A Facile machine learning multi-classification model for Streptococcus agalactiae clonal complexes.

ABSTRACT:

Background

The clinical significance of group B streptococcus (GBS) was different among different clonal complexes (CCs), accurate strain typing of GBS would facilitate clinical prognostic evaluation, epidemiological investigation and infection control. The aim of this study was to construct a practical and facile CCs prediction model for S. agalactiae.

Methods

A total of 325 non-duplicated GBS strains were collected from clinical samples in Xinhua Hospital, Shanghai, China. Multilocus sequence typing (MLST) method was used for molecular classification, the results were analyzed to derive CCs by Bionumeric 8.0 software. Antibiotic susceptibility test was performed using Vitek-2 Compact system combined with K-B method. Multiplex PCR method was used for serotype identification

SUBMITTER: Liu J 

PROVIDER: S-EPMC9675200 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature