总结
      肺炎克雷伯菌已成为全球临床上导致高死亡率的头号细菌病原体。临床K。 与经典K型相比，具有碳青霉烯耐药性和/或高通气表型的肺炎菌株导致更高的死亡率。 肺炎菌株。临床K的快速分化。 来自经典肺炎克雷伯菌的高耐药性/高通气性肺炎将使我们能够制定合理及时的治疗计划。在这项研究中，我们开发了一种卷积神经网络（CNN）作为一种预测方法，使用拉曼光谱原始数据快速识别K的ARGs、高通气编码因子和抗性表型。 肺炎菌株。总共71 K。 本研究包括肺炎菌株。15种常用抗菌剂对K。 确定了肺炎菌株。使用InVia Reflex共焦拉曼显微镜获得七千四百五十五个光谱，并用于基于深度学习和机器学习（ML）算法的分析。预测因子的质量是在一个独立的数据集中估计的。抗生素耐药性和毒力编码因子鉴定结果表明，CNN模型不仅简化了拉曼光谱的分类系统，而且为鉴定K提供了更高的准确性。 与支持向量机（SVM）和逻辑回归（LR）模型相比，肺炎具有高耐药性和毒力。通过在71K上对拉曼CNN平台进行反向测试。 肺炎菌株，我们发现拉曼光谱可以在数小时内实现高度准确和合理设计的细菌感染治疗计划。更重要的是，这种方法可以降低医疗成本和抗生素滥用，限制抗生素耐药性的发展，改善患者的预后。
Summary
Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K. pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing with classical K. pneumoniae strains. Rapid differentiation of clinical K. pneumoniae with high resistance/hypervirulence from classical K. pneumoniae would allow us to develop rational and timely treatment plans. In this study, we developed a convolution neural network (CNN) as a prediction method using Raman spectra raw data for rapid identification of ARGs, hypervirulence-encoding factors and resistance phenotypes from K. pneumoniae strains. A total of 71 K. pneumoniae strains were included in this study. The minimum inhibitory concentrations (MICs) of 15 commonly used antimicrobial agents on K. pneumoniae strains were determined. Seven thousand four hundred fifty-five spectra were obtained using the InVia Reflex confocal Raman microscope and used for deep learning-based and machine learning (ML) algorithms analyses. The quality of predictors was estimated in an independent data set. The results of antibiotic resistance and virulence-encoding factors identification showed that the CNN model not only simplified the classification system for Raman spectroscopy but also provided significantly higher accuracy to identify K. pneumoniae with high resistance and virulence when compared with the support vector machine (SVM) and logistic regression (LR) models. By back-testing the Raman-CNN platform on 71 K. pneumoniae strains, we found that Raman spectroscopy allows for highly accurate and rationally designed treatment plans against bacterial infections within hours. More importantly, this method could reduce healthcare costs and antibiotics misuse, limiting the development of antimicrobial resistance and improving patient outcomes.

https://ami-journals.onlinelibrary.wiley.com/doi/full/10.1111/1751-7915.13960