Natural Host Genetic Resistance to Lentiviral CNS Disease: A Neuroprotective MHC Class I Allele in SIV-Infected Macaques

Human immunodeficiency virus (HIV) infection frequently causes neurologic disease even with anti-retroviral treatment. Although associations between MHC class I alleles and acquired immunodeficiency syndrome (AIDS) have been reported, the role MHC class I alleles play in restricting development of HIV-induced organ-specific diseases, including neurologic disease, has not been characterized. This study examined the relationship between expression of the MHC class I allele Mane-A*10 and development of lentiviral-induced central nervous system (CNS) disease using a well-characterized simian immunodeficiency (SIV)/pigtailed macaque model. The risk of developing CNS disease (SIV encephalitis) was 2.5 times higher for animals that did not express the MHC class I allele Mane-A*10 (P = 0.002; RR = 2.5). Animals expressing the Mane-A*10 allele had significantly lower amounts of activated macrophages, SIV RNA, and neuronal dysfunction in the CNS than Mane-A*10 negative animals (P<0.001). Mane-A*10 positive animals with the highest CNS viral burdens contained SIV gag escape mutants at the Mane-A*10-restricted KP9 epitope in the CNS whereas wild type KP9 sequences dominated in the brain of Mane-A*10 negative animals with comparable CNS viral burdens. These concordant findings demonstrate that particular MHC class I alleles play major neuroprotective roles in lentiviral-induced CNS disease

CD4-Independent Entry and Replication of Simian Immunodeficiency Virus in Primary Rhesus Macaque Astrocytes Are Regulated by the Transmembrane Protein

Previous studies have demonstrated that the genetic determinants of simian immunodeficiency virus (SIV) neurovirulence map to the env and nef genes. Recent studies from our laboratory demonstrated that SIV replication in primary rhesus macaque astrocyte cultures is dependent upon the nef gene. Here, we demonstrate that macrophage tropism is not sufficient for replication in astrocytes and that specific amino acids in the transmembrane (TM) portion of Env are also important for optimal SIV replication in astrocytes. Specifically, a Gly at amino acid position 751 and truncation of the cytoplasmic tail of TM are required for efficient replication in these cells. Studies using soluble CD4 demonstrated that these changes within the TM protein regulate CD4-independent, CCR5-dependent entry of virus into astrocytes. In addition, we observed that two distinct CD4-independent, neuroinvasive strains of SIV/DeltaB670 also replicated efficiently in astrocytes, further supporting the role of CD4 independence as an important determinant of SIV infection of astrocytes in vitro and in vivo

Efficacy of different AV7909 dose regimens in a nonclinical model of pulmonary anthrax

ABSTRACTPulmonary anthrax caused by exposure to inhaled Bacillus anthracis, the most lethal form of anthrax disease, is a continued military and public health concern for the United States. The vaccine AV7909, consisting of the licensed anthrax drug substance AVA adjuvanted with CpG7909, induces high levels of toxin neutralizing antibodies in healthy adults using fewer doses than AVA. This study compares the ability of one- or two-dose regimens of AV7909 to induce a protective immune response in guinea pigs challenged with a lethal dose of aerosolized B. anthracis spores 6 weeks after the last vaccine dose. The results indicated that AV7909 was less effective when delivered as a single dose compared to the two-dose regimen that resulted in dose-dependent protection against death. The toxin neutralizing assay (TNA) titer and anti-PA IgG responses were proportional to the protective efficacy, with a 50% TNA neutralizing factor (NF50) greater than 0.1 associated with survival in animals receiving two doses of vaccine. The strong protection at relatively low TNA NF50 titers in this guinea pig model supports the exploration of lower doses in clinical trials to determine if these protective levels of neutralizing antibodies can be achieved in humans; however, protection with a single dose may not be feasible

Evaluating multiple performance measures across several dimensions at a multi-facility outpatient center

Over the past several decades healthcare delivery systems have received increased pressure to become more efficient from both a managerial and patient perspective. Many researchers have turned to simulation to analyze the complex systems that exist within hospitals, but surprisingly few have published guidelines on how to analyze models with multiple performance measures. Moreover, the published literature has failed to address ways of analyzing performance along more than one dimension, such as performance by day of the week, patient type, facility, time period, or some combination of these attributes. Despite this void in the literature, understanding performance along these dimensions is critical to understanding the root of operational problems in almost any daily clinic operation. This paper addresses the problem of multiple responses in simulation experiments of outpatient clinics by developing a stratification framework and an evaluation construct by which managers can compare several operationally different outpatient systems across multiple performance measure dimensions. This approach is applied to a discrete-event simulation model of a real-life, large-scale oncology center to evaluate its operational performance as improvement initiatives affecting scheduling practices, process flow, and resource levels are changed. Our results show a reduction in patient wait time and resource overtime across multiple patient classes, facilities, and days of the week. This research has already proven to be successful as certain recommendations have been implemented and have improved the system-wide performance at the oncology center. Copyright Springer Science+Business Media, LLC 2007Multiple response, Terminating discrete-event simulation, Performance measures, Outpatient scheduling, Healthcare applications, Multi-facility service system, Oncology center system analysis,