An Optical Fiber-Based Nanomotion Sensor for Rapid Antibiotic and Antifungal Susceptibility Tests.

The emergence of antibiotic and antifungal resistant microorganisms represents nowadays a major public health issue that might push humanity into a post-antibiotic/antifungal era. One of the approaches to avoid such a catastrophe is to advance rapid antibiotic and antifungal susceptibility tests. In this study, we present a compact, optical fiber-based nanomotion sensor to achieve this goal by monitoring the dynamic nanoscale oscillation of a cantilever related to microorganism viability. High detection sensitivity was achieved that was attributed to the flexible two-photon polymerized cantilever with a spring constant of 0.3 N/m. This nanomotion device showed an excellent performance in the susceptibility tests of Escherichia coli and Candida albicans with a fast response in a time frame of minutes. As a proof-of-concept, with the simplicity of use and the potential of parallelization, our innovative sensor is anticipated to be an interesting candidate for future rapid antibiotic and antifungal susceptibility tests and other biomedical applications.

A simplified version of rapid susceptibility testing of bacteria and yeasts using optical nanomotion detection.

We present a novel optical nanomotion-based rapid antibiotic and antifungal susceptibility test. The technique consisted of studying the effects of antibiotics or antifungals on the nanometric scale displacements of bacteria or yeasts to assess their sensitivity or resistance to drugs. The technique relies on a traditional optical microscope, a video camera, and custom-made image analysis software. It provides reliable results in a time frame of 2-4 h and can be applied to motile, non-motile, fast, and slowly growing microorganisms. Due to its extreme simplicity and low cost, the technique can be easily implemented in laboratories and medical centers in developing countries.

Machine learning method for the classification of the state of living organisms' oscillations.

The World Health Organization highlights the urgent need to address the global threat posed by antibiotic-resistant bacteria. Efficient and rapid detection of bacterial response to antibiotics and their virulence state is crucial for the effective treatment of bacterial infections. However, current methods for investigating bacterial antibiotic response and metabolic state are time-consuming and lack accuracy. To address these limitations, we propose a novel method for classifying bacterial virulence based on statistical analysis of nanomotion recordings. We demonstrated the method by classifying living Bordetella pertussis bacteria in the virulent or avirulence phase, and dead bacteria, based on their cellular nanomotion signal. Our method offers significant advantages over current approaches, as it is faster and more accurate. Additionally, its versatility allows for the analysis of cellular nanomotion in various applications beyond bacterial virulence classification.

Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.

Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performances of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical setting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9%. The study shows the potential of this nanomotion platform for future bacterial phenotype delineation.