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HIV dynamics in seminal plasma during primary HIV infection.

机译:原发性HIV感染期间精浆中的HIV动态。

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HIV dynamics in seminal plasma during primary HIV infection was evaluated through an observational study of individuals with primary HIV infection at the University of Washington Primary Infection Clinic. Seminal plasma HIV RNA was quantified using a real-time reverse transcription PCR assay. Blood plasma RNA was quantified by bDNA or PCR-based assays. Longitudinal analyses of HIV RNA levels over time used random effects regression analysis. From 1993 to 2005, 110 men collected 327 semen specimens. Initial blood and seminal plasma RNA levels in untreated men were only moderately correlated (Spearman r = 0.38, p = 0.0002). Estimated peak and set point levels were lower in semen than blood by 0.8 (p = 0.001) and 0.7 (p < 0.001) log(10) copies/ml, respectively. RNA decay rates were similar in the two compartments (p = 0.4). For 2 months after infection, mean HIV RNA levels in seminal plasma remained above a threshold level (3.8 log(10) copies/ml) that has been associated with recovery of infectious virus in vitro. HIV-positive men are likely to be most infectious in the first months following HIV acquisition. However, the modest relationship between HIV RNA levels in blood and seminal plasma suggests that the relative risk of HIV transmission during primary infection may vary from current estimates that are solely based on blood levels. Incorporating seminal plasma HIV levels into future mathematical models may increase the accuracy of these models.
机译:通过对华盛顿大学原发感染诊所对原发性HIV感染者的观察研究,评估了原发性HIV感染期间精浆中的HIV动态。精浆血浆HIV RNA使用实时逆转录PCR测定法定量。血浆RNA通过bDNA或基于PCR的测定进行定量。 HIV RNA水平随时间的纵向分析使用随机效应回归分析。从1993年到2005年,有110名男性收集了327份精液标本。未经治疗的男性的初始血液和精浆血浆RNA水平只有中等程度的相关性(Spearman r = 0.38,p = 0.0002)。精液中的估计峰值和设定点水平分别比血液低0.8(p = 0.001)和0.7(p <0.001)log(10)个拷贝/毫升。在两个隔室中,RNA衰减率相似(p = 0.4)。感染后2个月,精浆中的平均HIV RNA水平保持在阈值水平(3.8 log(10)拷贝/ ml)以上,该阈值与体外感染性病毒的恢复有关。感染艾滋病毒的男性可能在感染艾滋病毒后的头几个月感染最多。但是,血液和精浆中HIV RNA水平之间的适度关系表明,原发感染期间HIV传播的相对风险可能与仅基于血液水平的当前估计有所不同。将精浆血浆HIV水平纳入未来的数学模型可能会提高这些模型的准确性。

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