Nanophotonic Sialidase Immunoassay for Bacterial Vaginosis Diagnosis.
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ABSTRACT:
SUBMITTER: Rodriguez-Nava C
PROVIDER: S-EPMC7887842 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC6934538 | biostudies-literature
Project description:One of the most common diseases among women of reproductive age is bacterial vaginosis (BV). However, the etiology of BV remains unknown. In this study, we modeled the temporal sample of the vaginal microbiome as a network and investigated the relationship between the network edges and BV. Furthermore, we used feature selection algorithms including decision tree (DT) and ReliefF (RF) to select the network feature edges associated with BV and subsequently validated these feature edges through logistic regression (LR) and support vector machine (SVM). The results show that: machine learning can distinguish vaginal community states (BV, ABV, SBV, and HEA) based on a few feature edges; selecting the top five feature edges of importance can achieve the best accuracy for the feature selection and classification model; the feature edges selected by DT outperform those selected by RF in terms of classification algorithm LR and SVM, and LR with DT feature edges is more suitable for diagnosing BV; two feature selection algorithms exhibit differences in the importance of ranking of edges; the feature edges selected by DT and RF cannot construct sub-network associated with BV. In short, the feature edges selected by our method can serve as indicators for personalized diagnosis of BV and aid in the clarification of a more mechanistic interpretation of its etiology.
| S-EPMC9854373 | biostudies-literature
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.
| S-EPMC8111127 | biostudies-literature
Project description:Bacterial vaginosis (BV) is a difficult-to-treat recurrent condition in which health-associated lactobacilli are outnumbered by other anaerobic bacteria, such as Gardnerella vaginalis. Certain genotypes of G. vaginalis can produce sialidase, while others cannot. Sialidase is known to facilitate the destruction of the protective mucus layer on the vaginal epithelium by hydrolysis of sialic acid on the glycans of mucous membranes. This process possibly facilitates adhesion of bacterial cells on the epithelium since it has been linked with the development of biofilm in other pathogenic conditions. Although it has not been demonstrated yet, it is probable that G. vaginalis benefits from this mechanism by attaching to the vaginal epithelium to initiate biofilm development. In this study, using vaginal specimens of 120 women enrolled in the Ring Plus study, we assessed the association between the putative G. vaginalis sialidase A gene by quantitative polymerase chain reaction (qPCR), the diagnosis of BV according to Nugent score, and the occurrence of a BV-associated biofilm dominated by G. vaginalis by fluorescence in situ hybridisation (FISH). We detected the putative sialidase A gene in 75% of the G. vaginalis-positive vaginal specimens and found a strong association (p<0.001) between the presence of a G. vaginalis biofilm, the diagnosis of BV according to Nugent and the detection of high loads of the G. vaginalis sialidase A gene in the vaginal specimens. These results could redefine diagnosis of BV, and in addition might guide research for new treatment.
| S-EPMC5328246 | biostudies-literature
Project description:PurposeBacterial vaginosis (BV) is a common clinical condition characterized by odorous vaginal discharge, vaginal itching and/or burning. BV can occur when vaginal lactobacilli are depleted and replaced by diverse anaerobic bacteria. We evaluated a commercial multiplex PCR (ATRiDA) for the diagnosis of BV.MethodsCervicovaginal samples were included from women reporting urogenital symptoms and from women notified for sexually transmitted infections (STI) - who were not (necessarily) symptomatic. Clinical BV diagnoses were obtained from electronic patient files. The ATRiDA test measures the loads of Gardnerella vaginalis, Atopobium vaginae and Lactobacillus species in relation to overall bacterial load. The ATRiDA test outcome was compared to the clinical BV diagnosis and to vaginal microbiota composition, determined by 16SrRNA gene sequencing.ResultsWe included samples from 185 women reporting urogenital symptoms, of whom 81 had BV and 93 women who were notified for an STI, of whom 16 had BV. Overall, compared to the clinical BV diagnosis, the ATRiDA test demonstrated high sensitivity (96.9 %) and moderate specificity (70.2 %). The negative predictive value was high (>97.3). The positive predictive value differed by study group and was highest in women reporting urogenital symptoms (78.2 %). Sequencing showed that 54 % of women who had an ATRiDA BV-positive test outcome, but who were not clinically diagnosed with BV, had diverse anaerobic vaginal microbiota (asymptomatic vaginal dysbiosis).ConclusionThe ATRiDA test is a sensitive method for the detection of BV but, given the high occurrence of asymptomatic vaginal dysbiosis, a positive test outcome should be interpreted together with clinical symptoms.
| S-EPMC6230723 | biostudies-literature
Project description:OBJECTIVE:Some diagnostic features of the genitourinary syndrome of menopause (GSM) and bacterial vaginosis (BV) overlap, such as low levels of vaginal Lactobacillus and pH?>?5. We sought to determine clinicians' diagnostic and treatment practices for postmenopausal women presenting with BV and GSM scenarios and how commercial molecular screening tests are utilized. METHODS:Anonymous surveys were sent to practicing women's health clinicians to evaluate assessment and treatment strategies for postmenopausal women presenting with BV and GSM scenarios. RESULTS:When given a scenario of a postmenopausal woman with symptoms overtly positive for BV, a majority of providers (73%) would conduct a wet mount, though only 35% would evaluate full Amsel's criteria. A majority (89%) recommended treatment with antibiotics, 28.2% recommended vaginal estrogen in addition to antibiotics, and 11.8% recommended vaginal estrogen alone. Of providers who would use a molecular swab, 30% would wait for results before treating the patient's symptoms. When given a scenario of a postmenopausal woman presenting with GSM, a majority (80%) recommended vaginal estrogen, and only 4.6% recommended antibiotics. Few (16%) responders would evaluate with a molecular swab, half of whom would wait for results before prescribing treatment. Clinicians in practice for less than 10 years were more likely to rely on molecular swabs than those who had been practicing longer (P?<?0.0003). CONCLUSIONS:Methods used to evaluate postmenopausal women with vaginal symptoms vary. Future studies of postmenopausal women that differentiate diagnostic criteria between BV and GSM, and validate commercial molecular testing for BV in women over age 50 are needed.
| S-EPMC7291839 | biostudies-literature
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.
| S-EPMC8887553 | biostudies-literature
Project description:Despite the availability of a number of oral and intravaginal antibiotic medications for the treatment of bacterial vaginosis (BV), management of this condition remains challenging. Recurrent BV occurs in >50% of patients receiving guideline-recommended treatments. This may be due to persistence or resurgence of the BV biofilm after treatment cessation, failure to reestablish an optimal vaginal microbiome after treatment, reinfection from an untreated sexual partner, or a combination of these factors. Nonadherence to multidose BV therapies may potentially contribute to recurrent BV, although there are no published data that directly assess the role of nonadherence to poor treatment outcomes and recurrent BV. There is a need for studies of BV treatment adherence in real-world settings as well as studies to explore the relationship between treatment adherence and recurrence. This review explores challenges associated with diagnosing and treating BV, current multidose antibiotic treatment options, newer single-dose treatment options, and ways to potentially maximize treatment success for this common vaginal infection.
| S-EPMC7294746 | biostudies-literature
Project description:Quantitative PCR assays were developed for 4 organisms reported previously to be useful positive indicators for the diagnosis of bacterial vaginosis (BV)--Atopobium vaginae, Bacterial Vaginosis-Associated Bacterium 2 (BVAB-2), Gardnerella vaginalis, and Megasphaera-1--and a single organism (Lactobacillus crispatus) that has been implicated as a negative indicator for BV. Vaginal samples (n = 169), classified as positive (n = 108) or negative (n = 61) for BV based on a combination of the Nugent Gram stain score and Amsel clinical criteria, were analyzed for the presence and quantity of each of the marker organisms, and the results were used to construct a semiquantitative, multiplex PCR assay for BV based on detection of 3 positive indicator organisms (A. vaginae, BVAB-2, and Megasphaera-1) and classification of samples using a combinatorial scoring system. The prototype BV PCR assay was then used to analyze the 169-member developmental sample set and, in a prospective, blinded manner, an additional 227 BV-classified vaginal samples (110 BV-positive samples and 117 BV-negative samples). The BV PCR assay demonstrated a sensitivity of 96.7% (202/209), a specificity of 92.2% (153/166), a positive predictive value of 94.0%, and a negative predictive value of 95.6%, with 21 samples (5.3%) classified as indeterminate for BV. This assay provides a reproducible and objective means of evaluating critical components of the vaginal microflora in women with signs and symptoms of vaginitis and is comparable in diagnostic accuracy to the conventional gold standard for diagnosis of BV.
| S-EPMC3405607 | biostudies-literature