Evdokia Kastanos

A Novel Method for Bacterial UTI Diagnosis Using Raman Spectroscopy

The current state of the art on bacterial classification using Raman and Surface Enhanced Raman Spectroscopy (SERS) for the purpose of developing a rapid and more accurate method for urinary tract infection (UTI) diagnosis is presented. SERS, an enhanced version of Raman offering much increased sensitivity, provides complex biochemical information which, in conjunction with advanced analysis and classification techniques, can become a valuable diagnostic tool. The variety of metal substrates used for SERS, including silver and gold colloids, as well as nanostructured metal surfaces, is reviewed. The challenges in preprocessing noisy and complicated spectra and the various methods used for feature creation as well as a novel method using spectral band ratios are described. The various unsupervised and supervised classification methods commonly used for SERS spectra of bacteria are evaluated. Current research on transforming SERS into a valuable clinical tool for the diagnosis of UTIs is presented. Specifically, the classification of bacterial spectra (a) as positive or negative for an infection, (b) as belonging to a particular species of bacteria, and (c) as sensitive or resistant to an antibiotic are described. This work can lead to the development of novel technology with extremely important benefits for public health.

Classification of bacterial samples as negative or positive for a UTI and antibiogram using surface enhanced Raman spectroscopy

Urinary tract infection (UTI) diagnosis requires an overnight culture to identify a sample as positive or negative for a UTI. Additional cultures are required to identify the pathogen responsible for the infection and to test its sensitivity to antibiotics. A rise in ineffective treatments, chronic infections, rising health care costs and antibiotic resistance are some of the consequences of this prolonged waiting period of UTI diagnosis. In this work, Surface Enhanced Raman Spectroscopy (SERS) is used for classifying bacterial samples as positive or negative for UTI. SERS spectra of serial dilutions of E.coli bacteria, isolated from a urine culture, were classified as positive (105-108 cells/ml) or negative (103-104 cells/ml) for UTI after mixing samples with gold nanoparticles. A leave-one-out cross validation was performed using the first two principal components resulting in the correct classification of 82% of all samples. Sensitivity of classification was 88% and specificity was 67%. Antibiotic sensitivity testing was also done using SERS spectra of various species of gram negative bacteria collected 4 hours after exposure to antibiotics. Spectral analysis revealed clear separation between the spectra of samples exposed to ciprofloxacin (sensitive) and amoxicillin (resistant). This study can become the basis for identifying urine samples as positive or negative for a UTI and determining their antibiogram without requiring an overnight culture.

Complete urinary tract infection (UTI) diagnosis and antibiogram using surface enhanced Raman spectroscopy (SERS)

There are three stages to a complete UTI diagnosis: (1) identification of a urine sample as positive/negative for an infection, (2) identification of the responsible bacterium, (3) antibiogram to determine the antibiotic to which the bacteria are most sensitive to. Using conventional methods, all three stages require bacterial cultures in order to provide results. This long delay in diagnosis causes a rise in ineffective treatments, chronic infections, health care costs and antibiotic resistance. In this work, SERS is used to identify a sample as positive/negative for a UTI as well as to obtain an antibiogram against different antibiotics. SERS spectra of serial dilutions of E. coli bacteria mixed with silver nanoparticles, showed a linear correlation between spectral intensity and concentration. For antibiotic sensitivity testing, SERS spectra of three species of gram negative bacteria were collected four hours after exposure to the antibiotics ciprofloxacin and amoxicillin. Spectral analysis revealed clear separation between bacterial samples exposed to antibiotics to which they were sensitive and samples exposed to antibiotics to which they were resistant. With the enhancement provided by SERS, the technique can be applied directly to urine samples leading to the development of a new, rapid method for UTI diagnosis and antibiogram.

Multi-bacteria multi-antibiotic testing using surface enhanced Raman spectroscopy (SERS) for urinary tract infection (UTI) diagnosis

The inappropriate use of antibiotics leads to antibiotic resistance, which is a major health care problem. The current method for determination of bacterial susceptibility to antibiotics requires overnight cultures. However most of the infections cannot wait for the results to receive treatment, so physicians administer general spectrum antibiotics. This results in ineffective treatments and aggravates the rising problem of antibiotic resistance. In this work, a rapid method for diagnosis and antibiogram for a bacterial infection was developed using Surface Enhanced Raman Spectroscopy (SERS) with silver nanoparticles. The advantages of this novel method include its rapidness and efficiency which will potentially allow doctors to prescribe the most appropriate antibiotic for an infection. SERS spectra of three species of gram negative bacteria, Escherichia coli, Proteus spp., and Klebsiella spp. were obtained after 0 and 4 hour exposure to the seven different antibiotics. Bacterial strains were diluted in order to reach the concentration of (2x105 cfu/ml), cells/ml which is equivalent to the minimum concentration found in urine samples from UTIs. Even though the concentration of bacteria was low, species classification was achieved with 94% accuracy using spectra obtained at 0 hours. Sensitivity or resistance to antibiotics was predicted with 81%-100% accuracy from spectra obtained after 4 hours of exposure to the different antibiotics. This technique can be applied directly to urine samples, and with the enhancement provided by SERS, this method has the potential to be developed into a rapid method for same day UTI diagnosis and antibiogram.

Classification of Raman Spectra using Support Vector Machines

The classification of Raman Spectra is useful in identification and diagnosis applications. We have obtained Raman Spectra from bacterial samples using three different species of bacteria. Before any form of classification can be carried out on the Raman Spectra it is important that some form of normalization is used. This is due to the nature of the readings obtained by the acquisition equipment. The method used for normalization greatly affects the accuracy of the results. We have carried out experiments using Support Vector Machines and the correlation kernel. Our observations have led us to the hypothesis that the correlation kernel is “self-normalizing” and gives satisfactory results without the need of any other normalization technique.

Urinary tract infection diagnosis and response to antibiotics using Raman spectroscopy

Urinary tract infection diagnosis and antibiogram require a 48 hour waiting period using conventional methods. This results in ineffective treatments, increased costs and most importantly in increased resistance to antibiotics. In this work, a novel method for classifying bacteria and determining their sensitivity to an antibiotic using Raman spectroscopy is described. Raman spectra of three species of gram negative Enterobacteria, most commonly responsible for urinary tract infections, were collected. The study included 25 samples each of E.coli, Klebsiella p. and Proteus spp. A novel algorithm based on spectral ratios followed by discriminant analysis resulted in classification with over 94% accuracy. Sensitivity and specificity for the three types of bacteria ranged from 88-100%. For the development of an antibiogram, bacterial samples were treated with the antibiotic ciprofloxacin to which they were all sensitive. Sensitivity to the antibiotic was evident after analysis of the Raman signatures of bacteria treated or not treated with this antibiotic as early as two hours after exposure. This technique can lead to the development of new technology for urinary tract infection diagnosis and antibiogram with same day results, bypassing urine cultures and avoiding all undesirable consequences of current practice.

Urinary tract infection (UTI) multi-bacteria multi-antibiotic testing using surface enhanced Raman spectroscopy (SERS)

Antibiotic resistance is a major health care problem mostly caused by the inappropriate use of antibiotics. At the root of the problem lies the current method for determination of bacterial susceptibility to antibiotics which requires overnight cultures. Physicians suspecting an infection usually prescribe an antibiotic without waiting for the results. This practice aggravates the problem of bacterial resistance. In this work, a rapid method of diagnosis and antibiogram for a bacterial infection was developed using Surface Enhanced Raman Spectroscopy (SERS) with silver nanoparticles. SERS spectra of three species of gram negative bacteria, Escherichia coli, Proteus spp., and Klebsiella spp. were obtained after 0 and 4 hour exposure to the seven different antibiotics. Even though the concentration of bacteria was low (2x105 cfu/ml), species classification was achieved with 94% accuracy using spectra obtained at 0 hours. Sensitivity or resistance to antibiotics was predicted with 81%-100% accuracy from spectra obtained after 4 hours of exposure to the different antibiotics. With the enhancement provided by SERS, the technique can be applied directly to urine or blood samples, bypassing the need for overnight cultures. This technology can lead to the development of rapid methods of diagnosis and antibiogram for a variety of bacterial infections.

UTI diagnosis and antibiogram using Raman spectroscopy

Urinary tract infection diagnosis and antibiogram require a 48 hour waiting period using conventional methods. This results in ineffective treatments, increased costs and most importantly in increased resistance to antibiotics. In this work, a novel method for classifying bacteria and determining their sensitivity to an antibiotic using Raman spectroscopy is described. Raman spectra of three species of gram negative Enterobacteria, most commonly responsible for urinary tract infections, were collected. The study included 25 samples each of E.coli, Klebsiella p. and Proteus spp. A novel algorithm based on spectral ratios followed by discriminant analysis resulted in classification with over 94% accuracy. Sensitivity and specificity for the three types of bacteria ranged from 88-100%. For the development of an antibiogram, bacterial samples were treated with the antibiotic ciprofloxacin to which they were all sensitive. Sensitivity to the antibiotic was evident after analysis of the Raman signatures of bacteria treated or not treated with this antibiotic as early as two hours after exposure. This technique can lead to the development of new technology for urinary tract infection diagnosis and antibiogram with same day results, bypassing urine cultures and avoiding all undesirable consequences of current practice.

Rapid identification of bacterial resistance to Ciprofloxacin using surface-enhanced Raman spectroscopy

Due to its effectiveness and broad coverage, Ciprofloxacin is the fifth most prescribed antibiotic in the US. As current methods of infection diagnosis and antibiotic sensitivity testing (i.e. an antibiogram) are very time consuming, physicians prescribe ciprofloxacin before obtaining antibiogram results. In order to avoid increasing resistance to the antibiotic, a method was developed to provide both a rapid diagnosis and the sensitivity to the antibiotic. Using Surface Enhanced Raman Spectroscopy, an antibiogram was obtained after exposing the bacteria to Ciprofloxacin for just two hours. Spectral analysis revealed clear separation between sensitive and resistant bacteria and could also offer some inside into the mechanisms of resistance.

Classification of Raman spectra of bacteria using rank order kernels

The range of applications of Raman-based classification has expanded significantly, including applications in bacterial identification. In this paper, we propose the use of Rank Order Kernels to classify bacterial samples. Rank Order Kernels are two-dimensional image functions which operate on two-dimensional images. The first step in the classification therefore, is to transform the Raman spectra to two-dimensional images. This is achieved by splitting the spectra into segments and calculating the ratio between the mean value of each and every other segment. This creates a two-dimensional matrix of ratios for each Raman spectrum. A similarity metric based on rank order kernels operating on the two-dimensional matrices is then used with a nearest neighbor algorithm for classification. Our results show that this method is comparable in accuracy to other methods which were used previously for the same data set.