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Author: Popp, Jürgen × Author: Rösch, Petra × End date: 2023 × Start date: 2022 × Start date: 2020 × DDC: 540 ×

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Now showing 1 - 4 of 4
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    Isolation of bacteria from artificial bronchoalveolar lavage fluid using density gradient centrifugation and their accessibility by Raman spectroscopy
    (Berlin [u.a.] : Springer, 2021) Wichmann, Christina; Rösch, Petra; Popp, Jürgen
    Raman spectroscopy is an analytical method to identify medical samples of bacteria. Because Raman spectroscopy detects the biochemical properties of a cell, there are many factors that can influence and modify the Raman spectra of bacteria. One possible influence is a proper method for isolation of the bacteria. Medical samples in particular never occur in purified form, so a Raman-compatible isolation method is needed which does not affect the bacteria and thus the resulting spectra. In this study, we present a Raman-compatible method for isolation of bacteria from bronchoalveolar lavage (BAL) fluid using density gradient centrifugation. In addition to measuring the bacteria from a patient sample, the yield and the spectral influence of the isolation on the bacteria were investigated. Bacteria isolated from BAL fluid show additional peaks in comparison to pure culture bacteria, which can be attributed to components in the BAL sample. The isolation gradient itself has no effect on the spectra, and with a yield of 63% and 78%, the method is suitable for isolation of low concentrations of bacteria from a complex matrix. Graphical abstract.
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    Comparison of bacteria in different metabolic states by micro-Raman spectroscopy
    (New York, NY [u.a.] : Elsevier, 2022) Shen, Haodong; Rösch, Petra; Thieme, Lara; Pletz, Mathias W.; Popp, Jürgen
    It was shown that several metabolic states of bacteria with various characteristics such as chemical composition participate in the formation of biofilms. To study the connections and differences among different bacterial metabolic states, five species of bacteria in exponential phase, stationary phase and biofilm have been compared and investigated by micro-Raman spectroscopy. The spectral differences between different metabolic states showed that the chemical composition varied among those metabolic states. Moreover, as can be shown by the spectral differences and principal components (PCs), different species and strains of bacteria behave differently. Furthermore, a principal component analysis (PCA) combined with support vector machines (SVM) was applied to distinguish species of bacteria within the same metabolic states. Our study provides valuable data for the comparison of bacteria between different metabolic states utilizing micro-Raman spectroscopy in combination with chemometrics models.
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    Using Raman spectroscopy in infection research
    (Heidelberg : Spektrum, 2022) Cialla-May, Dana; Rösch, Petra; Popp, Jürgen
    Raman spectroscopy allows to analyze bacteria and other microorganisms label and destruction free. With different Raman techniques either colonies on agar plates or small structures like single bacterial cells can be analyzed allowing for their identification as well as enabling 2D and 3D information of intracellular bacteria or biofilms. Using surface enhanced Raman spectroscopy (SERS) allows detecting and identifying viruses as well as antibiotics relevant in the treatment of infections.
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    Use of polymers as wavenumber calibration standards in deep-UVRR
    (Amsterdam [u.a.] : Elsevier Science, 2022) Pistiki, Aikaterini; Ryabchykov, Oleg; Bocklitz, Thomas W.; Rösch, Petra; Popp, Jürgen
    Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400–1900 cm−1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.