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Silge A.,Friedrich - Schiller University of Jena | Silge A.,InfectoGnostics Research Campus Jena | Schumacher W.,Friedrich - Schiller University of Jena | Schumacher W.,InfectoGnostics Research Campus Jena | And 7 more authors.
Systematic and Applied Microbiology

The identification of Pseudomonas aeruginosa from samples of bottled natural mineral water by the analysis of subcultures is time consuming and other species of the authentic Pseudomonas group can be a problem. Therefore, this study aimed to investigate the influence of different aquatic environmental conditions (pH, mineral content) and growth phases on the cultivation-free differentiation between water-conditioned Pseudomonas spp. by applying Raman microspectroscopy. The final dataset was comprised of over 7500 single-cell Raman spectra, including the species Pseudomonas aeruginosa, P. fluorescens and P. putida, in order to prove the feasibility of the introduced approach. The collection of spectra was standardized by automated measurements of viable stained bacterial cells. The discrimination was influenced by the growth phase at the beginning of the water adaptation period and by the type of mineral water. Different combinations of the parameters were tested and they resulted in accuracies of up to 85% for the identification of P. aeruginosa from independent samples by applying chemometric analysis. © 2014 Elsevier GmbH. Source

Guo S.,Friedrich - Schiller University of Jena | Bocklitz T.,Friedrich - Schiller University of Jena | Bocklitz T.,InfectoGnostics Research Campus Jena | Popp J.,Friedrich - Schiller University of Jena | And 2 more authors.

In the last decade Raman-spectroscopy has become an invaluable tool for biomedical diagnostics. However, a manual rating of the subtle spectral differences between normal and abnormal disease states is not possible or practical. Thus it is necessary to combine Raman-spectroscopy with chemometrics in order to build statistical models predicting the disease states directly without manual intervention. Within chemometrical analysis a number of corrections have to be applied to receive robust models. Baseline correction is an important step of the pre-processing, which should remove spectral contributions of fluorescence effects and improve the performance and robustness of statistical models. However, it is demanding, time-consuming, and depends on expert knowledge to select an optimal baseline correction method and its parameters every time working with a new dataset. To circumvent this issue we proposed a genetic algorithm based method to automatically optimize the baseline correction. The investigation was carried out in three main steps. Firstly, a numerical quantitative marker was defined to evaluate the baseline estimation quality. Secondly, a genetic algorithm based methodology was established to search the optimal baseline estimation with the defined quantitative marker as evaluation function. Finally, classification models were utilized to benchmark the performance of the optimized baseline. For comparison, model based baseline optimization was carried out applying the same classifiers. It was proven that our method could provide a semi-optimal and stable baseline estimation without any chemical knowledge required or any additional spectral information used. © The Royal Society of Chemistry 2016. Source

Silge A.,Friedrich - Schiller University of Jena | Silge A.,InfectoGnostics Research Campus Jena | Bocklitz T.,Friedrich - Schiller University of Jena | Bocklitz T.,InfectoGnostics Research Campus Jena | And 7 more authors.
Analytical and Bioanalytical Chemistry

Metal oxide nanoparticles (NP) are applied in the fields of biomedicine, pharmaceutics, and in consumer products as textiles, cosmetics, paints, or fuels. In this context, the functionalization of the NP surface is a common method to modify and modulate the product performance. A chemical surface modification of NP such as an amino-functionalization can be used to achieve a positively charged and hydrophobic surface. Surface functionalization is known to affect the interaction of nanomaterials (NM) with cellular macromolecules and the responses of tissues or cells, like the uptake of particles by phagocytic cells. Therefore, it is important to assess the possible risk of those modified NP for human health and environment. By applying Raman microspectroscopy, we verified in situ the interaction of amino-modified ZrO2 NP with cultivated macrophages. The results demonstrated strong adhesion properties of the NP to the cell membrane and internalization into the cells. The intracellular localization of the NP was visualized via Raman depth scans of single cells. After the cells were treated with sodium azide (NaN3) and 2-deoxy-glucose to inhibit the phagocytic activity, NP were still detected inside cells to comparable percentages. The observed tendency of amino-modified ZrO2 NP to interact with the cultivated macrophages may influence membrane integrity and cellular functions of alveolar macrophages in the respiratory system. [Figure not available: see fulltext.] © 2016 Springer-Verlag Berlin Heidelberg Source

Guo S.,Friedrich - Schiller University of Jena | Guo S.,Institute of Photonic Technology | Heinke R.,Friedrich - Schiller University of Jena | Heinke R.,Institute of Photonic Technology | And 9 more authors.
Vibrational Spectroscopy

One of the most important issues for the application of Raman spectroscopy for biological diagnostics is how to deal efficiently with large datasets. The best solution is chemometrics, where statistical models are built based on a certain number of known samples and used to predict unknown datasets in future. However, the prediction may fail if the new datasets are measured under different conditions as those used for establishing the model. In this case, model transfer methods are required to obtain high prediction accuracy for both datasets. Known model transfer methods, for instance standard calibration and training models with datasets measured under multiple conditions, do not provide satisfactory results. Therefore, we studied two approaches to improve model transferability: wavenumber adjustment by a genetic algorithm (GA) after the standard calibration and model updating based on the Tikhonov regularization (TR). We based our investigation on Raman spectra of three spore species measured on four spectrometers. The methods were tested regarding two aspects. First, the wavenumber alignment is checked by computing Euclidean distances between the mean Raman spectra from different devices. Second, we evaluated the model transferability by means of the accuracy of a three-class classification system. According to the results, the model transferability was significantly improved by the wavenumber adjustment, even though the Euclidean distances were almost the same compared with those after the standard calibration. For the TR2 method the model transferability was dramatically improved by updating current models with very few samples from the new datasets. This improvement was not significantly lowered even if no spectral standardization was implemented beforehand. Nevertheless, the model transferability was enhanced by combining different model transform mechanisms. © 2016 Elsevier B.V. Source

Pahlow S.,Friedrich - Schiller University of Jena | Pahlow S.,InfectoGnostics Research Campus Jena | Stockel S.,Friedrich - Schiller University of Jena | Stockel S.,InfectoGnostics Research Campus Jena | And 12 more authors.
Analytical Chemistry

Pyoverdine is a substance which is excreted by fluorescent pseudomonads in order to scavenge iron from their environment. Due to specific receptors of the bacterial cell wall, the iron loaded pyoverdine molecules are recognized and transported into the cell. This process can be exploited for developing efficient isolation and enrichment strategies for members of the Pseudomonas genus, which are capable of colonizing various environments and also include human pathogens like P. aeruginosa and the less virulent P. fluorescens. A significant advantage over antibody based systems is the fact that siderophores like pyoverdine can be considered as "immutable ligands," since the probability for mutations within the siderophore uptake systems of bacteria is very low. While each species of Pseudomonas usually produces structurally unique pyoverdines, which can be utilized only by the producer strain, cross reactivity does occur. In order to achieve a reliable identification of the captured pathogens, further investigations of the isolated cells are necessary. In this proof of concept study, we combine the advantages of an isolation strategy relying on "immutable ligands" with the high specificity and speed of Raman microspectroscopy. In order to isolate the bacterial cells, pyoverdine was immobilized covalently on planar aluminum chip substrates. After capturing, single cell Raman spectra of the isolated species were acquired. Due to the specific spectroscopic fingerprint of each species, the bacteria can be identified. This approach allows a very rapid detection of potential pathogens, since time-consuming culturing steps are unnecessary. We could prove that pyoverdine based isolation of bacteria is fully Raman compatible and further investigated the capability of this approach by isolating and identifying P. aeruginosa and P. fluorescens from tap water samples, which are both opportunistic pathogens and can pose a threat for immunocompromised patients. © 2015 American Chemical Society. Source

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