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Lookup NU author(s): Dr Chien-Yi ChangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© Copyright © 2021 Huang, Gao, Chen, Zhong, Li, Guan, Deng, Xie, Ji, McIver, Chang and Liu.Group B Streptococcus (GBS) is an important etiological agent of maternal and neonatal infections as well as postpartum women and individuals with impaired immunity. We developed and evaluated a rapid classification method for sequence types (STs) of GBS based on statistic models with Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF/MS). Whole-cell lysates MALDI-TOF/MS analysis was performed on 235 well-characterized GBS isolates from neonatal invasive infections in a multi-center study in China between 2015 and 2017. Mass spectra belonging to major STs (ST10, ST12, ST17, ST19, ST23) were selected for model generation and validation. Recognition and cross validation values were calculated by Genetic Algorithm-K Nearest Neighbor (GA-KNN), Supervised Neural Network (SNN), QuickClassifier (QC) to select models with the best performance for validation of diagnostic efficiency. Informative peaks were further screened through peak statistical analysis, ST subtyping MSP peak data and mass spectrum visualization. For major STs, the ML models generated by GA-KNN algorithms attained highest cross validation values in comparison to SNN and QC algorithms. GA-KNN models of ST10, ST17, and ST12/ST19 had good diagnostic efficiency, with high sensitivity (95–100%), specificity (91.46%–99.23%), accuracy (92.79–99.29%), positive prediction value (PPV, 80%–92.68%), negative prediction value (NPV, 94.32%–99.23%). Peak markers were firstly identified for ST10 (m/z 6250, 3125, 6891) and ST17 strains (m/z 2956, 5912, 7735, 5218). Statistical models for rapid GBS ST subtyping using MALDI-TOF/MS spectrometry contributes to easier epidemical molecular monitoring of GBS infection diseases.
Author(s): Huang L, Gao K, Chen G, Zhong H, Li Z, Guan X, Deng Q, Xie Y, Ji W, McIver DJ, Chang C-Y, Liu H
Publication type: Article
Publication status: Published
Journal: Frontiers in Cellular and Infection Microbiology
Online publication date: 29/01/2021
Acceptance date: 11/12/2020
Date deposited: 07/04/2021
ISSN (electronic): 2235-2988
Publisher: Frontiers Media S.A.
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