CD4 T-lymphocyte counts (basic model M1), HIV VL (M6 model), and HAART (M7 model) effects on HPV clearance probability, HPV type-specific, in HIV-1-positive adolescent females, REACH cohort.

<p>Note: * 0.05≤p<0.1; ** p<0.05. <i>u₀₀</i>, <i>β_00, u_11, and β₁₁</i> are related to the parameters in equation (2).</p>a<p>– the units of <i>β₀₀</i> and <i>β₁₁</i> are 1000/[C], where [C] are the units of CD4 cell counts, i.e., cells/mm³.; b – non-significant.</p><p>SEs were obtained by re-estimating the model in which probability at specific value of CD4 cell count was chosen as a model parameter instead of .</p

Hazard ratios for HPV infection clearance probability for HIV-1-infected adolescent females from the REACH cohort, univariable and multivariable Cox proportional hazard regression (results are presented with 95% CIs).

<p>Note: ^†p<0.05; ^‡p<0.001; ns - not significant; n/a – not applicable.</p

Reconstruction of information about the missed measurements when one HPV status is unknown (<b>Figure 1A</b>) or several (e.g., three) HPV statuses in a raw are missed (<b>Figure 1B</b>).

<p>Here, denotes the set of predictors of HPV clearance probability, such as CD4 count, HIV-1 VL, HAART, and HPV type. When one HPV measurement is unknown (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030736#pone-0030736-g001" target="_blank">Figure 1A</a>), <i>i</i> and <i>j</i> describe the HPV status at the first and third visits, respectively, and parameters and denote the sets of predictors for transitions between first-to-second and second-to-third visits, respectively. The probability of changing HPV status from the first (i.e., known) state of HPV infection <i>i</i> to the status of HPV infection at the second visit (i.e., unknown) is <i>P_i</i>₀(<i>x_a</i>) when HPV status at the second visit is negative (i.e., “0”) or <i>P_i</i>₁(<i>x_a</i>) when it is positive (i.e., “1”). Respectively, at the third visit (with measured/known HPV status) HPV status <i>j</i> can be defined as <i>P</i>_0<i>j</i>(<i>x_b</i>) when at the second visit it supposed to be HPV-negative, and <i>P</i>_1<i>j</i>(<i>x_b</i>) when at the second visit it supposed to be HPV-positive. The sum over two possible intermediate states contributes to the total transition probability: so, the transition probability between two subsequent visits with measured HPV status could be presented as . When three subsequent HPV status are unknown (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030736#pone-0030736-g001" target="_blank">Figure 1B</a>), there are eight different combinations of HPV statuses in these states, each denoted by , , and as unmeasured HPV statuses which can be 0 or 1). Therefore, the transition probability between states with known HPV statuses is calculated as three-fold sum over all combinations of HPV statuses in these three unmeasured states.</p

CD4 T-lymphocyte counts (basic model M1), HIV VL (M6 model), and HAART with PI (M7 model) effects on probability of HPV clearance, by phylogenetic HPV group, in HIV-1-infected adolescent females, REACH cohort.

<p>Note:</p><p>*0.05≤p<0.1;</p><p>**p<0.05.<i>u₀₀</i>, <i>β_00, u_11, and β₁₁</i> are related to the parameters in equation (2)</p>a<p>– the units of <i>β₀₀</i> and <i>β₁₁</i> are 1000/[C], where [C] are the units of CD4 cell counts, i.e., cells/mm³.</p><p>SEs were obtained by re-estimating the model in which probability at specific value of CD4 cell count was chosen as a model parameter instead of .</p

Demographic, behavioral, and clinical characteristics of adolescent female study participants from the REACH cohort.

<p>Notes: ¹ – results are presented as mean (SD); ² – number of cases (percent);</p>†<p>– p<0.05 for the difference between HIV-1-positive and HIV-1-negative: continuous variables were analyzed by general linear model, and categorical were analyzed by chi-square;</p>‡<p>– p<0.05 for the difference with the referent group; continuous variables were analyzed by general linear model, and categorical were analyzed by PROC LOGISTIC.</p

The 3-month HPV type-specific probability of clearance depending on CD4 T-lymphocytes in HIV-1-positive adolescent girls from the REACH cohort.

<p>The 3-month HPV type-specific probability of clearance depending on CD4 T-lymphocytes in HIV-1-positive adolescent girls from the REACH cohort.</p

Probability of HPV clearance (in %, ±SE) at specific CD4 levels, by phylogenetic HPV group, in HIV-1-positive adolescent females, REACH cohort.

<p>Note: *The difference with HPV16/16-like type is significant (p<0.05).</p

Prevalence and Concordance of High Risk-Human Papillomavirus (HR-HPV) Test Results Between Clinician-collected and Self-collected Cervico-vaginal Samples at a Health Camp in Achham District, Nepal in 261 women.

§<p>95% CI = 95% Confidence Interval;</p><p>*High-Risk HPV (HR-HPV) defined as testing positive for one of the following genotypes: (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68);</p><p>**HSIL = High-grade Squamous Intraepithelial Lesion; SCC = Squamous Cell Carcinoma.</p

Genomic Copy Number Variants: Evidence for Association with Antibody Response to Anthrax Vaccine Adsorbed

<div><p>Background</p><p>Anthrax and its etiologic agent remain a biological threat. Anthrax vaccine is highly effective, but vaccine-induced IgG antibody responses vary widely following required doses of vaccinations. Such variation can be related to genetic factors, especially genomic copy number variants (CNVs) that are known to be enriched among genes with immunologic function. We have tested this hypothesis in two study populations from a clinical trial of anthrax vaccination.</p><p>Methods</p><p>We performed CNV-based genome-wide association analyses separately on 794 European Americans and 200 African-Americans. Antibodies to protective antigen were measured at week 8 (early response) and week 30 (peak response) using an enzyme-linked immunosorbent assay. We used DNA microarray data (Affymetrix 6.0) and two CNV detection algorithms, hidden markov model (PennCNV) and circular binary segmentation (GeneSpring) to determine CNVs in all individuals. Multivariable regression analyses were used to identify CNV-specific associations after adjusting for relevant non-genetic covariates.</p><p>Results</p><p>Within the 22 autosomal chromosomes, 2,943 non-overlapping CNV regions were detected by both algorithms. Genomic insertions containing <i>HLA-DRB5, DRB1</i> and <i>DQA1/DRA</i> genes in the major histocompatibility complex (MHC) region (chromosome 6p21.3) were moderately associated with elevated early antibody response (β = 0.14, p = 1.78×10⁻³) among European Americans, and the strongest association was observed between peak antibody response and a segmental insertion on chromosome 1, containing <i>NBPF4, NBPF5, STXMP3, CLCC1</i>, and <i>GPSM2</i> genes (β = 1.66, p = 6.06×10⁻⁵). For African-Americans, segmental deletions spanning <i>PRR20, PCDH17</i> and <i>PCH68</i> genes on chromosome 13 were associated with elevated early antibody production (β = 0.18, p = 4.47×10⁻⁵). Population-specific findings aside, one genomic insertion on chromosome 17 (containing <i>NSF, ARL17</i> and <i>LRRC37A</i> genes) was associated with elevated peak antibody response in both populations.</p><p>Conclusion</p><p>Multiple CNV regions, including the one consisting of MHC genes that is consistent with earlier research, can be important to humoral immune responses to anthrax vaccine adsorbed.</p></div

Dense Genotyping of Immune-Related Loci Identifies Variants Associated with Clearance of HPV among HIV-Positive Women in the HIV Epidemiology Research Study (HERS)

<div><p>Persistent high-risk human papillomavirus (HR-HPV) is a necessary and causal factor of cervical cancer. Most women naturally clear HPV infections; however, the biological mechanisms related to HPV pathogenesis have not been clearly elucidated. Host genetic factors that specifically regulate immune response could play an important role. All HIV-positive women in the HIV Epidemiology Research Study (HERS) with a HR-HPV infection and at least one follow-up biannual visit were included in the study. Cervicovaginal lavage samples were tested for HPV using type-specific HPV hybridization assays. Type-specific HPV clearance was defined as two consecutive HPV-negative tests after a positive test. DNA from participants was genotyped for 196,524 variants within 186 known immune related loci using the custom ImmunoChip microarray. To assess the influence of each single-nucleotide polymorphism (SNP) with HR-HPV clearance, the Cox proportional hazards model with the Wei-Lin-Weissfeld approach was used, adjusting for CD4+ count, low risk HPV (LR-HPV) co-infection, and relevant confounders. Three analytical models were performed: race-specific (African Americans (n = 258), European Americans (n = 87), Hispanics (n = 55), race-adjusted combined analysis, and meta-analysis of pooled independent race-specific analyses. Women were followed for a median time of 1,617 days. Overall, three SNPs (rs1112085, rs11102637, and rs12030900) in the <i>MAGI-3</i> gene and one SNP (rs8031627) in the <i>SMAD3</i> gene were associated with HR-HPV clearance (p<10⁻⁶). A variant (rs1633038) in <i>HLA-G</i> were also significantly associated in African American. Results from this study support associations of immune-related genes, having potential biological mechanism, with differential cervical HR-HPV infection outcomes.</p> </div