Activation of Latent HIV Using Drug-Loaded Nanoparticles

Antiretroviral therapy is currently only capable of controlling HIV replication rather than completely eradicating virus from patients. This is due in part to the establishment of a latent virus reservoir in resting CD4+ T cells, which persists even in the presence of HAART. It is thought that forced activation of latently infected cells could induce virus production, allowing targeting of the cell by the immune response. A variety of molecules are able to stimulate HIV from latency. However no tested purging strategy has proven capable of eliminating the infection completely or preventing viral rebound if therapy is stopped. Hence novel latency activation approaches are required. Nanoparticles can offer several advantages over more traditional drug delivery methods, including improved drug solubility, stability, and the ability to simultaneously target multiple different molecules to particular cell or tissue types. Here we describe the development of a novel lipid nanoparticle with the protein kinase C activator bryostatin-2 incorporated (LNP-Bry). These particles can target and activate primary human CD4+ T-cells and stimulate latent virus production from human T-cell lines in vitro and from latently infected cells in a humanized mouse model ex vivo. This activation was synergistically enhanced by the HDAC inhibitor sodium butyrate. Furthermore, LNP-Bry can also be loaded with the protease inhibitor nelfinavir (LNP-Bry-Nel), producing a particle capable of both activating latent virus and inhibiting viral spread. Taken together these data demonstrate the ability of nanotechnological approaches to provide improved methods for activating latent HIV and provide key proof-of-principle experiments showing how novel delivery systems may enhance future HIV therapy

Fuzzy-time-series network used to forecast linear and nonlinear time series

Bas, Eren/0000-0002-0263-8804; Aladag, Cagdas Hakan/0000-0002-3953-7601; Egrioglu, Erol/0000-0003-4301-4149WOS: 000358314000007Non-probabilistic forecasting methods are commonly used in various scientific fields. Fuzzy-time-series methods are well-known non-probabilistic and nonlinear forecasting methods. Although these methods can produce accurate forecasts, linear autoregressive models can produce forecasts that are more accurate than those produced by fuzzy-time-series methods for some real-world time series. It is well known that hybrid forecasting methods are useful techniques for forecasting time series and that they have the capabilities of their components. In this study, a new hybrid forecasting method is proposed. The components of the new hybrid method are a high-order fuzzy-time-series forecasting model and autoregressive model. The new hybrid forecasting method has a network structure and is called a fuzzy-time-series network (FTS-N). The fuzzy c-means method is used for the fuzzification of time series in FTS-N, which is trained by particle swarm optimization. Istanbul Stock Exchange daily data sets from 2009 to 2013 and the Taiwan Stock Exchange Capitalization Weighted Stock Index data sets from 1999 to 2004 were used to evaluate the performance of FTS-N. The applications reveal that FTS-N produces more accurate forecasts for the 11 real-world time-series data sets