Suppression of Multiclade R5 and X4 Human Immunodeficiency Virus Type-1 Infections by a Coreceptor-Based Anti-HIV Strategy

Genetic diversity and molecular epidemiology of HIV transmission

The high genetic diversity of HIV is one of its most significant features, as it has consequences in global distribution, vaccine design, therapy success, disease progression, transmissibility and viral load testing. Studying HIV diversity helps to understand its origins, migration patterns, current distribution and transmission events. New advances in sequencing technologies based on the parallel acquisition of data are now used to characterize within-host and population processes in depth. Additionally, we have seen similar advances in statistical methods designed to model the past history of lineages (the phylodynamic framework) to ultimately gain better insights into the evolutionary history of HIV. We can, for example, estimate population size changes, lineage dispersion over geographic areas and epidemiological parameters solely from sequence data. In this article, we review some of the evolutionary approaches used to study transmission patterns and processes in HIV and the insights gained from such studies.

Survey of the year 2005 commercial optical biosensor literature

We identified 1113 articles (103 reviews, 1010 primary research articles) published in 2005 that describe experiments performed using commercially available optical biosensors. While this number of publications is impressive, we find that the quality of the biosensor work in these articles is often pretty poor. It is a little disappointing that there appears to be only a small set of researchers who know how to properly perform, analyze, and present biosensor data. To help focus the field, we spotlight work published by 10 research groups that exemplify the quality of data one should expect to see from a biosensor experiment. Also, in an effort to raise awareness of the common problems in the biosensor field, we provide side-by-side examples of good and bad data sets from the 2005 literature.