Molecular characterization of HIV-1 infection in Northwest Spain (2009–2013): investigation of the subtype F outbreak

[Abstract] Background. HIV-1 subtype B is the predominant one in European regions several, while other subtypes and recombinants are also circulating with high prevalence. A sub-epidemic of subtype F with specific characteristics and low response to treatment has been recently identified in Galicia. In this study we investigated the characteristics of the HIV-1 subtype F sub-epidemic in A Coruña and Santiago de Compostela in Northwest Spain. Methods. 420 newly HIV-1 diagnosed patients during 2009–2013 were enrolled in this study. HIV-1 subtyping was carried out using automated subtyping tools and phylogenetic analysis. Molecular epidemiology investigation of subtypes B and F was performed by means of phylogenetic analysis using fast maximum likelihood. Phylodynamic analysis was performed using Bayesian method as implemented in BEAST v1.8. Results. Subtype B found to be the predominant (61.2% and 70.4%) followed by subtype F (25.6% and 12.0%) in both areas (A Coruña and Santiago de Compostela, respectively). The latter found to mainly spread among men having sex with men (MSM). The vast majority of subtype F lineages from both areas clustered monophyletically, while subtype B sequences clustered in several tree branches. The exponential growth of subtype F sub-epidemic dated back in 2008 by means of phylodynamic analysis. Most of new infections during 2009–2013 occurred within the subtype F transmission cluster. Conclusions. Subtype F circulates at high prevalence in A Coruña and Santiago de Compostela in Northwest Spain, suggesting that the HIV-1 epidemic in this region has distinct characteristics to the rest of Spain. Subtype F has being spreading among MSM and is currently the most actively spreading network. The single cluster spread of this local sub-epidemic might provide an explanation for the distinct characteristics and the low response to antiretroviral treatment

Modelling HIV/AIDS epidemic in Nigeria

Nigeria is one of the countries most affected by the HIV/AIDS pandemic, third only to India and South Africa. With about 10% of the global HIV/AIDS cases estimated to be in the country, the public health and socio-economic implications are enormous. This thesis has two broad aims: the first is to develop statistical models which adequately describe the spatial distribution of the Nigerian HIV/AIDS epidemic and its associated ecological risk factors; the second, to develop models that could reconstruct the HIV incidence curve, obtain an estimate of the hidden HIV/AIDS population and a short term projection for AIDS incidence and a measure of precision of the estimates. To achieve these objectives, we first examined data from various sources and selected three sets of data based on national coverage and minimal reporting delay. The data sets are the outcome of the National HIV/AIDS Sentinel Surveillance Survey conducted in 1999, 2001, 2003 and 2005 by the Federal Ministry of Health; the outcome of the survey of 1057 health and laboratory facilities conducted by the Nigerian Institute of Medical Research in 2000; and case by case HIV screening data collected from an HIV/AIDS centre of excellence. A thorough review of methods used by WHO/UNAIDS to produce estimates of the Nigerian HIV/AIDS scenario was carried out. The Estimation and Projection Package (EPP) currently being used for modelling the epidemic partitions the population into at-risk, not-at-risk and infected sub-populations. It also requires some parameter input representing the force of infection and behaviour or high risk adjustment parameter. It may be difficult to precisely ascertain the size of these population groups and parameters in countries as large and diverse as Nigeria. Also, the accuracy of vital rates used in the EPP and Spectrum program is doubtful. Literature on ordinary back-calculation, nonparametric back-calculation, and modified back-calculation methods was reviewed in detail. Also, an indepth review of disease mapping techniques including multilevel models and geostatistical methods was conducted. The existence of spatial clusters was investigated using cluster analysis and some measure of spatial autocorrelation (Moran I and Geary c coefficients, semivariogram and kriging) applied to the National HIV/AIDS Surveillance data. Results revealed the existence of spatial clusters with significant positive spatial autocorrelation coefficients that tended to get stronger as the epidemic developed through time. GAM and local regression fit on the data revealed spatial trends on the north-south and east - west axis. Analysis of hierarchical, spatial and ecological factor effects on the geographical variation of HIV prevalence using variance component and spatial multilevel models was performed using restricted maximum likelihood implemented in R and empirical and full Bayesian methods in WinBUGS. Results confirmed significant spatial effects and some ecological factors were significant in explaining the variation. Also, variation due to various levels of aggregation was prominent. Estimates of cumulative HIV infection in Nigeria were obtained from both parametric and nonparametric back-calculation methods. Step and spline functions were assumed for the HIV infection curve in the parametric case. Parameter estimates obtained using 3-step and 4-step models were similar but the standard errors of these parameters were higher in the 4-step model. Estimates obtained using linear, quadratic, cubic and natural splines differed and also depended on the number and positions of the knots. Cumulative HIV infection estimates obtained using the step function models were comparable with those obtained using nonparametric back-calculation methods. Estimates from nonparametric back-calculation were obtained using the EMS algorithm. The modified nonparametric back-calculation method makes use of HIV data instead of the AIDS incidence data that are used in parametric and ordinary nonparametric back-calculation methods. In this approach, the hazard of undergoing HIV test is different for routine and symptom-related tests. The constant hazard of routine testing and the proportionality coefficient of symptom-related tests were estimated from the data and incorporated into the HIV induction distribution function. Estimates of HIV prevalence differ widely (about three times higher) from those obtained using parametric and ordinary nonparametric back-calculation methods. Nonparametric bootstrap procedure was used to obtain point-wise confidence interval and the uncertainty in estimating or predicting precisely the most recent incidence of AIDS or HIV infection was noticeable in the models but greater when AIDS data was used in the back-projection model. Analysis of case by case HIV screening data indicate that of 33349 patients who attended the HIV laboratory of a centre of excellence for the treatment of HIV/AIDS between October 2000 and August 2006, 7646 (23%) were HIV positive with females constituting about 61% of the positive cases. The bulk of infection was found in patients aged 15-49 years, about 86 percent of infected females and 78 percent of males were in this age group. Attendance at the laboratory and the proportion of HIV positive tests witnessed a remarkable increase when screening became free of charge. Logistic regression analysis indicated a 3-way interaction between time period, age and sex. Removing the effect of time by stratifying by time period left 2-way interactions between age and sex. A Correction factor for underreporting was ascertained by studying attendance at the laboratory facility over two time periods defined by the cost of HIV screening. Estimates of HIV prevalence obtained from corrected data using the modified nonparametric back-calculation are comparable with UN estimates obtained by a different method. The Nigerian HIV/AIDS pandemic is made up of multiple epidemics spatially located in different parts of the country with most of them having the potential of being sustained into the future given information on some risk factors. It is hoped that the findings of this research will be a ready tool in the hands of policy makers in the formulation of policy and design of programs to combat the epidemic in the country. Access to data on HIV/AIDS are highly restricted in the country and this hampers more in-depth modelling of the epidemic. Subject to data availability, we recommend that further work be done on the construction of stratification models based on sex, age and the geopolitical zones in order to estimate the infection intensity in each of the population groups. Uncertainties surrounding assumptions of infection intensity and incubation distribution can be minimized using Bayesian methods in back-projection

Neuropsychiatric predictors of occupational persistence in HIV/AIDS.

It is well established that HIV (human immunodeficiency virus), the virus responsible for AIDS, directly attacks the central nervous system, altering cognition, behavior, and affect, and can result in a full dementia syndrome. HIV-associated neurocognitive complications, along with a myriad of other health threats, resulted in significant disability and unemployment for those infected. However, the advent of more effective antiretroviral medications used in combinations, along with homologous improvements in morbidity and mortality, have allowed for people living with HIV/AIDS to return to work, albeit not without challenges. Even mild cognitive impairment has been shown to affect employability and level of occupational functioning. The focus of this dissertation was to develop an understanding of the impact of HIV-associated neurocognitive challenges, the most common neuropsychiatric expression of HIV, on occupational persistence. This study analyzed existing data from a parent study conducted in New York City. The sample consisted of 116 community dwelling HIV positive men and women who were actively seeking employment after being unemployed subsequent to learning of their HIV status. The research design was a longitudinal prospective cohort study testing a multilevel growth model with a two- nested-level structure. The growth model examined individual differences in occupational persistence over a two year time period, testing multiple potential neuropsychological predictors and covariates. Changes in individual growth profiles were investigated, and possible explanations for observed differences were tested. The analysis found that memory is the most potent neuropsychological predictor of occupational success, both in terms of returning to work in the first six months of the study (event), as well as persisting on the job over time (two years). The second most influential neuropsychological predictor was executive functioning, which significantly influenced occupational persistence over time and an accelerated growth trajectory. These central findings along with other significant control interactions are discussed. The study limitations are discussed, along with opportunities for future research. The relevance of these findings is explored, specifically addressing the implications for social work practice and social work education

Comparison of Magnetic Resonance Spectroscopy (MRS) data in children with and without HIV at 11-12 years

Although HIV and antiretroviral drugs have been shown to cause damage in the brain, the long-term impacts of perinatal infection, early treatment and exposure in children at 11 years, remain unclear. The effects of HIV and antiretroviral therapy (ART), whilst indistinguishable, can be investigated at a chemical level through proton magnetic resonance spectroscopy (1H-MRS). Previous studies in children have largely focused on individual metabolite changes. However, several adult studies have now advanced beyond this to address patterns of metabolic activity that are altered with HIV infection. Using a 3T Skyra scanner, 136 children (76 HIV+, 30 HEU, 30 HU; 71 males) between the ages of 11.0- 12.5 years, and from a similar socioeconomic background, were scanned. In this study metabolite concentrations were quantified within the basal ganglia (BG), midfrontal gray matter (MFGM) and peritrigonal white matter (PWM). We utilised linear regression to investigate individual metabolite differences, comparing HIV-infected (HIV+) children from the Children with HIV Early Antiretroviral Therapy (CHER) trial, and HIV-exposed-uninfected (HEU) children, to HIV-unexposed (HU) children. Pearson's correlation analysis, factor analysis and logistic regression were then used to study alterations in metabolic patterns between HIV+ and HIV-uninfected (HIV-) children. Analysis of the data was carried out in R. We found elevated total choline in the BG (p = 0.03) and MFGM (p < 0.001) of HIV+ children, as well as reduced PWM total NAA (p = 0.03) and total creatine (p = 0.01). Altered metabolite concentrations were further observed in HEU children. Additionally, we identified a cross-regional coupling of choline which distinguishes HIV+ from HIV- children (p < 0.001). These findings indicate that multiregional inflammation and PWM axonal damage are occurring in HIV+ children at 11 years. Ultimately, the consequences of perinatal HIV acquisition, in spite of early treatment, continue to be seen at 11 years, as do the impacts of exposure

The role of systemic inflammation and the apolipoprotein E gene in human immunodeficiency virus-associated cognitive impairment

Includes abstract. Includes bibliographical references

Spatial and Temporal Dynamics of Influenza

Despite the significant amount of research conducted on the epidemiology of seasonal influenza, the patterns in the annual oscillations of influenza epidemics have not been fully described or understood. Furthermore, the current understanding of the intrinsic properties of influenza epidemics is limited by the geographic scales used to evaluate the data. Analyses conducted at larger spatial scales may potentially conceal local trends in disease structure which may reveal the effect of population structure or environmental factors on disease spread. By using influenza incidence data from the Commonwealth of Pennsylvania and United States influenza mortality data, this dissertation characterizes seasonal influenza epidemics, evaluates factors that drive local influenza epidemics, and provides an initial assessment in how administrative borders influence surveillance for local and regional influenza epidemics.Evidence of spatial heterogeneity existed in the distribution of influenza epidemics for Pennsylvania counties resulting in a cluster of elevated incidence in the South Central region of the state that persisted during the entire study period (2003-2009). Lower monthly precipitation levels during the influenza season (OR = 0.52, p = 0.0319), fewer residents over age 64 (OR = 0.27, p = 0.01) and fewer residents with more than a high school education (OR = 0.76, p = 0.0148) were significantly associated with membership in this cluster. In addition, significant synchrony in the timing of epidemics existed across the entire state and decayed with distance (regional correlation r = 62%). Synchrony as a function of population size displayed evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations was the best predictor of influenza spread suggesting that non-routine and leisure travel drive local epidemics. Within the United States, clusters of epidemic synchronization existed, most notably in densely populated regions where connectivity is stronger. Observation of county and state epidemic clusters highlights the importance and necessity of correctly identifying the ontologic unit of epidemicity for influenza and other diseases. Recognition of the appropriate geographic unit to implement effective surveillance and prevention methods can strengthen the public health response and minimize inefficient mechanisms

A Survey of Flow Cytometry Data Analysis Methods

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined

Evolution, epistasis, and the genotype-to-phenotype problem in Myxococcus xanthus.

The complex social behavior of M. xanthus makes it an excellent model system to study the relationship between genotype and phenotype. Under nutrient rich conditions, a swarm of M. xanthus cells coordinate their movement outward in search of prey. When starved, cells condense into multicellular structures called aggregates. Taken together, these two aspects of the M. xanthus life cycle display several sub-traits that are used to describe its phenotype. Furthermore, the genome of M. xanthus is large, encoding a predicted 7,314 genes, many of which have been linked to aspects of its multicellular phenotype. This work presented here addresses the genotype-to-phenotype (G2P) problem as it relates to the annotation of a biological process in a model system. The first project addresses G2P from a population genetics approach; we constructed a mutant strain library consisting of 180 single gene knockouts of the ABC transporter superfamily of genes to examine the distribution of mutant phenotypes among an entire group of genes. While the phenotype of only ~10% of mutants show extreme defects, more than three quarters of mutants are parsed into different categories of phenotypic deviation following our analyses. Our results demonstrate that strong mutant phenotypes are uncommon, but the majority of null mutants are phenotypically distinct from wild type in at least one trait. Thus, a more comprehensive understanding of the M. xanthus phenome will help elucidate the biological function of many uncharacterized genes. The second part of this dissertation examines the evolution of M. xanthus as it has been studied as a model organism in different laboratories. Disrupting a gene, or mutating a single nucleotide, may have no discernable impact on the organism\u27s phenotype by itself, but may still substantially affect the phenotypes of additional mutation through epistasis. This is an ongoing phenomena in M. xanthus; whole genome resequencing of several inter-laboratory isolates of M. xanthus wild type DK1622 reveals genomic variation that has resulted in significant phenotypic variation. We demonstrate that the naturally occurring genetic variants among wild type isolates is sufficient to mask the effect of a targeted mutation in one isolate that is significant in another. These results are the first to indicate that isolates of wild type M. xanthus DK1622 have evolved to a functionally significant degree