhibited subsequent growth. Furthermore, imaging of intact lungs revealed that macrophages are required for efficient tumor cell extravasation. Conclusion/Significance: These data indicate a direct enhancement of metastatic growth by macrophages through their effects on tumor cell extravasation, survival and subsequent growth and identifies these cells as a new therapeutic target fo

Regulation of inflammation in Japanese encephalitis

Uncontrolled inflammatory response of the central nervous system is a hallmark of severe Japanese encephalitis (JE). Although inflammation is necessary to mount an efficient immune response against virus infections, exacerbated inflammatory response is often detrimental. In this context, cells of the monocytic lineage appear to be important forces driving JE pathogenesis

Strategies to Target Tumor Immunosuppression

The tumor microenvironment is currently in the spotlight of cancer immunology research as a key factor impacting tumor development and progression. While antigen-specific immune responses play a crucial role in tumor rejection, the tumor hampers these immune responses by creating an immunosuppressive microenvironment. Recently, major progress has been achieved in the field of cancer immunotherapy, and several groundbreaking clinical trials demonstrated the potency of such therapeutic interventions in patients. Yet, the responses greatly vary among individuals. This calls for the rational design of more efficacious cancer immunotherapeutic interventions that take into consideration the “immune signature” of the tumor. Multimodality treatment regimens that aim to enhance intratumoral homing and activation of antigen-specific immune effector cells, while simultaneously targeting tumor immunosuppression, are pivotal for potent antitumor immunity

Spam Filtering in Social Networks Using Regularized Deep Neural Networks with Ensemble Learning

Part 1: Social Media - Games - OntologiesInternational audienceSpam filtering in social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machine and Naïve Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. To overcome this problem, here we propose a novel approach to social network spam filtering. The approach uses ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on a benchmark dataset in terms of accuracy and area under ROC. In addition, solid performance is achieved in terms of false negative and false positive rates. We also show that the proposed approach outperforms other popular algorithms used in spam filtering, such as decision trees, Naïve Bayes, artificial immune systems, support vector machines, etc