Dataset Information

A Biochemiluminescent Sialidase Assay for Diagnosis of Bacterial Vaginosis.

ABSTRACT: Bacterial vaginosis (BV) is a common condition among women of reproductive age. A sensitive, quantitative and rapid assay is needed for the diagnosis of and, particularly, therapy monitoring for BV. Bacterial sialidase appears to play an important role in bacterial biofilms on vaginal epithelium, a condition closely associated with BV. Here, we report a biochemiluminescent sialidase assay that uses a substrate derivatized with firefly luciferin. In the presence of sialidase in the reaction, the substrate is cleaved to release luciferin, which is subsequently oxidized by firefly luciferase to generate a light signal. Thus, the light signal intensity can be used to detect and measure the relative concentration of sialidase in a vaginal sample as a means of BV diagnosis. All reagents are present in a reagent bead and sample buffer, enabling essentially a one-step assay. The assay is highly sensitive and quantitative, with a sensitivity and specificity of 95.40% and 94.94%, respectively, compared to the Amsel method. Interestingly, only 27.6% of those with BV had high levels of sialidase activity with a signal to cutoff ratio of 10 or more. The assay may be used for diagnosis of BV, risk assessment of BV patients in terms of sialidase activity levels, and monitoring antibiotic therapy.

SUBMITTER: Wu S

PROVIDER: S-EPMC6934538 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A Biochemiluminescent Sialidase Assay for Diagnosis of Bacterial Vaginosis.

Wu Shengjun S Lin Xuexiang X Hui Kwok Min KM Yang Su S Wu Xuanlan X Tan Yichen Y Li Meimei M Qin Ai-Qing AQ Wang Qingxi Q Zhao Qi Q Ding Pengfei P Shi Kaisheng K Li X James XJ

Scientific reports 20191227 1

Bacterial vaginosis (BV) is a common condition among women of reproductive age. A sensitive, quantitative and rapid assay is needed for the diagnosis of and, particularly, therapy monitoring for BV. Bacterial sialidase appears to play an important role in bacterial biofilms on vaginal epithelium, a condition closely associated with BV. Here, we report a biochemiluminescent sialidase assay that uses a substrate derivatized with firefly luciferin. In the presence of sialidase in the reaction, the ...[more]

PMID: 31882933

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Project description:Bacterial vaginosis (BV) is caused by the excessive and imbalanced growth of bacteria in vagina, affecting 30 to 50% of women. Gram staining followed by Nugent scoring based on bacterial morphotypes under the microscope is considered the gold standard for BV diagnosis; this method is often labor-intensive and time-consuming, and results vary from person to person. We developed and optimized a convolutional neural network (CNN) model and evaluated its ability to automatically identify and classify three categories of Nugent scores from microscope images. The CNN model was first established with a panel of microscopic images with Nugent scores determined by experts. The model was trained by minimizing the cross-entropy loss function and optimized by using a momentum optimizer. The separate test sets of images collected from three hospitals were evaluated by the CNN model. The CNN model consisted of 25 convolutional layers, 2 pooling layers, and a fully connected layer. The model obtained 82.4% sensitivity and 96.6% specificity with the 5,815 validation images when altered vaginal flora and BV were considered the positive samples, which was better than the rates achieved by top-level technologists and obstetricians in China. The capability of our model for generalization was so strong that it exhibited 75.1% accuracy in three categories of Nugent scores on the independent test set of 1,082 images, which was 6.6% higher than the average of three technologists, who are hold bachelor's degrees in medicine and are qualified to make diagnostic decisions. When three technologists ran one specimen in triplicate, the precision of three categories of Nugent scores was 54.0%. One hundred three samples diagnosed by two technologists on different days showed a repeatability of 90.3%. The CNN model outperformed human health care practitioners in terms of accuracy and stability for three categories of Nugent score diagnosis. The deep learning model may offer translational applications in automating diagnosis of bacterial vaginosis with proper supporting hardware.

Project description:BackgroundBacterial vaginosis-a condition defined by a shift from Lactobacillus dominance to a polymicrobial, anaerobic bacterial community-increases the risk of acquiring sexually transmitted infections and other complications of the female reproductive tract. Antibiotic treatment frequently fails to return the microbiome to an optimal Lactobacillus-dominated state. No criteria currently exist to identify the patients likely to experience treatment failure.ObjectiveWe sought to identify the pretreatment community signatures associated with treatment failure through 16S ribosomal RNA gene analysis.Study designTwenty-eight women who were enrolled in an oral metronidazole treatment trial of bacterial vaginosis were studied. Cervicovaginal lavage samples were collected before metronidazole treatment and at 7 and 30 days posttreatment. Cervicovaginal lavage DNA was amplified and sequenced using a paired-end, V4 region 2×150 MiSeq run.ResultsOf the 28 women, 25% failed to clear bacterial vaginosis; 35.7% demonstrated a transient clearance, shifting to community-type 2 (Lactobacillus iners dominant) at visit 2 only; 7.1% demonstrated a delayed clearance, reaching community-type 2 at the final visit only; and 32.1% of patients experienced sustained bacterial vaginosis clearance. Examination of the community composition and structure demonstrated that both the richness and the evenness were significantly lower for the women who experienced sustained clearance, whereas the women who failed to clear bacterial vaginosis possessed the highest median levels of richness, evenness, and diversity pretreatment. Soluble immune factors in the lower reproductive tract improved significantly following a shift from community-type 4 to a Lactobacillus-dominant microbiome, with the samples categorized as community-type 2 possessing significantly higher levels of secretory leukocyte protease inhibitor, growth-regulated alpha protein, and macrophage inflammatory protein-3 and significantly lower levels of intercellular adhesion molecule-1. Although the shifts to Lactobacillus dominance improved the markers of mucosal tissue health, these gains were only temporary among the women who experienced recurrence.ConclusionAssemblies of highly diverse microbiota are associated with the enhanced resilience of bacterial vaginosis to standard metronidazole treatment. These communities may be foundational to treatment resistance or simply an indication of a well-established community made possible by canonical biofilm-forming taxa. Future studies must target the transcriptional activity of these communities under the pressure of antibiotic treatment to resolve the mechanisms of their resistance.

Dataset Information

A Biochemiluminescent Sialidase Assay for Diagnosis of Bacterial Vaginosis.

Publications

A Biochemiluminescent Sialidase Assay for Diagnosis of Bacterial Vaginosis.

Similar Datasets

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