Rapid bacterial antibiotic susceptibility test based on simple surface-enhanced Raman spectroscopic biomarkers

Liu, Chia Ying; Han, Yin-Yi; Shih, Po Han; Lian, Wei Nan; Wang, Huai Hsien; Lin, Chi; Hsueh, Po-Ren; Wang, Juen Kai; Wang, Yuh‐Lin

doi:10.1038/srep23375

Cited by 99 publications

(97 citation statements)

References 37 publications

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“…High signal-to-noise ratios (SNRs) are thus needed to reach high identification accuracies 9 , typically requiring long measurement times that prohibit high-throughput single-cell techniques. Additionally, the large number of clinically relevant species, strains, and antibiotic resistance patterns require comprehensive datasets that are not gathered in studies that focus on differentiating between species 10,11 , isolates (typically referred to as strains in the literature) 12,13 , or antibiotic susceptibilities [14][15][16][17][18][19] . In this work, we address this challenge by training a convolutional neural network (CNN) to classify noisy bacterial spectra by isolate, empiric treatment, and antibiotic resistance.…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

et al. 2019

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Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-tonoise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%.Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum.

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Section: Introductionmentioning

confidence: 99%

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

et al. 2019

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“…When bacteria in the sample is susceptible to the presence of an antibiotic at a given concentration, the SERS pattern decreases in amplitude over time, while the resistant strains do not show any significant change in their spectral pattern. The authors [27] were able to demonstrate the change in spectral pattern of S. aureus-oxacillin and E. coli-imipenem within 2 hours. Additionally, they determined the breakpoint value of spectral signal ratios, above which the antibiotic would be considered ineffective and below which they are considered susceptible.…”

Section: Future Technologiesmentioning

confidence: 89%

A Comprehensive Review of the Present and Future Antibiotic Susceptibility Testing (AST) Systems

Puttaswamy¹,

Gupta²,

Regunath³

et al. 2018

Arch Clin Microbiol

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Tools and instruments available in the clinical microbiology labs for analysis of patient samples and diagnosis are constantly evolving. The main impetus behind this is to decrease the overall time taken to obtain the results from the instruments, enhance the ease of sample processing, increasing the sample turn-around time with the ultimate goal of earlier patient treatment and better recovery rates. This is especially true in the case of antibiotic susceptibility testing (AST), where every hour saved in obtaining the results leading to an earlier switch to targeted antibiotic therapy will have a direct influence on improving clinical outcomes. Reduction in the time to obtain AST results reduces the duration of use of broad-spectrum antibiotics, which in turn decreases the emergence of antibiotic resistance among bacteria. Many of the traditional methods available for AST are labor intensive and slow despite being precise in obtaining results. Thus, there is a trend towards development and use of automated diagnostic devices which are rapid and easy to use. This review article provides a detailed summary of traditional AST methods, currently used automated methods, and focuses on some of the promising emerging and future technologies in the field of rapid antibiotic susceptibility profiling.

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“…Another issue is to search for the possibility of testing direct clinical samples reducing the assay requirements for handling and treatment of the microbial contaminated fluid as well as bacterial isolation and enrichment. Fast antimicrobial susceptibility testing has been achieved by diverse methods, among these are the molecular techniques [2,3] biosensors [4] fluorescence detection [5], as well as the adaptation of new techniques such as Raman Spectroscopy [6]. Therefore it is desirable to perform an exploratory assay which could eventually set the basis for a fast antibiogram.…”

Section: Introductionmentioning

confidence: 99%

Biospeckle laser digital image processing for quantitative and statistical evaluation of the activity of Ciprofloxacin onEscherichia coliK-12

Grassi

Velásquez

Belandria

et al. 2018

Preprint

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47Antibiotic susceptibility testing is a necessary step prior to the treatment of clinical 48 infections. A major concern is the time required to obtain a fast and reliable result. The aim 49 of this work is to use Biospeckle laser in a 15min assay for an antimicrobial susceptibility 50 test of Ciprofloxacin in serial two-fold dilutions on Escherichia coli K-12 using Venereal 51 Disease Research Laboratory (VDRL) plates. Analysis of images by video edition is 52 performed on a quantitatively selected region of interest, and processed with ImageJ-53 ImageDP; and by the construction of time series and analysis with either statistical 54 diagnostics tests or Autoregressive Integrated Moving Average (ARIMA) models. 55 Antimicrobial susceptibility tests are also performed for the purpose of quantitative 56 comparison, showing a profile that is comparable to the result obtained with ImageJ-57 ImageDP processing after 15min of antibiotic action. Only the time series of the least 58 affected bacteria (low Ciprofloxacin concentration) behaves in an expected manner, being 59 non-independent and mainly non-linear, non-normal, and heteroscedastic. The most 60 affected bacteria (higher Ciprofloxacin concentration) are non-independent and tend to be 61 linear, normal and heteroscedastic. Adjustment to a linear regression identifies both, the 62 culture medium without bacteria and the most affected bacteria, normality identifies the 63 most affected bacteria and heteroscedasticity-homoscedasticity distinguishes the presence-64absence of bacteria, respectively. ARIMA models () 11 fit the 65 time series of the most affected bacteria while the latter also fits the culture medium 66 without bacteria. The time series of the least affected bacteria are identified by a 67 (7,1,2)(1,0,1) 11 model. The non-linear, non-normal and heteroscedastic behavior of this 68 group is probably responsible for its adjustment to a model with a relatively high 69 parameter. The four methods: diagnostic statistical tests, fitting of ARIMA models, ImageJ-70 ImageDP and antimicrobial susceptibility tests, show similar results, being able to 71 distinguish among the groups of assays with bacteria and Ciprofloxacin below and above 72 the Minimal Inhibitory Concentration. 74 Author Summary 75Biospeckle laser patterns occur when a dynamic surface is illuminated. This research 76 describes its application to the activity of Escherichia coli bacteria and the effect of 18 77 different concentrations of an antibiotic (Ciprofloxacin) in a 15min assay, using VDRL 78 plates where the sample has a relatively small volume and is flat shaped. The assay is 79 performed on an anti-vibration table in a dark room with a laser that sequentially 80 illuminates each of the wells of the plate. A camera takes short 30sec videos with 81 approximately 750 frames and sends them to a computer where image processing takes 82 place. In order to select a segment of 80 successive frames to analyze, the region with the 83 higher variation was identified, punched out and edited as a "fl...

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Rapid bacterial antibiotic susceptibility test based on simple surface-enhanced Raman spectroscopic biomarkers

Cited by 99 publications

References 37 publications

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

A Comprehensive Review of the Present and Future Antibiotic Susceptibility Testing (AST) Systems

Biospeckle laser digital image processing for quantitative and statistical evaluation of the activity of Ciprofloxacin onEscherichia coliK-12

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