Estimating the accuracy of polymerase chain reaction-based tests using endpoint dilution

PCR-based tests for various microorganisms or target DNA sequences are generally acknowledged to be highly sensitive yet the concept of sensitivity is ill-defined in the literature on these tests. We propose that sensitivity should be expressed as a function of the number of target DNA molecules in the sample (or specificity when the target number is 0). However, estimating this sensitivity curve is problematic since it is difficult to construct samples with a fixed number of targets. Nonetheless, using serially diluted replicate aliquots of a known concentration of the target DNA sequence, we show that it is possible to disentangle random variations in the number of target DNA molecules from the underlying test sensitivity. We develop parametric, nonparametric and semiparametric (spline-based) models for the sensitivity curve. The methods are compared on a new test for M. genitalium

Mycoplasma Genitalium Among Women With Nongonococcal, Nonchlamydial Pelvic Inflammatory Disease

Pelvic inflammatory disease (PID) is a frequent condition of young women, often resulting in reproductive morbidity. Although Neisseria gonorrhoeae and/or Chlamydia trachomatis are/is recovered from approximately a third to a half of women with PID, the etiologic agent is often unidentified. We need PCR to test for M genitalium among a pilot sample of 50 women with nongonococcal, nonchlamydial endometritis enrolled in the PID evaluation and clinical health (PEACH) study. All participants had pelvic pain, pelvic organ tenderness, and leukorrhea, mucopurulent cervicitis, or untreated cervicitis. Endometritis was defined as ≥5 surface epithelium neutrophils per ×400 field absent of menstrual endometrium and/or ≥2 stromal plasma cells per ×120 field. We detected M genitalium in 7 (14%) of the women tested: 6 (12%) in cervical specimens and 4 (8%) in endometrial specimens. We conclude that M genitalium is prevalent in the endometrium of women with nongonococcal, nonchlamydial PID

From the NIH: Proceedings of a Workshop on the Importance of Self-Obtained Vaginal Specimens for Detection of Sexually Transmitted Infections

On June 27, 2006, the NIH conducted a workshop to review published data and current field practices supporting the use of self-obtained vaginal swabs (SOVs) as specimens for diagnosis of sexually transmitted infections (STIs). The workshop also explored the design of studies that could support FDA clearance of SOVs for STI testing, particularly for specimens collected in nonclinical settings including patients’ homes. This report summarizes the workshop findings and recommendations. Participants concluded that self-obtained vaginal swabs are well accepted by women of all ages and that SOVs perform as well as or better than other specimen types for Chlamydia trachomatis and Neisseria gonorrhoeae detection using transcription-mediated amplification. In addition, workshop participants recommended the validation of SOV testing by public health practitioners and manufacturers of STI diagnostic tests to expedite incorporation of SOVs as a diagnostic option in clinical and nonclinical settings for Chlamydia trachomatis and Neisseria gonorrhoeae testing. Similarly, SOVs should be explored for use in the diagnosis of other sexually transmitted pathogens

Biomarkers in Wave III of the Add Health Study

One of the many unique features of Wave III of the Add Health Study was the collection of biological samples. These biological samples permitted the identification of individuals with sexually transmitted infections [STI] (including HIV), and genotype ascertainment for pairs of full-siblings or twins who resided in the same households. The STI testing allows for analyses of individual, household, family, and environmental risk factors for laboratory-confirmed sexually transmitted infections (versus self-report), and the genetic sample facilitates analyses that differentiate between parental, social, and genetic influence, as well as the extent to which genetic differences in neurotransmitter function are associated with a wide range of behaviors. The inclusion of these biomarker data requires special considerations in the analysis of Wave III Add Health data. Thus, the purpose of this monograph is to outline relevant procedures, design, and sampling schemes used in the collection of biomarker data, and to serve as a user’s guide for its analysis and interpretation. The monograph is intended to supplement existing descriptions of the Add Health study, rather than to replace them. Therefore, please refer to the web pages describing the Add Health Study design for more extensive detail on the study (www.cpc.unc.edu/addhealth) and the sampling weights necessary to work with the data (www.cpc.unc.edu/addhealth/codebooks/wave3). Issues that require special consideration include sample design (e.g., who was selected for each type of biomarker test), specimen collection, laboratory methods, and laboratory test performance. Each of these themes is described in separate chapters to this monograph, but should be viewed as complementary to each other

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

MgpB Contains a Single C-terminal Transmembrane Domain.

<p>(A) Schematic depiction of MgpB showing the location of the N-terminal segment absent in the mature protein (aa 1–58, red), variable regions B, EF, and G (yellow), and the position of the previously described transmembrane domains M1–M5 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138244#pone.0138244.ref039" target="_blank">39</a>] (gray and red, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138244#pone.0138244.s001" target="_blank">S1 Table</a> for coordinates). The recombinant peptides spanning MgpB used for the production of rabbit antibodies are indicated with black bars. The N-, D1-, and D2-domains described by Opitz and Jacobs [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138244#pone.0138244.ref034" target="_blank">34</a>] spanning aa 200–384, 769–954, and 1,123–1,360, respectively, are shown in blue with the precise binding locations of attachment inhibiting monoclonal antibodies [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138244#pone.0138244.ref034" target="_blank">34</a>] indicated by lines with circles. (B) Transmembrane domains in MgpB predicted by the TMHMM program. The N-terminal signal peptide and a single C-terminal transmembrane domain (both red) are identified with high probability, predicting a topology in which the majority of the protein is extracellular (pink) with a short cytoplasmic domain (blue), a prediction corroborated by the majority of algorithms used. (C) The N-terminal transmembrane helix identified in panel B includes the predicted MgpB signal peptide. Sequencing of mature MgpB [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138244#pone.0138244.ref039" target="_blank">39</a>] indicates that the N-terminus begins at amino acid 59 (solid underline following red arrow). Using the Gram-positive network, all computational programs (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138244#sec002" target="_blank">Materials & Methods</a>) identify a signal peptide spanning amino acids 1–30 (bold) with the predicted cleavage site occurring between the amino acids VGG-YF; results using the Gram-negative network give a shorter signal peptide covering amino acids 1–26 with the cleavage site between VIT-GV (dotted underline).</p