Vasconcelos Thaís Muniz, Rodrigues Luiza Souza, Krul Damaris, Barbosa Sabrina da Conceição, Siqueira Adriele Celine, Almeida Samanta Cristine Grassi, Pacheco Souza Ana Paula de Oliveira, Pillonetto Marcelo, Oliveira Rodrigo, Moonen Carolyn Gertruda Josephina, Siebra Christian de Alencar, Dalla-Costa Libera Maria
Faculdades Pequeno Príncipe, Curitiba, Paraná, Brazil.
Instituto de Pesquisa Pelé Pequeno Príncipe, Curitiba, Paraná, Brazil.
Front Microbiol. 2024 Nov 21;15:1497377. doi: 10.3389/fmicb.2024.1497377. eCollection 2024.
Invasive pneumococcal disease (IPD) is a major cause of morbidity and mortality worldwide, particularly in the pediatric population (children and infants), with high rates of hospitalization and death. This study aimed to create and validate a classifier for serotyping using Fourier-transform infrared (FT-IR) spectroscopy as a rapid alternative to the classical serotyping technique. In this study, a database comprising 76 clinical isolates, including 18 serotypes (predominantly serotypes 19A, 6C, and 3) of from pediatric patients with IPD, was tested at a tertiary pediatric hospital in southern Brazil during 2016-2023. All isolates were previously serotyped using the Quellung reaction, and 843 FT-IR spectra were obtained to create a classification model using artificial neural network (ANN) machine learning. After the creation of this classifier, internal validation was performed using 384 spectra as the training dataset and 459 as the testing dataset, resulting in a predictive accuracy of 98% for serotypes 19A, 6, 3, 14, 18C, 22F, 23A, 23B, 33F, 35B, and 9N. In this dataset, serotypes 10A/16F, 15ABC, and 7CF could not be differentiated and were, therefore, grouped as labels. FT-IR is a promising, rapid, and low-cost method for the phenotypic classification of capsular serotypes. This methodology has significant implications for clinical and epidemiological practice, improving patient management, monitoring infection trends, and developing new vaccines.
侵袭性肺炎球菌疾病（IPD）是全球发病和死亡的主要原因，尤其是在儿童群体（儿童和婴儿）中，住院率和死亡率很高。本研究旨在创建并验证一种使用傅里叶变换红外（FT-IR）光谱进行血清分型的分类器，作为传统血清分型技术的快速替代方法。在本研究中，2016年至2023年期间，在巴西南部的一家三级儿童医院对一个包含76株临床分离株的数据库进行了测试，这些分离株来自患有IPD的儿科患者，包括18种血清型（主要是19A、6C和3型）。所有分离株先前都使用荚膜肿胀反应进行了血清分型，并获得了843个FT-IR光谱，以使用人工神经网络（ANN）机器学习创建分类模型。创建此分类器后，使用384个光谱作为训练数据集，459个光谱作为测试数据集进行内部验证，结果显示19A、6、3、14、18C、22F、23A、23B、33F、35B和9N型血清型的预测准确率为98%。在该数据集中，10A/16F、15ABC和7CF型血清型无法区分，因此被归为一组标签。FT-IR是一种用于荚膜血清型表型分类的有前景、快速且低成本的方法。该方法对临床和流行病学实践具有重要意义，可改善患者管理、监测感染趋势并开发新疫苗。
