Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.
Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.
复制标题
DOI：
10.1038/s41598-021-97882-4
复制
发表时间：
2021-09-16
影响因子：
4.6
通讯作者：
Aydin O
中科院分区：
文献类型：
--
作者：
Ciloglu FU;Caliskan A;Saridag AM;Kilic IH;Tokmakci M;Kahraman M;Aydin O
在过去的一年里，全世界的注意力都集中在抗击COVID-19疾病上，但另一个在门口等待的威胁--抗菌素耐药性也不应被遗忘。虽然快速准确地进行诊断对于防止抗生素耐药性的发展至关重要，但细菌鉴定技术包括一些具有挑战性的过程。为了应对这一挑战，我们提出了一种深度神经网络（DNN），可以使用表面增强拉曼光谱（Sers）来区分耐药细菌。采用基于堆叠自动编码器（SAE）的动态神经网络对耐甲氧西林金黄色葡萄球菌（MRSA）和甲氧西林敏感的金黄色葡萄球菌（S。金黄色葡萄球菌（MSSA）细菌的表面增强拉曼光谱（Sers）。DNN的性能与传统的分类器进行了比较。由于Sers技术提供了高信噪比（SNR）的数据，MRSA和MSSA之间的相对条带强度发现了一些细微的差异。基于SAE的DNN可以从原始数据中学习特征，并以97.66%的准确率对其进行分类。此外，该模型以0.99的曲线下面积（AUC）区分细菌。与传统分类器相比，基于SAE的DNN在准确性和AUC值方面具有上级优势。所得结果也得到了统计分析的支持。这些结果表明，深度学习具有通过使用Sers光谱数据来表征和检测耐药细菌的巨大潜力。
Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.
登录
查看更多内容
DOI：
10.1016/j.saa.2017.04.044
发表时间：
2017-08-05
影响因子：
4.4
作者：
Cheong, Youjin;Kim, Young Jin;Lee, Hee Joo
通讯作者：
Lee, Hee Joo
影响因子：
2.4
作者：
Kneipp, K;Kneipp, H;Feld, MS
通讯作者：
Feld, MS
影响因子：
2.5
作者：
Efrima, S.;Zeiri, L.
通讯作者：
Zeiri, L.
影响因子：
7.4
作者：
Jarvis, RM;Goodacre, R
通讯作者：
Goodacre, R
影响因子：
120.7
作者：
Fleming-Dutra, Katherine E.;Hersh, Adam L.;Hicks, Lauri A.
通讯作者：
Hicks, Lauri A.