摘要
      抗微生物耐药性是全球范围内日益严重的公共卫生问题，已有700多人 000人因抗生素耐药性细菌引起的感染而死亡。为了应对这一挑战，重要的是根据有关细菌病原体的物种特征及其抗微生物耐药性基因型和表型的数据设计适当的治疗方案。在此，开发了一种利用人工智能分析拉曼光谱的新方法，以在基因型和表型水平上识别微生物及其对常用抗生素的易感性。本研究共包括130株肠球菌和头葡萄球菌，它们具有已知的常用抗菌剂的最低抑菌浓度（MIC）。在配置和训练模型后，开发了基于长短期记忆（LSTM）的拉曼平台，发现该平台能够提供89.9的精度 ± 1.1%, 82.4 ± 在细菌种类分类、抗微生物耐药性基因鉴定和耐药性表型预测方面，分别为0.6%和60.4–89.2%。这种新方法比使用机器学习算法的方法具有更高的准确性。结果表明，拉曼光谱与LSTM分析相结合可用于快速细菌物种鉴定、ARGs检测和耐药性表型评估。
Abstract
Antimicrobial resistance is an aggravating public health problem worldwide, with more than 700 000 deaths attributable to infections caused by antibiotic-resistant bacteria annually. To tackle this challenge, it is important to design appropriate regimens based on data regarding the species identity of bacterial pathogen concerned, as well as their antimicrobial-resistance genotypes and phenotypes. Herein, a novel method that utilizes artificial intelligence to analyze Raman spectra to identify microbes and their susceptibility to commonly used antibiotics at both genotype and phenotype level is developed. A total of 130 strains of Enterococcus spp. and Staphylococcus capitis with known minimum inhibitory concentrations (MICs) of commonly used antimicrobial agents are included in this study. After the models are configured and trained, long short-term memory (LSTM) based Raman platform is developed and is found to be able to offer an accuracy of 89.9 ± 1.1%, 82.4 ± 0.6%, and 60.4–89.2% in bacterial species classification, identification of antimicrobial-resistance genes (ARGs), and prediction of resistance phenotypes, respectively. This novel method exhibits higher level of accuracy than those using the machine learning algorithms. The results indicate that Raman spectroscopy combined with LSTM analysis can be used for rapid bacterial species identification, detection of ARGs, and assessment of drug-resistance phenotypes.

https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202200235