1997
DOI: 10.1016/s0166-0934(97)02215-5
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Improvement in establishing the period of rubella virus primary infection using a mild protein denaturant

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Cited by 5 publications
(5 citation statements)
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“…An avidity index (AI), the percentage of high-avidity antibodies in a specimen, was calculated for each specimen by dividing the absorbance at 405 nm of the urea-treated specimen by the absorbance at 405 nm of the untreated specimen and multiplying by 100. In this assay, 40% was designated as the cutoff for recent infection, with an AI higher than 40% considered as highly avid and indicative of past exposure (4,10).…”
Section: Methodsmentioning
confidence: 99%
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“…An avidity index (AI), the percentage of high-avidity antibodies in a specimen, was calculated for each specimen by dividing the absorbance at 405 nm of the urea-treated specimen by the absorbance at 405 nm of the untreated specimen and multiplying by 100. In this assay, 40% was designated as the cutoff for recent infection, with an AI higher than 40% considered as highly avid and indicative of past exposure (4,10).…”
Section: Methodsmentioning
confidence: 99%
“…Consequently, neither of these antibody isotypes could have made any significant contribution to the overall AI observed. Previous studies have also reported avidity assays to be helpful and reliable in the diagnosis of recent primary rubella virus infection (10), where IgG avidity has been found to increase with time after primary infection but not following reinfection (4,12,21). Analyzing seroconversion panels in an avidity assay probed with antibody FIG. 2.…”
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confidence: 97%
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“…The IgG antibody avidity test has been shown to be very useful for diagnosing recent primary rubella (8,14), toxoplasmosis (21), and cytomegalovirus infection (2,27) in pregnant women; for distinguishing primary hepatitis C virus infection from chronic or past hepatitis C virus infection (18); and for serodiagnosis of many other acute viral diseases (1,13,29). Regarding measles infection, the IgG avidity test has been used for estimating the efficacy of measles vaccines (31) and for identifying secondary vaccine failures (25).…”
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confidence: 99%