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Antigen delivery platforms based on engineered viruses or virus-like particles are currently developed as vaccines against infectious diseases. As the interaction of vaccines with dendritic cells (DCs) shapes the immunological response, we compared the interaction of a range of virus-based vectors and virus-like particles with DCs in a murine model of systemic administration and transcriptome analyses of splenic DCs. The transcriptome profiles of DCs separated the vaccine vectors into two distinct groups characterized by high- and low-magnitude differential gene expression, which strongly correlated with (1) the surface expression of costimulatory molecules CD40, CD83, and CD86 on DCs, and (2) antigen-specific T-cell responses. Pathway analysis using PANOGA (Pathway and Network-Oriented GWAS Analysis) revealed that the JAK/STAT pathway was significantly activated by both groups of vaccines. In contrast, the oxidative phosphorylation pathway was significantly downregulated only by the high-magnitude DC-stimulating vectors. A gene signature including exclusively chemokine-, cytokine-, and receptor-related genes revealed a vector-specific pattern. Overall, this in vivo DC stimulation model demonstrated a strong relationship between the levels of induced DC maturation and the intensity of T-cell-specific immune responses with a distinct cytokine/chemokine profile, metabolic shifting, and cell surface expression of maturation markers. It could represent an important tool for vaccine design.

This paper is focused on the integration of state-of-the-art technologies in the fields of telecommunications, simulation algorithms, and data mining in order to develop a Type 1 diabetes patient's semi to fully-automated monitoring and management system. The main components of the system are a glucose measurement device, an insulin delivery system (insulin injection or insulin pumps), a mobile phone for the GPRS network, and a PDA or laptop for the Internet. In the medical environment, appropriate infrastructure for storage, analysis and visualizing of patients' data has been implemented to facilitate treatment design by health care experts.