Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience
BackgroundBacterial 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 evalua...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2024.1377225/full |
| _version_ | 1797221692978757632 |
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| author | Sihai Lu Sihai Lu Zhuo Li Zhuo Li Xinyue Chen Xinyue Chen Fengshuangze Chen Fengshuangze Chen Hao Yao Xuena Sun Yimin Cheng Liehong Wang Penggao Dai Penggao Dai |
| author_facet | Sihai Lu Sihai Lu Zhuo Li Zhuo Li Xinyue Chen Xinyue Chen Fengshuangze Chen Fengshuangze Chen Hao Yao Xuena Sun Yimin Cheng Liehong Wang Penggao Dai Penggao Dai |
| author_sort | Sihai Lu |
| collection | DOAJ |
| description | BackgroundBacterial 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.MethodsResidual 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.ResultsThe 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.ConclusionThe 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. |
| first_indexed | 2024-04-24T13:09:29Z |
| format | Article |
| id | doaj.art-54771003d7064e5881a178b8ddf2d309 |
| institution | Directory Open Access Journal |
| issn | 2235-2988 |
| language | English |
| last_indexed | 2024-04-24T13:09:29Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj.art-54771003d7064e5881a178b8ddf2d3092024-04-05T04:50:39ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882024-04-011410.3389/fcimb.2024.13772251377225Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experienceSihai Lu0Sihai Lu1Zhuo Li2Zhuo Li3Xinyue Chen4Xinyue Chen5Fengshuangze Chen6Fengshuangze Chen7Hao Yao8Xuena Sun9Yimin Cheng10Liehong Wang11Penggao Dai12Penggao Dai13National Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi’an, ChinaDepartment of Research and Development, Shaanxi Lifegen Co., Ltd., Xi’an, ChinaNational Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi’an, ChinaClinical Laboratory, The First Affiliated Hospital of Xi’an Medical University, Xi’an, ChinaNational Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi’an, ChinaDepartment of Research and Development, Shaanxi Lifegen Co., Ltd., Xi’an, ChinaNational Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi’an, ChinaAcademic Center, Henry M Gunn High School, Palo Alto, CA, United StatesNational Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi’an, ChinaDepartment of Research and Development, Shaanxi Lifegen Co., Ltd., Xi’an, ChinaDepartment of Obstetrics and Gynecology, The Hospital of Xi’ an Shiyou University, Xi’an, ChinaDepartment of Obstetrics and Gynecology, Qinghai Red Cross Hospital, Qinghai, Xining, ChinaNational Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi’an, ChinaDepartment of Research and Development, Shaanxi Lifegen Co., Ltd., Xi’an, ChinaBackgroundBacterial 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.MethodsResidual 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.ResultsThe 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fcimb.2024.1377225/fullBV diagnosismachine learningmPCRLactobacillus spp.CST |
| spellingShingle | Sihai Lu Sihai Lu Zhuo Li Zhuo Li Xinyue Chen Xinyue Chen Fengshuangze Chen Fengshuangze Chen Hao Yao Xuena Sun Yimin Cheng Liehong Wang Penggao Dai Penggao Dai Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience Frontiers in Cellular and Infection Microbiology BV diagnosis machine learning mPCR Lactobacillus spp. CST |
| title | Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience |
| title_full | Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience |
| title_fullStr | Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience |
| title_full_unstemmed | Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience |
| title_short | Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience |
| title_sort | vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence assisted multiplex pcr testing in women with bacterial vaginosis a single center experience |
| topic | BV diagnosis machine learning mPCR Lactobacillus spp. CST |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2024.1377225/full |
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