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      Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience

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          Abstract

          Background

          Bacterial vaginosis (BV) is a most common microbiological syndrome. The use of molecular methods, such as multiplex real-time PCR (mPCR) and next-generation sequencing, has revolutionized our understanding of microbial communities. Here, we aimed to use a novel multiplex PCR test to evaluate the microbial composition and dominant lactobacilli in non-pregnant women with BV, and combined with machine learning algorithms to determine its diagnostic significance.

          Methods

          Residual material of 288 samples of vaginal secretions derived from the vagina from healthy women and BV patients that were sent for routine diagnostics was collected and subjected to the mPCR test. Subsequently, Decision tree (DT), random forest (RF), and support vector machine (SVM) hybrid diagnostic models were constructed and validated in a cohort of 99 women that included 74 BV patients and 25 healthy controls, and a separate cohort of 189 women comprising 75 BV patients, 30 intermediate vaginal microbiota subjects and 84 healthy controls, respectively.

          Results

          The rate or abundance of Lactobacillus crispatus and Lactobacillus jensenii were significantly reduced in BV-affected patients when compared with healthy women, while Lactobacillus iners, Gardnerella vaginalis, Atopobium vaginae, BVAB2, Megasphaera type 2, Prevotella bivia, and Mycoplasma hominis were significantly increased. Then the hybrid diagnostic models were constructed and validated by an independent cohort. The model constructed with support vector machine algorithm achieved excellent prediction performance (Area under curve: 0.969, sensitivity: 90.4%, specificity: 96.1%). Moreover, for subjects with a Nugent score of 4 to 6, the SVM-BV model might be more robust and sensitive than the Nugent scoring method.

          Conclusion

          The application of this mPCR test can be effectively used in key vaginal microbiota evaluation in women with BV, intermediate vaginal microbiota, and healthy women. In addition, this test may be used as an alternative to the clinical examination and Nugent scoring method in diagnosing BV.

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          Most cited references36

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          Vaginal microbiome of reproductive-age women.

          The means by which vaginal microbiomes help prevent urogenital diseases in women and maintain health are poorly understood. To gain insight into this, the vaginal bacterial communities of 396 asymptomatic North American women who represented four ethnic groups (white, black, Hispanic, and Asian) were sampled and the species composition characterized by pyrosequencing of barcoded 16S rRNA genes. The communities clustered into five groups: four were dominated by Lactobacillus iners, L. crispatus, L. gasseri, or L. jensenii, whereas the fifth had lower proportions of lactic acid bacteria and higher proportions of strictly anaerobic organisms, indicating that a potential key ecological function, the production of lactic acid, seems to be conserved in all communities. The proportions of each community group varied among the four ethnic groups, and these differences were statistically significant [χ(2)(10) = 36.8, P < 0.0001]. Moreover, the vaginal pH of women in different ethnic groups also differed and was higher in Hispanic (pH 5.0 ± 0.59) and black (pH 4.7 ± 1.04) women as compared with Asian (pH 4.4 ± 0.59) and white (pH 4.2 ± 0.3) women. Phylotypes with correlated relative abundances were found in all communities, and these patterns were associated with either high or low Nugent scores, which are used as a factor for the diagnosis of bacterial vaginosis. The inherent differences within and between women in different ethnic groups strongly argues for a more refined definition of the kinds of bacterial communities normally found in healthy women and the need to appreciate differences between individuals so they can be taken into account in risk assessment and disease diagnosis.
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            Reliability of diagnosing bacterial vaginosis is improved by a standardized method of gram stain interpretation.

            The purpose of the study was to examine intercenter variability in the interpretation of Gram-stained vaginal smears from pregnant women. The intercenter reliability of individual morphotypes identified on the vaginal smear was evaluated by comparing them with those obtained at a standard center. A new scoring system that uses the most reliable morphotypes from the vaginal smear was proposed for diagnosing bacterial vaginosis. This scoring system was compared with the Spiegel criteria for diagnosing bacterial vaginosis. The scoring system (0 to 10) was described as a weighted combination of the following morphotypes: lactobacilli, Gardnerella vaginalis or bacteroides (small gram-variable rods or gram-negative rods), and curved gram-variable rods. By using the Spearman rank correlation to determine intercenter variability, gram-positive cocci had poor agreement (0.23); lactobacilli (0.65), G. vaginalis (0.69), and bacteroides (0.57) had moderate agreement; and small (0.74) and curved (0.85) gram-variable rods had good agreement. The reliability of the 0 to 10 scoring system was maximized by not using gram-positive cocci, combining G. vaginalis and bacteroides morphotypes, and weighting more heavily curved gram-variable rods. For comparison with the Spiegel criteria, a score of 7 or higher was considered indicative of bacterial vaginosis. The standardized score had improved intercenter reliability (r = 0.82) compared with the Spiegel criteria (r = 0.61). The standardized score also facilitates future research concerning bacterial vaginosis because it provides gradations of the disturbance of vaginal flora which may be associated with different levels of risk for pregnancy complications.
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              Molecular identification of bacteria associated with bacterial vaginosis.

              Bacterial vaginosis affects millions of women and is associated with several serious health conditions. The cause of bacterial vaginosis remains poorly understood despite numerous studies based on cultures. Bacteria in microbial communities can be identified without cultivation by characterizing their ribosomal DNA (rDNA) sequences. We identified bacteria in samples of vaginal fluid with a combination of broad-range polymerase-chain-reaction (PCR) amplification of 16S rDNA with clone analysis, bacterium-specific PCR assay of 16S rDNA, and fluorescence in situ hybridization (FISH) performed directly on vaginal fluid from 27 subjects with bacterial vaginosis and 46 without the condition. Twenty-one subjects were studied with the use of broad-range PCR of 16S rDNA, and 73 subjects were studied with the use of bacterium-specific PCR. Women without bacterial vaginosis had 1 to 6 vaginal bacterial species (phylotypes) in each sample (mean, 3.3), as detected by broad-range PCR of 16S rDNA, and lactobacillus species were the predominant bacteria noted (83 to 100 percent of clones). Women with bacterial vaginosis had greater bacterial diversity (P<0.001), with 9 to 17 phylotypes (mean, 12.6) detected per sample and newly recognized species present in 32 to 89 percent of clones per sample library (mean, 58 percent). Thirty-five unique bacterial species were detected in the women with bacterial vaginosis, including several species with no close cultivated relatives. Bacterium-specific PCR assays showed that several bacteria that had not been previously described were highly prevalent in subjects with bacterial vaginosis but rare in healthy controls. FISH confirmed that newly recognized bacteria detected by PCR corresponded to specific bacterial morphotypes visible in vaginal fluid. Women with bacterial vaginosis have complex vaginal infections with many newly recognized species, including three bacteria in the Clostridiales order that were highly specific for bacterial vaginosis. Copyright 2005 Massachusetts Medical Society.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/328208Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1642074Role: Role: Role: Role:
                Role: Role: Role:
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                URI : https://loop.frontiersin.org/people/558044Role: Role: Role: Role: Role:
                Journal
                Front Cell Infect Microbiol
                Front Cell Infect Microbiol
                Front. Cell. Infect. Microbiol.
                Frontiers in Cellular and Infection Microbiology
                Frontiers Media S.A.
                2235-2988
                05 April 2024
                2024
                : 14
                : 1377225
                Affiliations
                [1] 1 National Engineering Research Center for Miniaturized Detection Systems, Northwest University , Xi’an, China
                [2] 2 Department of Research and Development, Shaanxi Lifegen Co., Ltd. , Xi’an, China
                [3] 3 Clinical Laboratory, The First Affiliated Hospital of Xi’an Medical University , Xi’an, China
                [4] 4 Academic Center, Henry M Gunn High School , Palo Alto, CA, United States
                [5] 5 Department of Obstetrics and Gynecology, The Hospital of Xi’ an Shiyou University , Xi’an, China
                [6] 6 Department of Obstetrics and Gynecology, Qinghai Red Cross Hospital , Qinghai, Xining, China
                Author notes

                Edited by: Ryan Steven Doster, University of Louisville, United States

                Reviewed by: Francesco De Seta, Institute for Maternal and Child Health Burlo Garofolo (IRCCS), Italy

                Salome N. Seiffert, Zentrum für Labormedizin (ZLM), Switzerland

                *Correspondence: Penggao Dai, daipg@ 123456nwu.edu.cn ; Liehong Wang, 15509719@ 123456qq.com

                †These authors have contributed equally to this work and share first authorship

                Article
                10.3389/fcimb.2024.1377225
                11026559
                38644962
                44a1d4f2-74c1-4148-a6ca-0e6f2ca5be09
                Copyright © 2024 Lu, Li, Chen, Chen, Yao, Sun, Cheng, Wang and Dai

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 January 2024
                : 21 March 2024
                Page count
                Figures: 5, Tables: 2, Equations: 0, References: 36, Pages: 13, Words: 5939
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Key R & D Program of Shaanxi Province (2022ZDXM–SF–05).
                Categories
                Cellular and Infection Microbiology
                Original Research
                Custom metadata
                Clinical Infectious Diseases

                Infectious disease & Microbiology
                bv diagnosis,machine learning,mpcr, lactobacillus spp.,cst
                Infectious disease & Microbiology
                bv diagnosis, machine learning, mpcr, lactobacillus spp., cst

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