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基于 MALDI-TOF 质谱和统计模型的 B 群链球菌多位点序列分型的快速分类。

Rapid Classification of Multilocus Sequence Subtype for Group B Based on MALDI-TOF Mass Spectrometry and Statistical Models.

机构信息

Clinical Laboratory, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.

Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmacy, Xi'an Jiaotong University, Xi'an, China.

出版信息

Front Cell Infect Microbiol. 2021 Jan 29;10:577031. doi: 10.3389/fcimb.2020.577031. eCollection 2020.

DOI:10.3389/fcimb.2020.577031
PMID:33585264
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7878539/
Abstract

Group B (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.

摘要

B 群链球菌(GBS)是一种重要的病原体,可导致母婴和新生儿感染,以及产后妇女和免疫功能受损的个体感染。我们开发并评估了一种基于基质辅助激光解吸/电离飞行时间质谱(MALDI-TOF/MS)统计模型的快速 GBS 序列型(ST)分类方法。对 2015 年至 2017 年在中国多中心研究中来自新生儿侵袭性感染的 235 株典型 GBS 分离株进行了全细胞裂解物 MALDI-TOF/MS 分析。选择主要 ST(ST10、ST12、ST17、ST19、ST23)的质谱用于模型生成和验证。通过遗传算法-K 最近邻(GA-KNN)、监督神经网络(SNN)、QuickClassifier(QC)计算识别和交叉验证值,以选择用于验证诊断效率的最佳性能模型。通过峰统计分析、ST 亚型 MSP 峰数据和质谱可视化进一步筛选信息峰。对于主要 ST,与 SNN 和 QC 算法相比,GA-KNN 算法生成的 ML 模型具有最高的交叉验证值。GA-KNN 模型对 ST10、ST17 和 ST12/ST19 具有良好的诊断效率,具有高灵敏度(95-100%)、特异性(91.46%-99.23%)、准确性(92.79-99.29%)、阳性预测值(PPV,80%-92.68%)和阴性预测值(NPV,94.32%-99.23%)。首次为 ST10 菌株(m/z 6250、3125、6891)和 ST17 菌株(m/z 2956、5912、7735、5218)鉴定了峰标记。使用 MALDI-TOF/MS 光谱法进行快速 GBS ST 分型的统计模型有助于更容易地对 GBS 感染性疾病进行流行病学分子监测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbd8/7878539/4aa42cb67a7b/fcimb-10-577031-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbd8/7878539/749b0caed0ed/fcimb-10-577031-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbd8/7878539/a59f0aeeb2f4/fcimb-10-577031-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbd8/7878539/4aa42cb67a7b/fcimb-10-577031-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbd8/7878539/749b0caed0ed/fcimb-10-577031-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbd8/7878539/a59f0aeeb2f4/fcimb-10-577031-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbd8/7878539/4aa42cb67a7b/fcimb-10-577031-g003.jpg

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