A Brief History of Testing for Recent HIV Infection

A Brief History of Testing for Recent HIV Infection

by Dr. George Rutherford

University of California, San Francisco (UCSF)

Soon after the first-generation HIV test was licensed in 1985, there was interest in understanding what went on in infection before measurable HIV antibody appeared. This was largely driven by blood banks’ need to be able to identify donors with HIV infection before they became antibody positive (approximately three weeks from exposure in the original tests). The advent of the ability to test for an HIV antigen, the p24 nucleocapsid protein, which is produced in excess by the replicating virus and enters the circulation, marked the beginning of our technical ability to detect recent HIV infection from laboratory tests. As shown in Figure 1, p24 appears in the circulation at approximately 14 days post-infection. If a patient tests positive for p24 antigen but not for HIV antibody, that person is likely between 14- and 21-days post-infection. This is a very narrow window, and large samples are required to find people in this window. Blood tests have continued to evolve, and blood banks now screen with third-generation tests, which detect antibody by around 21 days post-infection, and antigen testing. HIV RNA testing is another earlier marker of infection that appears even earlier than p24 antigen; it is, however, used primarily for testing newborns and infants for HIV infection (who can have transplacentally transfused maternal antibody).

Figure 1. HIV antibody, p24 and RNA levels as a function of time since exposure.

What properties should an ideal test for incident HIV infection have? It should be universally positive in recent infection and universally negative later in infection. It should be unaffected by HIV subtype, mode of transmission, antiretroviral therapy, presence of opportunistic infections and demographic factors such as age, sex, race and pregnancy. Finally, for the purposes of calculating incidence, it should have a relatively long and uniform window period. There are two broad categories of ways to calculate incidence: direct and indirect methods (Table 1). Below we examine one approach to direct measurement, estimation using laboratory tests for recent infection.

Table 1. Direct and indirect methods for estimating incidence.

Direct methodsIndirect methods
  • Longitudinal follow-up of individuals who do not have HIV infection:
    – Repeated testing of selected cohorts to determine proportion that has acquired infection over time
    – Linking people known to be uninfected with their future laboratory results to see if seroconversion has occurred
  • Estimation using laboratory tests for recent HIV infection
  • Numbers of new cases reported to surveillance system
    – Note that this is NOT the same as incidence because these are only the diagnosed cases
  • Modelling estimated incidence from serial seroprevalence surveys
  • Modelling estimated incidence using assumptions about risk behaviour and HIV transmission
  • Indirect estimates from HIV prevalence in young, recently exposed populations

Antigen and nucleic acid testing in the window period. Early interest in acute HIV infection was two-fold. First, clinicians were interested in the diagnosis of acute HIV infection, a clinical syndrome that included symptoms such as fever, rash, myalgias and arthralgias [[1]]. The second was to use laboratory markers of acute infection to estimate HIV incidence in cross-sectional samples. In 1995, Brookmeyer and colleagues used p24 antigen testing (without regard to HIV antibody) to calculate HIV incidence in a sample of HIV-seronegative patients attending a sexually transmitted disease clinic in Pune, India [[2],[3]]. They found 15 of 1,241 patients to be antigen-positive, antibody-negative and, using a window period of 22.5 days, calculated an incidence rate of 19.6 per 100 person years in this sample. This general method, but using HIV RNA testing instead of p24 testing, was used by Pilcher and colleagues to estimate HIV incidence in North Carolina among individuals testing HIV-negative in state testing facilities [[4]]. They found 23 nucleic acid-positive specimens among 108,644 HIV-uninfected individuals and, combining these results with results from the sensitive/less-sensitive EIA, described below, calculated an HIV incidence of 2.2 infections per 1,000 per year. Similar techniques were proposed for estimating incidence among blood donors [[5]]. However, whether using p24 antigen testing or HIV RNA testing, these methods have not been subsequently used on large samples to estimate incidence largely due to the sample sizes required and the uncertainty as to what the sample represents [[6]].

Serological Testing Algorithm for Recent HIV Seroconversion. In 1998 a new method for identifying patients with recent infection appeared, the Serological Testing Algorithm for Recent HIV Seroconversion (STAHRS) [[7]]. This method tested serum twice, once with a relatively more poorly performing first-generation antibody test (the less-sensitive or detuned test) and the second with a state-of-the-art third generation test (the sensitive test). The less sensitive test was calibrated to detect HIV antibodies approximately 150 days after infection while the sensitive test could detect it within 21 days. Patients who were positive by the new test but negative by the older test were categorized as recently infected, that is, they were in a window period of 21 to 150 days post-infection (Figure 2).

Estimating incidence using STAHRS, however, turned out to be problematic because of variable window periods among different HIV-1 clades. The original window period of 129 days had been estimated using North American type B clades, and the poor performance with non-type B clades [[8]] and problems with false-positive results (that is, false recent results) among patients on antiretroviral therapy (ART) [[9]] led to its eventual discontinuation as a way to estimate incidence. Interestingly, though, the authors saw that the test might be used for something more than incidence estimates and wrote, “It is useful … at the clinical level for patient care … and at the public health level for focusing and evaluating HIV prevention efforts.”

Figure 2. More-sensitive/less-sensitive (detuned) EIA for recent infection

Because of the clade-to-clade variability and other issues that limited the utility of STAHRS, attention turned to tests that would have similar performance across clades. The next series of assays assessed particular antibody characteristics – concentration [[10],[11]], proportion [[12]], isotype [[13]] or avidity [[14]]. All these characteristics appear between the earliest serological response and established infection, that is, the window period. Of these, two, the BED-capture EIA (or BED-CEIA) incidence assay and the avidity assay, have been widely used to estimate incidence in cross-sectional samples.

            BED-CEIA. The BED-CEIA uses a quantitative IgG-capture EIA to determine the relative ratio of HIV-1-specific IgG to total IgG [12]. This ratio is generally lower in earlier infection and higher in longer-term infection. The attraction of the BED-CEIA was that a synthesized peptide, representing a conserved region in the B, E (now CRF-01AE) and D clades of HIV-1, allowed for detection of early HIV infection in several clades [[15]]. The problem, however, remained misclassification of results as recent infections, particularly among those with late-stage infection, low CD4 cell counts and low viral loads and among those on ART [[16],[17],[18],[19]]. Moreover, clade-to-clade differences persisted, with variable window periods for various clades [[20]]. Because of these ongoing limitations, testing algorithms were introduced that combined to compensate for misclassification of long-term infections, such as testing for low CD4 counts, low viral loads or the presence of antiretroviral drug residua or even combining incidence assays, the so-called Recent Infection Testing Algorithm (RITA) [[21]]. Despite these attempts at improved performance, however, the UNAIDS Reference Group on Estimates, Modelling and Projections compared results from BED-CEIA testing in several African countries and Thailand and found that BED-CEIA overestimated incidence derived from a variety of methods. As a result, the Group recommended that BED-CEIA not be used for routine surveillance applications [[22]]. However, investigators continued to work with BED-CEIA and the newer limiting avidity (LAg) assay and using RITAs to improve performance [[23],[24],[25]] largely by minimizing false recent results.

            Limiting antigen avidity testing. LAg testing has avoided several of the pitfalls of BED-CEIA testing and is now the basis for the point-of-care recency tests now being widely used in PEPFAR-funded projects. Avidity assays are based on the strength of antibody-antigen binding and the observation that early antibodies bind less strongly (less avidly) to HIV antigens than antibodies produced by a more mature immune reaction [[26],[27]]. Beyond a certain cut-off point, the antibody response is considered more mature, and the infection is classified as long-term. Unless ART is begun during primary infection, there should be minimal false positives due to viral suppression, whether therapeutically with ART or naturally among elite suppressors [[28]]. Moreover, the maturing avidity pattern has been shown to be similar in patients with clade B and non-clade B infections [[29]]. However, there is some evidence that avidity declines in patients with opportunistic infections, which may lead to misclassification [[30]].

In a 2014 meeting in Barcelona, WHO’s Incidence Assay Working Group reviewed recent data directly comparing the LAg, BED-CEIA, less-sensitive/detuned Vitros, Vitros avidity and BioRad avidity tests, which found that LAg had the lowest false recency rate (Table 2) [[31]]. Moreover, they endorsed the use of RITAs to reduce false recency rates [[32]], which at the time of the meeting had been used successfully to validate incidence measurements from Kenya [[33]], South Africa [[34]] and Eswatini [[35]].

Table 2. Estimated test properties (and 95% confidence intervals) for each assay, for various specimen sets [31].

            Since the 2014 statement, numerous investigators have employed LAg assays with various permutations of RITAs to estimate incidence from representative samples. For instance, Kim and Rehle used the LAg assay plus two different algorithms to estimate incidence in representative national samples from Kenya and South Africa [[36]]. One algorithm used the LAg assay plus viral load testing; the other used the LAg assay, viral load testing and serum testing for antiviral drugs. The investigators tested HIV-positive specimens qualitatively for the presence of antiretroviral drugs using high-performance liquid chromatography with tandem mass spectrometry. In Kenya drugs tested for were lamivudine (3TC), nevirapine (NVP), efavirenz (EFV) and lopinavir (LPV). In South Africa they were 3TC, NVP, EFV, LPV, zidovudine, atazanavir and darunavir, reflecting drugs recommended in the national guidelines. They found that 4 participants in Kenya and 18 in South Africa who had tested positive by the LAg assay and had high viral loads, also tested positive for antiretrovirals, which, when removed by the RITA, led to an almost 20% reduction in incidence estimates (Table 3).

Table 3. Annualized HIV incidence among persons aged 15-49 years by recent infection testing algorithm, Kenya and South Africa, 2012.

            Other countries, notably Botswana [[37]], South Africa [[38]] and several of the Population-Based HIV Impact Assessments (PHIA) [[39],[40]] have used LAg avidity-based RITAs to estimate incidence (Table 4). PHIA investigators estimated incidences ranging from 0.06 per 100 person years in Ethiopia [[41]] to 1.36 per 100 person years in Eswatini [[42]]. They generally used RITAs that included viral load, but they also were able to add testing for antiretroviral drugs and were able to compare results (Table 4) [[43],[44],[45]].

Table 4. PHIA incidence results by country, 2016-2019.

Country  YearEstimated incidence per 100 persons 15-64 years(95% confidence interval)
Cameroon [[46]]2017-20180.27 (0.14-0.41)
Eswatini [42]2017-20181.36 (0.92-1.81)*
Ethiopia [41]2017-20180.06 (0.00-0.12)
Lesotho [[47]]2016-20171.10 (0.68-1.52)**
Malawi [43]2015-20160.37 (0.20-0.54)
Namibia [[48]]20170.36 (0.18-0.55)
Tanzania [[49]]2016-20170.29 (0.18-0.39)
Uganda [[50]]2016-20170.40 (0.25-0.56)
Zambia [44]20160.61 (0.40-1.81)**
Zimbabwe [45]2015-20160.47 (0.25-0.59)



            Rapid point-of-care LAg testing. Rapid point-of-care (RT-POC) LAg avidity tests are the newest wrinkle and are being deployed in a number of PEPFAR-supported countries. Two kits are commercially available from Sedia Biosciences (Asanté) [[51]] and Maxim Biomedical (Swift) [[52]]. The tests can be done with capillary blood and, as they are rapid tests, can be applied in routine HIV testing settings. Some preliminary data are available from Central America, Kenya and Malawi. In Central America, they are used to identify patients presenting to HIV testing sites in Guatemala, Nicaragua and Honduras. In Guatemala, 69% of patients testing positive by RT-POC identified sexual partners who were HIV-infected compared to 39% of those with long-term HIV infection while in Nicaragua the yield increased from 55% to 75% [[53]]. In Malawi, RT-POC LAg avidity tests were used to characterize recent infection among 15-to-24-year-old women [[54]]. Of 589 women who were newly diagnosed with HIV infection, 68 (11.7%) had recent infection, which translated to an estimated incidence of 0.59 per 100 person years. Older women and those residing in Blantyre had significantly higher incidence rates. Another report from Grebe and colleagues [[55]] found that in a pooled seroconverter group using Sedia® LAg-avidity assay and RITAs the mean duration of recent infection (MDRI) was approximately 40 days longer in women than in men; there was no difference between pregnant and non-pregnant women. Other field trials have recently been completed, and more data will be presented at the International AIDS Society’s Conference on HIV Science in Mexico City in July 2019.


[1].     Cooper DA, Gold J, Maclean P, et al. Acute AIDS retrovirus infection. Definition of a clinical illness associated with seroconversion. Lancet 1985; 1:537-40.

[2].     Brookmeyer R, Quinn TC. Estimation of current human immunodeficiency virus infection rates from a cross-sectional survey using early diagnostic tests. Am J Epidemiol 1995; 141:168-72.

[3].     Brookmeyer R, Quinn T, Shepherd M, Mehendale S, Rodrigues J, Bollinger R. The AIDS epidemic in India: a new method for estimating current human immunodeficiency virus (HIV) incidence rates. Am J Epidemiol 1995; 142:709-13.

[4].     Pilcher CD, Fiscus SA, Nguyen TQ, et al. Detection of acute infections during HIV testing in North Carolina. N Engl J Med 2005; 352:1873-83.

[5].     Busch MP, Glynn SA, Stramer SL, et al. A new strategy for estimating risks of transfusion-transmitted viral infections based on rates of detection of recently infected donors. Transfusion 2005; 45:254-64. 

[6].     Le Vu S, Pillonel J, Semaille C, et al. Principles and uses of HIV incidence estimation from recent infection testing – a review. Euro Surveill 2008; 13(36):11-16.

[7].     Janssen RS, Satten GA, Stramer SL, et al. New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes. JAMA 1998; 280:42-48.

[8].     Young CL, Hu DJ, Byers R, et al. Evaluation of a sensitive/less sensitive testing algorithm using the bioMérieux Vironostika-LS assay for detecting recent HIV-1 subtype B’ or E infection in Thailand. AIDS Res Hum Retroviruses 2003; 19:481–86. 

[9].     Laeyendecker O, Rothman RE, Henson C, et al. The effect of viral suppression on cross sectional incidence testing in the Johns Hopkins hospital emergency department. J Acquir Immune Defic Syndr 2008; 48:211–15. 

[10].   Rawal BD, Degula A, Lebedeva L, et al. Development of a new less-sensitive enzyme immunoassay for detection of early HIV-1 infection. J Acquir Immune Defic Syndr 2003; 33:349-55.

[11].   Barin F, Meyer L, Lancar R, et al. Development and validation of an immunoassay for identification of recent human immunodeficiency virus type 1 infections and its use on dried serum spots. J Clin Microbiol 2005; 43:4441-47. 

[12]. Parekh BS, Kennedy MS, Dobbs T, et al. Quantitative detection of increasing HIV type 1 antibodies after seroconversion: a simple assay for detecting recent HIV infection and estimating incidence. AIDS Res Hum Retroviruses 2002; 18:295-307. 

[13].   Wilson KM, Johnson EI, Croom HA, et al. Incidence immunoassay for distinguishing recent from established HIV-1 infection in therapy-naïve populations. AIDS 2004; 18:2253-59. 

[14].   Suligoi B, Galli C, Massi M, et al. Precision and accuracy of a procedure for detecting recent human immunodeficiency virus infections by calculating the antibody avidity index by an automated immunoassay-based method. J Clin Microbiol 2002; 40:4015-20. 

[15].  Dobbs T, Kennedy S, Pau CP et al: Performance characteristics of the immunoglobulin G-capture BED-enzyme immunoassay, an assay to detect recent human immunodeficiency virus type 1 seroconversion. J Clin Microbiol, 2004; 42(6): 2623–28 

[16].   Karita E, Price M, Hunter E, et al. Investigating the utility of the HIV-1 BED capture enzyme immunoassay using cross-sectional and longitudinal seroconverter specimens from Africa. AIDS 2007; 21:403–08. 

[17].   Gupta SB, Murphy G, Koenig E, et al. Comparison of methods to detect recent HIV type 1 infection in cross-sectionally collected specimens from a cohort of female sex workers in the Dominican Republic. AIDS Res Hum Retroviruses 2007; 23:1475–80. 

[18].   Hayashida T, Gatanaga H, Tanuma J, Oka S. Effects of low HIV type 1 load and antiretroviral treatment on IgG-capture BED-enzyme immunoassay. AIDS Res Hum Retroviruses 2008; 24: 495–98. 

[19].   Marinda ET, Hargrove JW, Preiser, W et al: Significantly diminished long-term specificity of the BED capture enzyme immunoassay among patients with HIV-1 with very low CD4 counts and those on antiretroviral therapy. J Acquir Immune Defic Syndr 2010; 53:496–99. 

[20].   Smolen-Dzirba J, Wasik TJ. Current and future assays for identifying recent HIV infections at the population level. Med Sci Monit 2011; 17:RA124-33. 

[21].   Mastro TD, Kim AA, Hallett T, et al. Estimating HIV incidence in populations using tests for recent infection: issues, challenges and the way forward. J HIV AIDS Surveill Epidemiol 2010; 2:1-14. 

[22].   UNAIDS Reference Group on Estimates, Modelling and Projections. Statement on the use of the BED-assay for the estimation of HIV-1 incidence for surveillance or epidemic monitoring. Available at: http://data.unaids.org/pub/epislides/2006/statement_bed_ policy_13dec05_en.pdf.Accessed 31 March 2019.

[23].   Coates TJ, Kulich M, Celentano DD, et al. Effect of community-based voluntary counselling and testing on HIV incidence and social and behavioural outcomes (NIMH Project Accept; HPTN 043): a cluster-randomised trial. Lancet Glob Health 2014; 2:e267-77.

[24].   Konikoff J, Brookmeyer R, Longosz AF, et al. Performance of a limiting-antigen avidity enzyme immunoassay for cross-sectional estimation of HIV incidence in the United States. PLoS One 2013; 8:e82772.

[25].    Laeyendecker O, Kulich M, Donnell D, et al. Development of methods for cross-sectional HIV incidence estimation in a large, community randomized trial. PLoS One 2013; 8:e78818.

[26].   Suligoi B, Galli C, Massi M, et al. Precision and accuracy of a procedure for detecting recent human immunodeficiency virus infections by calculating the antibody avidity index by an automated immunoassay-based method. J Clin Microbiol 2002; 40:4015–20.  

[27].   Suligoi B, Massi M, Galli C, et al. Identifying recent HIV infections using the avidity index and an automated enzyme immunoassay. J Acquir Immune Defic Syndr 2003; 32:424–28. 

[28].   Selleri M, Orchi N, Zaniratti MS, et al. Effective highly active antiretroviral therapy in patients with primary HIV-1 infection prevents the evolution of the avidity of HIV-1-specific antibodies. J Acquir Immune Defic Syndr 2007; 46: 145–50. 

[29].   Suligoi B, Butto S, Galli C, et al. Detection of recent HIV infections in African individuals infected by HIV-1 non-B subtypes using HIV antibody avidity. J Clin Virol 2008; 41:288–92. 

[30].  Chawla A, Murphy G, Donnelly C, et al. Human immunodeficiency virus (HIV) antibody avidity testing to identify recent infection in newly diagnosed HIV type 1 (HIV-1)-seropositive persons infected with diverse HIV-1 subtypes. J Clin Microbiol, 2007; 45:415–20. 

[31].   Kassanjee R, Pilcher CD, Keating SM, et al. Independent assessment of candidate HIV incidence assays on specimens in the CEPHIA repository. AIDS 2014; 28:2439-49. 

[32].   UNAIDS, World Health Organization. Technical update on HIV incidence assays for surveillance and monitoring purposes. Geneva: World Health Organization, 2014. Available at http://www.unaids.org/sites/default/files/media_asset/HIVincidenceassayssurveillancemonitoring_en.pdf. Accessed 31 March 2019. 

[33].   Kimanga DO, Ogola S, Umuro M, et al. Prevalence and incidence of HIV infection, trends, and risk factors among persons 15-64 years in Kenya: results from a nationally representative study. J Acquir Immune Defic Syndr 2014; 66(Suppl 1):S13-26. 

[34].   Shisana O, Rehle T, Simbayi LC, et al. South African National HIV Prevalence, Incidence and Behaviour Survey, 2012.Pretoria: Health Services Research Council, 2014. Available at: http://www.hsrc.ac.za/en/research-outputs/view/6871. Accessed 31 March 2019.

[35].   Swaziland Ministry of Health. Swaziland HIV Incidence Measurement Survey (SHIMS): First Findings Report. Mbabane, eSwatini: Ministry of Health, November 2012. Available at: https://www.k4health.org/sites/default/files/SHIMS_Report.pdf. Accessed 31 March 2019.

[36].   Kim AA, Rehle T. Assessing estimates of HIV incidence with a recent infection testing algorithm that involves viral load testing and exposure to antiretroviral therapy. AIDS Res Hum Retroviruses 2018; 34:863-66. 

[37].   Moyo S, Gaseitsiwe S, Mohammed T, et al. Cross-sectional estimates revealed high HIV incidence in Botswana rural communities in the era of successful ART scale-up in 2013-2015. PLoS One 2018; 13:e0204840. 

[38].   Hansoti B, Stead D, Eisenberg A, et al. A window into the HIV epidemic from a South African emergency department. AIDS Res Hum Retroviruses 2019; 35:139-44. 

[39].   Justman JE, Mugurungi O, El-Sadr WM. HIV population surveys – bringing precision to the global response. N Engl J Med 2018; 378:1859-61.

[40].   ICAP. PHIA Project. Available at: https://phia.icap.columbia.edu/. Accessed 31 March 2019. 

[41].   Ethiopian Federal Ministry of Health. Ethiopian Population-Based HIV Impact Assessment (EPHIA) 2017-2018.Summary sheet: preliminary findings. Addis Ababa, Ethiopia: Federal Ministry of Health, December 2018.

[42].   Eswatini Ministry of Health. Swaziland HIV Incidence Measurement Survey 2: a Population-Based HIV Impact Assessment (SHIMS2) 2016-2017.Summary sheet: preliminary findings.Mbabane, Eswatini: Ministry of Health, November 2017.

[43].   Malawian Ministry of Health.  Malawi Population-Based HIV Impact Assessment (MPHIA).Final Report. Lilongwe, Malawi: Ministry of Health, October 2018.

[44].   Zambian Ministry of Health. Zambia Population-Based HIV Impact Assessment (ZAMPHIA).Final Report. Lusaka, Zambia: Ministry of Health, February 2019.

[45].   Zimbabwean Ministry of Health and Child Care. Zimbabwe Population-Based HIV Impact Assessment (ZIMPHIA), 2015-2016.First Report. Harare, Zimbabwe: Ministry of Health and Child Care, July 2017.

[46].   Cameroonian Ministry of Health. Cameroon Population-Based HIV Impact Assessment (CAMPHIA) 2017.Summary sheet: preliminary findings. Yaoundé, Cameroon: Ministry of Health, July 2018.

[47].   Lesotho Ministry of Health. Lesotho Population-Based HIV Impact Assessment (LePHIA) 2016-2017.Summary sheet: preliminary findings. Maseru, Lesotho: Ministry of Health, July 2018.                                                     

[48].   Namibian Ministry of Health and Social Services. Namibia Population-Based HIV Impact Assessment (NAMPHIA) 2017.Summary sheet: preliminary findings. Windhoek, Nambia: Ministry of Health and Social Service, July 2018.

[49].   Tanzania Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDGEC), Zanzibar Ministry of Health. Tanzania HIV Impact Survey (THIS) 2016-2107.Summary sheet: preliminary findings. Dar Es Salaam, Tanzania: MoHCDGEC, December 2017.

[50].   Uganda Ministry of Health. Uganda Population-Based HIV Impact Assessment (UPHIA) 2016-2017.Extended summary sheet: preliminary findings. Kampala, Uganda: Ministry of Health, April 2018.

[51]. Sedia Biosciences Corporation. Asanté™ HIV-1 Rapid Recency® assay for identification of recent HIV-1 infections. Available at: http://www.sediabio.com/products/asante-rapid-hiv-1-recency-assay. Accessed 31 March 2019.

[52].   Maxim Biosciences. Maxim Swift HIV Recent Infection Assay. Available at: http://www.maximbio.com/viewitem.php?itemID=92002&categoryID=12. Accessed 31 March 2019. 

[53]. Northbrook S. Using rapid HIV recency assay to rapidly detect, monitor, and responds to recent infections in Central America. Available at: https://www.pepfar.gov/documents/organization/285520.pdf. Accessed 31 March 2019.

[54].   Payne D, Maher AD, Curran K, et al. Recent HIV infection surveillance among adolescent girls and young women in Malawi [Poster 831]. Conference on Retroviruses and Opportunistic Infections, Seattle, Washington, March 5, 2019.

[55].   Grebe E, Murphy G, Keating SM, et al. Impact of HIV-1 subtype and sex on Sedia limiting antigen avidity assay performance [Poster 942]. Conference on Retroviruses and Opportunistic Infections, Seattle, Washington, March 7, 2019. 

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