Rapid Raman spectroscopy-based test for antimicrobial resistance

被引:0
作者
Mushenkov, Vladimir [1 ]
Zhigalova, Ksenia [2 ]
Denisov, Pavel [2 ]
Gordeev, Alexey [2 ]
Lukyanov, Dmitry [1 ,3 ]
Kukushkin, Vladimir [4 ]
Priputnevich, Tatiana [2 ]
Zavyalova, Elena [1 ]
机构
[1] Lomonosov Moscow State Univ, Chem Dept, Moscow, Russia
[2] Minist Healthcare Russian Federat, Natl Med Res Ctr Obstet Gynecol & Perinatol, Moscow, Russia
[3] Skolkovo Inst Sci & Technol, Ctr Mol & Cellular Biol, Moscow, Russia
[4] Russian Acad Sci, Osipyan Inst Solid State Phys, Chernogolovka, Russia
基金
俄罗斯科学基金会;
关键词
antibiotics; antimicrobial resistance; MTT; Raman spectroscopy; 3-(4,5-DIMETHYLTHIAZOL-2-YL)-2,5-DIPHENYL TETRAZOLIUM BROMIDE; MTT ASSAY; REDUCTION;
D O I
10.1098/rsob.240258
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Antimicrobial resistance (AMR) is one of the top global health threats. In 2019, AMR was associated with 4.95 million deaths, of which 1.97 million were caused by drug-resistant infections directly. The main subset of AMR is antibiotic resistance, that is, the resistance of bacteria to antibiotic treatment. Traditional and most commonly used antibiotic susceptibility tests are based on the detection of bacterial growth and its inhibition in the presence of an antimicrobial. These tests typically take over 1-2 days to perform, so empirical therapy schemes are often administered before proper testing. Rapid tests for AMR are necessary to optimize the treatment of bacterial infection. Here, we combine the MTT test with Raman spectroscopy to provide a 1.5 h long test for minimal inhibitory concentration determination. Several Escherichia coli and Klebsiella pneumoniae strains were tested with three types of antibiotics, including ampicillin from penicillin family, kanamycin from aminoglycoside family and levofloxacin from fluoroquinolone family. The test provided the same minimal inhibitory concentrations as traditional Etest confirming its robustness.
引用
收藏
页数:10
相关论文
共 50 条
[21]   Raman spectroscopy-based diagnostics of water deficit and salinity stresses in two accessions of peanut [J].
Morey, Rohini ;
Farber, Charles ;
McCutchen, Bill ;
Burow, Mark D. ;
Simpson, Charles ;
Kurouski, Dmitry ;
Cason, John .
PLANT DIRECT, 2021, 5 (08)
[22]   Modified PCA and PLS: Towards a better classification in Raman spectroscopy-based biological applications [J].
Guo, Shuxia ;
Roesch, Petra ;
Popp, Juergen ;
Bocklitz, Thomas .
JOURNAL OF CHEMOMETRICS, 2020, 34 (04)
[23]   Combating Antimicrobial Resistance: Spectroscopy Meets Machine Learning [J].
Saikia, Dimple ;
Dadhara, Ritam ;
Tanan, Cebajel ;
Avati, Prajwal ;
Verma, Tushar ;
Pandey, Rishikesh ;
Singh, Surya Pratap .
PHOTONICS, 2025, 12 (07)
[24]   Beyond images: Emerging role of Raman spectroscopy-based artificial intelligence in diagnosis of gastric neoplasia [J].
Ho, Khek Yu .
CHINESE JOURNAL OF CANCER RESEARCH, 2022, 34 (05) :539-542
[25]   Raman spectroscopy-based water content is a negative predictor of articular human cartilage mechanical function [J].
Unal, M. ;
Akkus, O. ;
Sun, J. ;
Cai, L. ;
Erol, U. L. ;
Sabri, L. ;
Neu, C. P. .
OSTEOARTHRITIS AND CARTILAGE, 2019, 27 (02) :304-313
[26]   Rapid diagnosis of diabetes based on ResNet and Raman spectroscopy [J].
Wu, Jianying ;
Cui, Xinyue ;
Kang, Zhenping ;
Wang, Shanshan ;
Zhu, Guoqiang ;
Yang, Shufen ;
Wang, Shun ;
Li, Hongtao ;
Lu, Chen ;
Lv, Xiaoyi .
PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY, 2022, 39
[27]   Rapid Detection of Anthocyanin in Mulberry Based on Raman Spectroscopy [J].
Zhang Hui-jie ;
Cai Chong ;
Cui Xu-hong ;
Zhang Lei-lei .
SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41 (12) :3771-3775
[28]   Raman spectroscopy-based microfluidic platforms: A promising tool for detection of foodborne pathogens in food products [J].
Jayan, Heera ;
Yin, Limei ;
Xue, Shanshan ;
Zou, Xiaobo ;
Guo, Zhiming .
FOOD RESEARCH INTERNATIONAL, 2024, 180
[29]   Raman spectroscopy-based molecularly imprinted polymer sensor for sensitive detection of lysophosphatidic acid in serum [J].
Tarannum, Nazia ;
Kumar, Deepak ;
Yadav, Akanksha ;
Yadav, Anil K. .
JOURNAL OF RAMAN SPECTROSCOPY, 2024, 55 (07) :809-818
[30]   Deep convolutional neural networks as a unified solution for Raman spectroscopy-based classification in biomedical applications [J].
Kazemzadeh, Mohammadrahim ;
Hisey, Colin L. ;
Zargar-Shoshtari, Kamran ;
Xu, Weiliang ;
Broderick, Neil G. R. .
OPTICS COMMUNICATIONS, 2022, 510