BACKGROUND: Bacterial vaginosis (BV) is a poorly detected public health problem that is associated with preterm delivery and for which no reliable diagnostic tool exists. METHODS: Molecular analysis of 231 vaginal samples, classified by Gram stain-based Nugent score, was used to propose molecular criteria for BV; these criteria were prospectively applied to 56 new samples. A quantitative molecular tool targeting 8 BV-related microorganisms and a human gene was developed using a specific real-time polymerase chain reaction assay and serial dilutions of a plasmid suspension. The targeted microorganisms were Gardnerella vaginalis, Lactobacillus species, Mobiluncus curtisii, Mobiluncus mulieris, and Candida albicans (which can be identified by Gram staining), as well as Atopobium vaginae, Mycoplasma hominis, and Ureaplasma urealyticum (which cannot be detected by Gram staining). RESULTS: With use of the Nugent score, 167 samples were classified as normal, 20 were classified as BV, and 44 were classified as intermediate. Except for U. urealyticum, M. mulieris, and Lactobacillus species, DNA of the tested bacteria was detected more frequently in samples demonstrating BV, but the predictive value of such detection was low. The molecular quantification of A. vaginae (DNA level, > or = 10(8) copies/mL) and G. vaginalis (DNA level, > or = 10(9) copies/mL) had the highest predictive value for the diagnosis of BV, with excellent sensitivity (95%), specificity (99%), and positive (95%) and negative (99%) predictive values; 25 (57%) of the samples demonstrating intermediate flora had a BV profile. When applied prospectively, our molecular criteria had total positive and negative predictive values of 96% and 99%, respectively. CONCLUSIONS: We report a highly reproducible, quantitative tool to objectively analyze vaginal flora that uses cutoff values for the concentrations of A. vaginae and G. vaginalis to establish the molecular diagnosis of BV.
BACKGROUND:Bacterial vaginosis (BV) is a poorly detected public health problem that is associated with preterm delivery and for which no reliable diagnostic tool exists. METHODS: Molecular analysis of 231 vaginal samples, classified by Gram stain-based Nugent score, was used to propose molecular criteria for BV; these criteria were prospectively applied to 56 new samples. A quantitative molecular tool targeting 8 BV-related microorganisms and a human gene was developed using a specific real-time polymerase chain reaction assay and serial dilutions of a plasmid suspension. The targeted microorganisms were Gardnerella vaginalis, Lactobacillus species, Mobiluncus curtisii, Mobiluncus mulieris, and Candida albicans (which can be identified by Gram staining), as well as Atopobium vaginae, Mycoplasma hominis, and Ureaplasma urealyticum (which cannot be detected by Gram staining). RESULTS: With use of the Nugent score, 167 samples were classified as normal, 20 were classified as BV, and 44 were classified as intermediate. Except for U. urealyticum, M. mulieris, and Lactobacillus species, DNA of the tested bacteria was detected more frequently in samples demonstrating BV, but the predictive value of such detection was low. The molecular quantification of A. vaginae (DNA level, > or = 10(8) copies/mL) and G. vaginalis (DNA level, > or = 10(9) copies/mL) had the highest predictive value for the diagnosis of BV, with excellent sensitivity (95%), specificity (99%), and positive (95%) and negative (99%) predictive values; 25 (57%) of the samples demonstrating intermediate flora had a BV profile. When applied prospectively, our molecular criteria had total positive and negative predictive values of 96% and 99%, respectively. CONCLUSIONS: We report a highly reproducible, quantitative tool to objectively analyze vaginal flora that uses cutoff values for the concentrations of A. vaginae and G. vaginalis to establish the molecular diagnosis of BV.
Authors: Charles P Cartwright; Bryndon D Lembke; Kalpana Ramachandran; Barbara A Body; Melinda B Nye; Charles A Rivers; Jane R Schwebke Journal: J Clin Microbiol Date: 2012-04-25 Impact factor: 5.948
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Authors: Michelle D Cherne; Amy L Cole; Lisa Newberry; Mary Schmidt-Owens; Michael Deichen; Alexander M Cole Journal: Infect Immun Date: 2020-03-23 Impact factor: 3.441