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1型人类免疫缺陷病毒近期感染的检测方法。

Assays for the detection of recent infections with human immunodeficiency virus type 1.

作者信息

Murphy G, Parry J V

机构信息

Virus Reference Department, Health Protection Agency Centre for Infections, London, United Kingdom.

出版信息

Euro Surveill. 2008 Sep 4;13(36):18966.

Abstract

The Serological Testing Algorithm for Recent HIV Seroconversion (STARHS) is a generic term for several laboratory techniques that can be used to differentiate recent from long standing infections with human immunodeficiency virus-1 (HIV-1). There are several other approaches that identify acute seroconverters, but STARHS methods are distinguished by their ability to identify infections that occurred during an extended period of 4-6 months prior to sampling. While the STARHS techniques have been employed on an individual basis, their main usefulness lies in the potential of estimating the rate of acquisition of new HIV infection, or incidence, in a population by application to cross-sectional sero-surveys. This is substantially simpler and less expensive than cohort studies. As such, STARHS techniques facilitate the timely monitoring of the impact on HIV incidence of factors such as interventions, demographic factors and behavioural patterns. The major STARHS techniques currently available are described. Furthermore, the principles behind the methods used are discussed and the limitations of the current assays and the confounding factors that may affect assay specificity are described. A model algorithm for the application of a STARHS assay is shown. Finally, we outline recommendations for laboratory quality systems that will improve the efficiency of STARHS testing, reproducibility of results and reliability of incidence estimates.

摘要

近期HIV血清转化血清学检测算法(STARHS)是几种实验室技术的通用术语,可用于区分人类免疫缺陷病毒1型(HIV-1)近期感染与长期感染。还有其他几种识别急性血清转化者的方法,但STARHS方法的独特之处在于其能够识别在采样前4至6个月的较长时间内发生的感染。虽然STARHS技术已被单独使用,但其主要用途在于通过应用于横断面血清学调查来估计人群中新发HIV感染率或发病率的潜力。这比队列研究要简单得多且成本更低。因此,STARHS技术有助于及时监测干预措施、人口因素和行为模式等因素对HIV发病率的影响。本文描述了目前可用的主要STARHS技术。此外,还讨论了所用方法背后的原理,并描述了当前检测方法的局限性以及可能影响检测特异性的混杂因素。展示了一个应用STARHS检测的模型算法。最后,我们概述了对实验室质量体系的建议,这些建议将提高STARHS检测的效率、结果的可重复性以及发病率估计的可靠性。

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