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

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

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

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

Associations of full-length <i>KIR2DS4</i> gene with longitudinal viral load (VL) and CD4⁺ T-cell (CD4) count in 207 youth with chronic (seroprevalent) HIV-1 infection.

<p><b>Notes</b>: The overall <i>r</i>² = 0.039 and 0.033 in the multivariable models for VL and CD4 count, respectively. There is no clear interaction between full-length <i>KIR2DS4</i> (gene) and sex (<i>p</i> = 0.274).</p

<i>KIR2DS4</i> gene expression and natural killer (NK) cell function in subjects with chronic HIV-1 infection.

<p><b>A</b>. Flow cytometry gating strategy for NK cells. <b>B</b>. Staining of membrane-bound KIR2DS4 on cells derived from subjects with full-length <i>KIR2DS4</i> gene (g+), to facilitate the sorting of two populations positive (p+) or negative (p−) for the gene product. <b>C</b>. Staining of membrane-bound KIR2DS4 on cells derived from subjects with truncated KIR2DS4 gene only, i.e., negative for full-length <i>KIR2DS4</i> gene and gene product (g−/p−). <b>D</b>. Percentage of NK cells expressing KIR2DS4 before and after stimulation with HLA-deficient target cells (K562 and 221). <b>E</b>. Distribution of NK cell subsets among 43 subjects with and without the full-length <i>KIR2DS4</i> genotype. <b>F–H</b>. Representative results for staining cell surface CD107a (lysosomal-associated membrane protein 1) and intracellular IFN-γ or MIP-1β before (red) and after (blue) stimulation with K562 cells. In <b>D</b> and <b>E</b>, the horizontal bars connected by a vertical line correspond to the median and interquartile range.</p

Linear correlation between HIV-1-related outcomes and KIR2DS4 expression in the absence of antiretroviral therapy.

<p>Results are based on data from 23 subjects with full-length <i>KIR2DS4</i> gene (samples drawn from a single visit). <b>A</b>. Correlation of KIR2DS4 expression with plasma HIV-1 viral load (VL) at the time of sampling. <b>B</b>. Correlation with CD4⁺ T-cell (CD4) count (cells/µL) at the time of sampling. In each panel, solid and dotted lines correspond to the projected trend line and its 95% confidence intervals, respectively (by linear regression).</p