Lu Sihai, Li Zhuo, Chen Xinyue, Chen Fengshuangze, Yao Hao, Sun Xuena, Cheng Yimin, Wang Liehong, Dai Penggao
National Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi'an, China.
Department of Research and Development, Shaanxi Lifegen Co., Ltd., Xi'an, China.
Front Cell Infect Microbiol. 2024 Apr 5;14:1377225. doi: 10.3389/fcimb.2024.1377225. eCollection 2024.
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.
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.
The rate or abundance of and were significantly reduced in BV-affected patients when compared with healthy women, while , , , BVAB2, 2, , and 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.
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.
细菌性阴道病（BV）是一种最常见的微生物综合征。多重实时聚合酶链反应（mPCR）和下一代测序等分子方法的应用彻底改变了我们对微生物群落的认识。在此，我们旨在使用一种新型多重聚合酶链反应检测来评估非妊娠BV女性的微生物组成和优势乳酸杆菌，并结合机器学习算法确定其诊断意义。
收集288份来自健康女性和BV患者阴道分泌物的残留样本，这些样本被送去进行常规诊断，并进行mPCR检测。随后，构建决策树（DT）、随机森林（RF）和支持向量机（SVM）混合诊断模型，并在一个由99名女性组成的队列中进行验证，该队列包括74名BV患者和25名健康对照，以及另一个由189名女性组成的队列，分别包括75名BV患者、30名中间型阴道微生物群受试者和84名健康对照。
与健康女性相比，受BV影响的患者中 和 的比例或丰度显著降低，而 、 、 、BVAB2、 2、 和 显著增加。然后通过独立队列构建并验证混合诊断模型。采用支持向量机算法构建的模型具有出色的预测性能（曲线下面积：0.969，灵敏度：90.4%，特异性：96.1%）。此外，对于Nugent评分为4至6分的受试者，SVM - BV模型可能比Nugent评分方法更稳健、更敏感。
这种mPCR检测可有效用于评估BV女性、中间型阴道微生物群女性和健康女性的关键阴道微生物群。此外，该检测可作为诊断BV的临床检查和Nugent评分方法的替代方法。
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