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Infection and drug resistance · 2023

Raman-Based Antimicrobial Susceptibility Testing on Antibiotics of Last Resort.

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Xiao Zhirou, Qu Liping, Chen Haijun, Liu Wanting, Zhan Yi, Ling Jiahui, Shen Hongwei, Yang Ling, Chen Dingqiang

Аннотация

BACKGROUND: Antibiotic resistance represents a serious global health challenge, particularly with the emergence of strains resistant to last-resort antibiotics such as tigecycline, polymyxin B, and vancomycin. Urgent measures are required to alleviate this situation. To facilitate the judicious use of antibiotics, rapid and precise antimicrobial susceptibility testing (AST) is essential. Heavy water (deuterium oxide, D2O)-labeled Raman spectroscopy has emerged as a promising time-saving tool for microbiological testing.

METHODS: Deuterium incorporation and experimental conditions were examined to develop and apply a Raman-based AST method to evaluate the efficacy of last-resort antibiotics, including tigecycline, polymyxin B, and vancomycin, against Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterococcus faecium. Essential agreement and categorical agreement were used to assess the metabolism inactivation concentration based on Raman spectroscopy (R-MIC)-a new metric developed in this study-and minimum inhibitory concentration (MIC) determined via the traditional microdilution broth method. Spearman's rank correlation coefficient was employed to measure the association between R-MIC and MIC values.

RESULTS: The Raman-based AST method achieved a 100% categorical agreement (92/92) with the traditional microdilution broth method within five hours, while the traditional method required approximately 24 h. The R-MIC values shared 68.5% (63/92) consistency with the MIC values. In addition, the R-MIC and MIC values were highly correlated (Spearman's r=0.96), resulting in an essential agreement of 100%.

CONCLUSION: Our optimized experimental method and conditions indicate that Raman-based AST holds great promise as a solution to overcome the time-consuming challenges of traditional AST methods.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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