New therapies that prevent invasion and metastasis in combination with current treatments could therefore significantly reduce cancer recurrence and morbidity. Metastasis is driven by altered signaling pathways that induce changes in cell-cell adhesion, the cytoskeleton, integrin function, protease expression, epithelial-to-mesenchymal transition and

cell survival. The ribosomal S6 kinase (RSK) family of kinases is a group of extracellular signal-regulated kinase/mitogen-activated protein kinase (ERK/MAPK) effectors that can regulate these steps of metastasis by phosphorylating both nuclear and cytoplasmic targets. However, our understanding of RSK function in metastasis remains incomplete check details and is complicated by the fact that the four RSK isoforms perform nonredundant, sometimes BI 2536 opposing functions. Although some isoforms promote cell motility and invasion by altering transcription and integrin activity, others impair cell motility and invasion through effects on the actin cytoskeleton. The mechanism of RSK action depends both on the isoform and the cancer type. However, despite the variance in RSK-mediated outcomes, chemical inhibition of this group of kinases has proven effective in blocking invasion and metastasis of several solid tumors in preclinical models. RSKs are therefore a promising drug target for antimetastatic cancer treatments that could supplement and improve current therapeutic

approaches. This review highlights contradiction and agreement in the current data on the function of RSK isoforms in metastasis and suggests ways forward in developing RSK inhibitors

as new antimetastasis drugs. Cancer Res; 73(20); 6099-105. (C) 2013 AACR.”
“The prevention of infectious diseases is a global health priority area. The early detection of possible epidemics is the first and important defense line against infectious diseases. However, conventional surveillance systems, e. g., the Centers for Disease Control and Prevention (CDC), rely on clinical data. The CDC publishes the surveillance results weeks after epidemic outbreaks. To improve the early detection of epidemic outbreaks, we designed a syndromic surveillance system to predict the epidemic trends based on disease-related Google search volume. Specifically, we first represented the epidemic trend with multiple alert GSK923295 levels to reduce the noise level. Then, we predicted the epidemic alert levels using a continuous density HMM, which incorporated the intrinsic characteristic of the disease transmission for alert level estimation. Respective models are built to monitor both national and regional epidemic alert levels of the U. S. The proposed system can provide real-time surveillance results, which are weeks before the CDC’s reports. This paper focusses on monitoring the infectious disease in the U. S., however, we believe similar approach may be used to monitor epidemics for the developing countries as well.