17 research outputs found

    Additional file 3: Table S3. of Rates of switching to second-line antiretroviral therapy and impact of delayed switching on immunologic, virologic, and mortality outcomes among HIV-infected adults with virologic failure in Rakai, Uganda

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    Risk factors of Immunologic decline, virologic increase or death among HIV infected adults failing on first line ART and switched to Second line ART. Cox proportional MSM model of the time to event of composite endpoint defined as reaching immunologic decline or virologic increase as defined in a and b above, or dying. (DOCX 16 kb

    Additional file 2: Table S2. of Rates of switching to second-line antiretroviral therapy and impact of delayed switching on immunologic, virologic, and mortality outcomes among HIV-infected adults with virologic failure in Rakai, Uganda

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    Risk factors of virologic increase among HIV infected adults failing on first line ART and switched to Second line ART. Cox proportional MSM model of the time to event of virologic increase defined as increase in viral load 芒聣慕0.5 log 10 copies/ml above viral load at virologic failure. (DOCX 16 kb

    MOESM3 of Quantitation of the latent HIV-1 reservoir from the sequence diversity in viral outgrowth assays

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    Additional file 3. Fig. S3. Summary of convergence properties for MCMC sampling. The plots display results for three replicate Markov chain samples (black, red, blue) on the same simulated data set, where there were eight wells with 106 cells, four wells with 4聽脳聽104 cells, and four wells with 320 cells; the true IUPM was set to 位 = 1; and the variant frequencies were set to f = {0.5, 0.25, 0.125, 0.0625, 0.0625}. (left) Decay of autocorrelation with increasing lag between samples in the Markov chain. Throughout the study, we thinned chain samples at a lag of 1000 steps. (right) Traces of posterior probability for the first 10,000 steps of the three replicate Markov chains, which corresponds to the length of the burn-in period used in this study. Based on these results, the rate of approach to the posterior distribution was fairly rapid: on the order of 1000 steps

    The transmission model based on the Incidence Patterns Model.

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    <p>The transmission model uses the posterior distribution from the IPM on the number of new infections acquired and the HIV prevalence in each population group to estimate the distribution of infections transmitted by each group using prior information on the mixing patterns between groups, transmissibility (depending on transmission probability, condom use, STI prevalence), and ART coverage in each group. The diagram is illustrative and does not specifically represent the groups described in the study. The intensity of the grey cells reflects the magnitude of the factors described.</p> <p>DHS, Demographic and Health Surveys; IPM, Incidence Patterns Model; STI, sexually transmitted infection.</p

    MOESM4 of Quantitation of the latent HIV-1 reservoir from the sequence diversity in viral outgrowth assays

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    Additional file 4. Fig. S4. Prominent species identification and elimination of potential outgrowth derived recombinant sequences. a All Pol derived consensus sequences with amplicon totals greater than 0.02% of the total amplicon number for well 5M4 (1,000,000 rCD4+ cells plated) from patient 111 were aligned and viewed in a Neighbor-Joining (NJ) phylogenetic tree. Prominent species were defined as those with amplicon totals > 2.5% of the total amplicon read number for well 5M4 and are indicated with red arrows. b Since the number of prominent species in well 5M4 were 芒聣慕 3 the prominent sequences were aligned and viewed in a highlighter plot. The probable outgrowth derived recombinant sequence is indicated with a red X and was removed from the analysis (probable recombination area highlighted in red box). c All Pol prominent outgrowth sequences for patient 111 were viewed in a NJ tree and variants assigned based on clonality (well 5M4 indicated with red arrows)

    MOESM2 of Quantitation of the latent HIV-1 reservoir from the sequence diversity in viral outgrowth assays

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    Additional file 2. Fig. S2. Effect of misspecified prior distributions on posterior estimates of IUPM from real data sets. The filled curves represent the same posterior distributions as in Fig. 5, where the prior distributions on variant frequencies were set to 脦膮 = {10, 1,芒聙艢 , 1} for patient 106 and 脦膮 = {1,芒聙艢, 1} for patient 111. The dashed curves represent the posterior distributions obtained when these priors were swapped between the patient data sets. These results illustrate that misspecification of the prior distribution on variant frequencies can have a measurable effect on posterior estimates of IUPM where the underlying variant frequencies are skewed toward a single common variant (patient 106). However when the virus population has low clonality and there is a mixture of positive and negative wells at the lowest dilution of the QVOA (patient 111, see Table 2), the posterior estimates are more robust to the prior settings
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