Manipulating the Interferon Signaling Pathway: Implications for HIV Infection

During human immunodeficiency virus(HIV) infection, type I interferon(IFN-I) signaling induces an antiviral state that includes the production of restriction factors that inhibit virus replication, thereby limiting the infection. As seen in other viral infections, type I IFN can also increase systemic immune activation which, in HIV disease, is one of the strongest predictors of disease progression to acquired immune deficiency syndrome(AIDS) and non-AIDS morbidity and mortality.Moreover, IFN-I is associated with CD4 T cell depletion and attenuation of antigen-specific T cell responses. Therefore,therapeutic manipulation of IFN-I signaling to improve HIV disease outcome is a source of much interest and debate in thefield. Recent studies have highlighted the importance of timing(acute vs. chronic infection) and have suggested that specific targeting of type I IFNs and their subtypes may help harness the beneficial roles of the IFN-I system while avoiding its deleterious activities.     

Random change point model for joint modeling of cognitive decline and dementia

We propose a joint model for cognitive decline and risk of dementia to describe the pre-diagnosis phase of dementia. We aim to estimate the time when the cognitive evolution of subjects in the pre-dementia phase becomes distinguishable from normal evolution and to study whether the shape of cognitive decline depends on educational level. The model combines a piecewise polynomial mixed model with a random change point for the evolution of the cognitive test and a log-normal model depending on the random change point for the time to dementia. Parameters are estimated by maximum likelihood using a Newton-Raphson-like algorithm. The expected cognitive evolution given age to dementia is then derived and the marginal distribution of dementia is estimated to check the log-normal assumption.     

A Regression Modeling Approach for Describing Patterns of HIV Genetic Variation

We introduce a novel approach for describing patterns of HIV genetic variation using regression modeling techniques. Parameters are defined for describing genetic variation within and between viral populations by generalizing Simpson's index of diversity. Regression models are specified for these variation parameters and the generalized estimating equation framework is used for estimating both the regression parameters and their corresponding variances. Conditions are described under which the usual asymptotic approximations to the distribution of the estimators are met. This approach provides a formal statistical framework for testing hypotheses regarding the changing patterns of HIV genetic variation over time within an infected patient. The application of these methods for testing biologically relevant hypotheses concerning HIV genetic variation is demonstrated in an example using sequence data from a subset of patients from the Multicenter AIDS Cohort Study.     

Bayesian Model Selection for Incomplete Data Using the Posterior Predictive Distribution

Summary We explore the use of a posterior predictive loss criterion for model selection for incomplete longitudinal data. We begin by identifying a property that most model selection criteria for incomplete data should consider. We then show that a straightforward extension of the Gelfand and Ghosh (1998, Biometrika, 85 , 1–11) criterion to incomplete data has two problems. First, it introduces an extra term (in addition to the goodness of fit and penalty terms) that compromises the criterion. Second, it does not satisfy the aforementioned property. We propose an alternative and explore its properties via simulations and on a real dataset and compare it to the deviance information criterion (DIC). In general, the DIC outperforms the posterior predictive criterion, but the latter criterion appears to work well overall and is very easy to compute unlike the DIC in certain classes of models for missing data.     

Age Distribution of Cancer: The Incidence Turnover at Old Age

In recent years data on cancer incidence in the USA, the Netherlands, and in Hong Kong indicate a flattening and perhaps a turnover at advanced age, but no model has been successful in fitting this data and thus providing clues to the underlying biology. In this work we assume these data are reliable and free from bias. We find that a Beta distribution fits SEER age-specific cancer incidence data for all adult cancers extremely well, and its interpretation as a model leads to the possibility that there is a beneficial cancer extinction process that becomes important at elevated age. Particularly evident from the data is the apparent remarkable uniformity of adult cancers peaking in incidence at about the same age, including cancers in other countries. Possible biological mechanisms include increasing apoptosis and cell senescence with age. Further, the model suggests that cancer is not inevitable at advanced age, but reaches a maximum cumulative probability of affliction with any cancer of about 70% for men and 53% for women in the US, and much smaller values for individual cancers.     

A joint model for longitudinal measurements and survival data in the presence of multiple failure types

Summary .   In this article we study a joint model for longitudinal measurements and competing risks survival data. Our joint model provides a flexible approach to handle possible nonignorable missing data in the longitudinal measurements due to dropout. It is also an extension of previous joint models with a single failure type, offering a possible way to model informatively censored events as a competing risk. Our model consists of a linear mixed effects submodel for the longitudinal outcome and a proportional cause-specific hazards frailty submodel ( Prentice et al., 1978 , Biometrics 34, 541–554) for the competing risks survival data, linked together by some latent random effects. We propose to obtain the maximum likelihood estimates of the parameters by an expectation maximization (EM) algorithm and estimate their standard errors using a profile likelihood method. The developed method works well in our simulation studies and is applied to a clinical trial for the scleroderma lung disease.     

Challenges in the Testing of Non-Heart-Beating Cadavers for Viral Markers: Implications for the Safety of Tissue Donors

Natural changes that occur in blood and tissue after death may result in false positive results in antigen and antibody detection tests performed to identify markers of viral infection in potential tissue donors. Such tissue, which might otherwise be acceptable for therapeutic purposes, would not meet current standards for safe tissue banking. This is especially important in the context of insufficiency in the tissue supply. In this study, a series of blood samples collected during routine post-mortem examination was assayed using a range of commercially available kits for the detection of HBsAg, anti-HCV and anti-HIV 1 + 2 antibody/antigen. Results of tests on 104 samples collected from 97 individuals indicate that some kits result in a higher number of initial reactive samples than others. Approximately 40% of samples were reactive in one or more HBsAg assay, less than 10% in at least one anti-HIV kit and only 1 sample at low level on an anti-HCV kit. Liver or lymph node samples from individuals whose serum sample gave reactive results in antigen/antibody assays were tested for viral nucleic acid in the corresponding nucleic acid amplification test. Only one individual’s sample was confirmed to test positive for HBsAg in a confirmatory neutralisation test and by nucleic acid amplification technology, and a second individual whose serum was scored reactive for anti-HCV, but negative for HBsAg, had a liver sample which was HBV DNA positive and HCV RNA negative. The results of the study indicate that antibody/antigen assays are not as specific as NAT using state of the art DNA extraction techniques. Both types of assay complement each other and used together will help assure the safety of tissues for transplantation.     

Estimation of operating characteristics for dependent diagnostic tests based on latent Markov models

We describe a method for making inferences about the joint operating characteristics of multiple diagnostic tests applied longitudinally and in the absence of a definitive reference test. Log-linear models are adopted for the classification distributions conditional on the latent state, where inclusion of appropriate interaction terms accommodates conditional dependencies among the tests. A marginal likelihood is constructed by marginalizing over a latent two-state Markov process. Specific latent processes we consider include a first-order Markov model, a second-order Markov model, and a time-nonhomogeneous Markov model, although the method is described in full generality. Adaptations to handle missing data are described. Model diagnostics are considered based on the bootstrap distribution of conditional residuals. The methods are illustrated by application to a study of diffuse bilateral infiltrates among patients in intensive care wards in which the objective was to assess aspects of validity and clinical agreement.     

Confidence Intervals for the Difference of Median Survival Times Using the Stratified Cox Proportional Hazards Model

The stratified Cox proportional hazards model is introduced to incorporate covariates and involve nonproportional treatment effect of two groups into the analysis and then the confidence interval estimators for the difference in median survival times of two treatments in stratified Cox model are proposed. The one is based on baseline survival functions of two groups, and the other on average survival functions of two groups. I illustrate the proposed methods with an example from a study conducted by the Radiation Therapy Oncology Group in cancer of the mouth and throat. Simulations are carried out to investigate the small‐sample properties of proposed methods in terms of coverage rates.     

A Stochastic Regression Model for General Trend Analysis of Longitudinal Continuous Data

A predictive continuous time model is developed for continuous panel data to assess the effect of time‐varying covariates on the general direction of the movement of a continuous response that fluctuates over time. This is accomplished by reparameterizing the infinitesimal mean of an Ornstein–Uhlenbeck processes in terms of its equilibrium mean and a drift parameter, which assesses the rate that the process reverts to its equilibrium mean. The equilibrium mean is modeled as a linear predictor of covariates. This model can be viewed as a continuous time first‐order autoregressive regression model with time‐varying lag effects of covariates and the response, which is more appropriate for unequally spaced panel data than its discrete time analog. Both maximum likelihood and quasi‐likelihood approaches are considered for estimating the model parameters and their performances are compared through simulation studies. The simpler quasi‐likelihood approach is suggested because it yields an estimator that is of high efficiency relative to the maximum likelihood estimator and it yields a variance estimator that is robust to the diffusion assumption of the model. To illustrate the proposed model, an application to diastolic blood pressure data from a follow‐up study on cardiovascular diseases is presented. Missing observations are handled naturally with this model.     

Longitudinal Analysis of the Human Antibody Response to Chikungunya Virus Infection: Implications for Serodiagnosis and Vaccine Development

Chikungunya virus (CHIKV) is an alphavirus which causes chronic and incapacitating arthralgia in humans. Although previous studies have shown that antibodies against the virus are produced during and after infection, the fine specificity of the antibody response against CHIKV is not known. Here, using plasma from patients at different times postinfection, we characterized the antibody response against various proteins of the virus. We have shown that the E2 and E3 glycoproteins and the capsid and nsP3 proteins are targets of the anti-CHIKV antibody response. Moreover, we have identified the different regions in these proteins which contain the linear epitopes recognized by the anti-CHIKV antibodies and determined their structural localization. Data also illustrated the effect of a single K₂₅₂Q amino acid change at the E2 glycoprotein that was able to influence antibody binding and interaction between the antibodies and epitope because of the changes of epitope-antibody binding capacity. This study provides important knowledge that will not only aid in the understanding of the immune response to CHIKV infection but also provide new knowledge in the design of modern vaccine development. Furthermore, these pathogen-specific epitopes could be used for future seroepidemiological studies that will unravel the molecular mechanisms of human immunity and protection from CHIKV disease.     

RNA-based gene therapy for the treatment and prevention of HIV: from bench to bedside

Gene therapy is considered a feasible approach for the treatment and prevention of HIV/AIDS. Targeting both viral genes and host dependency factors can interfere with the viral lifecycle and prevent viral replication. A number of approaches have been taken to target these genes, including ribozymes, aptamers, and RNAi based therapies. A number of these therapies are now beginning to make their way into clinical trials and providing proof of principle that gene therapy is a safe and realistic option for treating HIV. Here, we focus on those therapies that have progressed along the pipeline to preclinical and clinical testing.     

A semiparametric model for the analysis of recurrent-event panel data

In many longitudinal studies, interest focuses on the occurrence rate of some phenomenon for the subjects in the study. When the phenomenon is nonterminating and possibly recurring, the result is a recurrent-event data set. Examples include epileptic seizures and recurrent cancers. When the recurring event is detectable only by an expensive or invasive examination, only the number of events occurring between follow-up times may be available. This article presents a semiparametric model for such data, based on a multiplicative intensity model paired with a fully flexible nonparametric baseline intensity function. A random subject-specific effect is included in the intensity model to account for the overdispersion frequently displayed in count data. Estimators are determined from quasi-likelihood estimating functions. Because only first- and second-moment assumptions are required for quasi-likelihood, the method is more robust than those based on the specification of a full parametric likelihood. Consistency of the estimators depends only on the assumption of the proportional intensity model. The semiparametric estimators are shown to be highly efficient compared with the usual parametric estimators. As with semiparametric methods in survival analysis, the method provides useful diagnostics for specific parametric models, including a quasi-score statistic for testing specific baseline intensity functions. The techniques are used to analyze cancer recurrences and a pheromone-based mating disruption experiment in moths. A simulation study confirms that, for many practical situations, the estimators possess appropriate small-sample characteristics.     

A platform for designing HIV integrase inhibitors. Part 2: a two-metal binding model as a potential mechanism of HIV integrase inhibitors

We propose a two-metal binding model as a potential mechanism of chelating inhibitors against HIV integrase (HIV IN) represented by 2-hydroxy-3-heteroaryl acrylic acids (HHAAs). Potential inhibitors would bind to two metal ions in the active site of HIV IN to prevent human DNA from undergoing the integration reaction. Correlation of the results of metal (Mg²⁺ and Mn²⁺) titration studies with HIV IN inhibition for a series of active and inactive compounds provides support for the model. Results suggest Mg²⁺ is an essential cofactor for chelating inhibitors.     

Comparison of Linear,Nonlinear and Semiparametric Mixed‐effects Models for Estimating HIV Dynamic Parameters

The potency of antiretroviral agents in AIDS clinical trials can be assessed on the basis of an early viral response such as viral decay rate or change in viral load (number of copies of HIV RNA) of the plasma. Linear, parametric nonlinear, and semiparametric nonlinear mixed‐effects models have been proposed to estimate viral decay rates in viral dynamic models. However, before applying these models to clinical data, a critical question that remains to be addressed is whether these models produce coherent estimates of viral decay rates, and if not, which model is appropriate and should be used in practice. In this paper, we applied these models to data from an AIDS clinical trial of potent antiviral treatments and found significant incongruity in the estimated rates of reduction in viral load. Simulation studies indicated that reliable estimates of viral decay rate were obtained by using the parametric and semiparametric nonlinear mixed‐effects models. Our analysis also indicated that the decay rates estimated by using linear mixed‐effects models should be interpreted differently from those estimated by using nonlinear mixed‐effects models. The semiparametric nonlinear mixed‐effects model is preferred to other models because arbitrary data truncation is not needed. Based on real data analysis and simulation studies, we provide guidelines for estimating viral decay rates from clinical data. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

静脉注射吸毒人群HIV／AIDS数学模型分析

建立了四川省西昌市静脉注射吸毒人群HIV／AIDS传播的数学模型,给出了模型的理论分析和数值模拟结果．通过必要的分析,给出了各类平衡点的存在性和稳定性,系统的一致持续生存,以及基本再生数的数学表达式和具体取值．揭示了该静注人群中的HIV／AIDS有进一步蔓延的趋势,但如果采取适当的干预措施,该静注人群中HIV／AIDS流行可得到有效的控制．     

Metabolism and Distribution of Guanosine Given Intraperitoneally: Implications for Spinal Cord Injury

Intraperitoneal administration of guanosine to rats with chronic spinal cord injury stimulates remyelination and functional recovery. If guanosine produced its effects in the nervous system, it should enter it and elevate endogenous concentrations. [ ³ H]-guanosine (8 mg/kg) was administered intraperitoneally to rats and its distribution and concentration in different sites determined. Guanosine rapidly entered all tissues; its concentration peaked at about 15 minutes except in adipose tissue and CNS where it continued to rise for 30 minutes. Its chief metabolic product in all sites was guanine with over twice as much guanine as guanosine present in CNS after 30 minutes.