Surgeons at Detroit Medical Center and Wayne State University in Detroit are developing a faster, less expensive method of identifying bacterial infections and determining their antibiotic resistance, according to an American College of Surgeons news release.
Surgeons used Raman spectroscopy to look at bacteria's infrared wavelengths and pinpoint unique patterns of molecular vibration in blood samples inoculated with Staphylococcus aureus, the bacteria that causes Staph infections.
The researchers observed 120 spectral patterns from four strains of antibiotic resistant S. aureus: two that were sensitive to the antibiotic methicillin, one that was resistant to methicillin and a more stubborn form of Staph infection that has a reduced susceptibility to a last-resort antibiotic called vancomycin.
Raman spectroscopy enabled the researchers to distinguish the methicillin-sensitive S. aureus from methicillin-resistant S. aureus with 90.2 percent accuracy and MRSA from RVS-MRSA with 96.3 percent accuracy. The S. aureus profiles were then entered into a statistical program to create a preformed model of the Raman spectra. When the surgeons tested new spectra, the program was 98 percent accurate in classifying the bacteria as one of the four strains.
Researchers are developing a technology to integrate a pathogen database and the Raman spectroscopy technique into a hand-held device that would cut turnaround times for diagnostic test results.
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Surgeons used Raman spectroscopy to look at bacteria's infrared wavelengths and pinpoint unique patterns of molecular vibration in blood samples inoculated with Staphylococcus aureus, the bacteria that causes Staph infections.
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The researchers observed 120 spectral patterns from four strains of antibiotic resistant S. aureus: two that were sensitive to the antibiotic methicillin, one that was resistant to methicillin and a more stubborn form of Staph infection that has a reduced susceptibility to a last-resort antibiotic called vancomycin.
Raman spectroscopy enabled the researchers to distinguish the methicillin-sensitive S. aureus from methicillin-resistant S. aureus with 90.2 percent accuracy and MRSA from RVS-MRSA with 96.3 percent accuracy. The S. aureus profiles were then entered into a statistical program to create a preformed model of the Raman spectra. When the surgeons tested new spectra, the program was 98 percent accurate in classifying the bacteria as one of the four strains.
Researchers are developing a technology to integrate a pathogen database and the Raman spectroscopy technique into a hand-held device that would cut turnaround times for diagnostic test results.
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