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Washington D.C., USA, 22-27 July 2012www.aids2012.org Optimizing a dried blood spot-based pooled RT- PCR technique for identification of acute HIV infection.

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Presentation on theme: "Washington D.C., USA, 22-27 July 2012www.aids2012.org Optimizing a dried blood spot-based pooled RT- PCR technique for identification of acute HIV infection."— Presentation transcript:

1 Washington D.C., USA, 22-27 July 2012www.aids2012.org Optimizing a dried blood spot-based pooled RT- PCR technique for identification of acute HIV infection in Mochudi, Botswana RG Davis 1,2, S Dzoro 1, S Moyo 1, S Gaseitsiwe 1,2, R Musonda 1,2, V Novitsky 1,2, M Essex 1,2 1 Botswana-Harvard AIDS Institute Partnership, Gaborone, Botswana 2 Harvard School of Public Health AIDS Initiative, Boston, United States The residents of Mochudi, Botswana The staff of the Botswana-Harvard Partnership Prof. Marcello Pagano and Sarah Anoke for their biostatistics support. This work was funded by a Fulbright/Fogarty Fellowship in Public Health administered through the Harvard School of Public Health. With thanks to:

2 Washington D.C., USA, 22-27 July 2012www.aids2012.org Background / Methods BHP combination prevention strategy including treatment-for-prevention arm. The risk of HIV transmission is high before, during, and soon after seroconversion. Pooling techniques have been advanced to improve cost effectiveness of nucleic acid testing for diagnosis of serologically undetectable AHI in resource limited settings. The goal of this study is to develop and apply a novel dried blood spot (DBS)-based RT-PCR pooling technique to facilitate household sample collection, efficient diagnosis, and treatment-for-prevention. 10 HIV positive DBS samples diluted in duplicate to 6 standardized copy numbers. RT- PCR performed using the Abbott RealTime HIV-1 assay.

3 Washington D.C., USA, 22-27 July 2012www.aids2012.org Figure 5. The logistic regression reflects the log odds ratios of detection: Logit(p) = β 0 + β 1 x 1 where p is the sensitivity and x is the expected viral load. Rewriting the regression equation in terms of p allows us to graph sensitivity as a function of expected viral load. *Pointwise 95% confidence interval. * Results


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