Lyu Jing-Wen, Zhang Xue Di, Tang Jia-Wei, Zhao Yun-Hu, Liu Su-Ling, Zhao Yue, Zhang Ni, Wang Dan, Ye Long, Chen Xiao-Li, Wang Liang, Gu Bing
Department of Laboratory Medicine, School of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu Province, China.
Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
Microbiol Spectr. 2023 Mar 6;11(2):e0412622. doi: 10.1128/spectrum.04126-22.
Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; = 21), carbapenem-resistant K. pneumoniae, (CRKP; = 50), and carbapenem-sensitive K. pneumoniae (CSKP; = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.
肺炎克雷伯菌被世界卫生组织列为极其重要的优先病原体，可在临床环境中导致严重后果。由于其在全球范围内日益增加的多重耐药性，肺炎克雷伯菌有可能引发极难治疗的感染。因此，在临床诊断中快速准确地鉴定多重耐药肺炎克雷伯菌对于其预防和感染控制至关重要。然而，传统方法和分子方法的局限性严重阻碍了该病原体的及时诊断。作为一种无标记、非侵入性且低成本的方法，表面增强拉曼散射（SERS）光谱因其在微生物病原体诊断中的应用潜力而受到广泛研究。在本研究中，我们从具有不同耐药谱的临床样本中分离并培养了121株肺炎克雷伯菌菌株，其中包括耐多粘菌素肺炎克雷伯菌（PRKP；n = 21）、耐碳青霉烯类肺炎克雷伯菌（CRKP；n = 50）和碳青霉烯类敏感肺炎克雷伯菌（CSKP；n = 50）。对于每株菌株，总共生成了64个SERS光谱以提高数据重现性，然后通过卷积神经网络（CNN）进行计算分析。结果表明，深度学习模型CNN加注意力机制可实现高达99.46%的预测准确率，5折交叉验证的稳健性得分达98.87%。综上所述，我们的结果证实了SERS光谱在深度学习算法辅助下预测肺炎克雷伯菌菌株耐药性的准确性和稳健性，该算法成功区分并预测了PRKP、CRKP和CSKP菌株。本研究重点在于同时鉴别和预测对碳青霉烯类敏感、耐碳青霉烯类和耐多粘菌素表型的肺炎克雷伯菌菌株。CNN加注意力机制的实施使预测准确率最高达到99.46%，这证实了SERS光谱与深度学习算法相结合在临床环境中进行抗菌药敏试验的诊断潜力。
