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

Neuropathology of wild-type and nef-attenuated T cell tropic simian immunodeficiency virus (SIVmac32H) and macrophage tropic neurovirulent SIVmac17E-Fr in cynomolgus macaques

The neuropathology of simian immunodeficiency (SIV) infection in cynomolgus macaques (Macaca fascicularis) was investigated following infection with either T cell tropic SIVmacJ5, SIVmacC8 or macrophage tropic SIVmac17E-Fr. Formalin fixed, paraffin embedded brain tissue sections were analysed using a combination of in situ techniques. Macaques infected with either wild-type SIVmacJ5 or neurovirulent SIVmac17E-Fr showed evidence of neuronal dephosphorylation, loss of oligodendrocyte and CCR5 staining, lack of microglial MHC II expression, infiltration by CD4+ and CD8+ T cells and mild astrocytosis. SIVmacJ5-infected animals exhibited activation of microglia whilst those infected with SIVmac17E-Fr demonstrated a loss of microglia staining. These results are suggestive of impaired central nervous system (CNS) physiology. Furthermore, infiltration by T cells into the brain parenchyma indicated disruption of the blood brain barrier (BBB). Animals infected with the Δnef-attenuated SIVmacC8 showed microglial activation and astrogliosis indicative of an inflammatory response, lack of MHC II and CCR5 staining and infiltration by CD8+ T cells. These results demonstrate that the SIV infection of cynomolgus macaque can be used as a model to replicate the range of CNS pathologies observed following HIV infection of humans and to investigate the pathogenesis of HIV associated neuropathology

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,