Lipid Nanoparticles as Carriers for RNAi against Viral Infections: Current Status and Future Perspectives

The efforts made to develop RNAi-based therapies have led to productive research in the field of infections in humans, such as hepatitis C virus (HCV), hepatitis B virus (HBV), human immunodeficiency virus (HIV), human cytomegalovirus (HCMV), herpetic keratitis, human papillomavirus, or influenza virus. Naked RNAi molecules are rapidly digested by nucleases in the serum, and due to their negative surface charge, entry into the cell cytoplasm is also hampered, which makes necessary the use of delivery systems to exploit the full potential of RNAi therapeutics. Lipid nanoparticles (LNP) represent one of the most widely used delivery systems for in vivo application of RNAi due to their relative safety and simplicity of production, joint with the enhanced payload and protection of encapsulated RNAs. Moreover, LNP may be functionalized to reach target cells, and they may be used to combine RNAi molecules with conventional drug substances to reduce resistance or improve efficiency. This review features the current application of LNP in RNAi mediated therapy against viral infections and aims to explore possible future lines of action in this field.

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed