利用卷积神经网络增强的拉曼光谱技术快速识别血流感染病原体和耐药性

Haiquan Kang, Ziling Wang, Jingfang Sun, Shuang Song, Lei Cheng, Yi Sun, Xingqi Pan, Changyu Wu, Ping Gong, Hongchun Li
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摘要

血流感染(BSI)是一个重要的医学问题,其特点是发病率和死亡率升高、住院时间延长、医疗成本高昂以及诊断困难。通过及时识别致病病原体及其对抗生素和抗菌药的敏感性,可以显著改善 BSI 患者的临床治疗效果。传统的 BSI 诊断方法是通过血液培养,但由于培养潜伏期较长,而且在检测病原菌及其耐药性方面存在局限性,因此往往难以达到诊断目的。最近,表面增强拉曼散射(SERS)作为一种快速、有效的鉴定病原菌和评估耐药性的技术而备受瞩目。这种方法具有快速、灵敏和非破坏性等优点,可进行分子指纹识别。本研究的目的是将深度学习(DL)与 SERS 相结合,用于快速鉴定 BSI 中的常见病原体及其耐药性。为了评估将深度学习与 SERS 结合用于直接检测的可行性,研究人员采用了红细胞裂解和差速离心法,从血液培养阳性的血液样本中分离细菌。使用拉曼光谱从这两种方法中分别收集了 12,046 和 11,968 个 SERS 光谱,随后使用 DL 算法进行了分析。研究结果表明,卷积神经网络(CNN)在识别流行病原体及其耐药菌株方面具有相当大的潜力。在从血液中分离细菌方面,差速离心技术优于红细胞裂解技术,对病原菌的检测准确率达 98.68%,在识别耐碳青霉烯类肺炎克雷伯菌方面的准确率高达 99.85%,令人印象深刻。总之,这项研究成功地开发了一种创新方法,将 DL 与 SERS 相结合,用于快速鉴定 BSIs 中的病原菌及其耐药性。这种新方法有望大大改善患者的预后并优化医疗效率。它的潜在影响可能是深远的,有可能改变 BSIs 的诊断和治疗格局。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks
Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.
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