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Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy
2020, Lorenz B., Ali N., Bocklitz T., Rösch P., Popp J.
Bacteria can be harmless commensals, beneficial probiotics, or harmful pathogens. Therefore, mankind is challenged to detect and identify bacteria in order to prevent or treat bacterial infections. Examples are identification of species for treatment of infection in clinics and E. coli cell counting for water quality monitoring. Finally, in some instances, the pathogenicity of a species is of interest. The main strategies to investigate pathogenicity are detection of target genes which encode virulence factors. Another strategy could be based on phenotypic identification. Raman spectroscopy is a promising phenotypic method, which offers high sensitivities and specificities for the identification of bacteria species. In this study, we evaluated whether Raman microspectroscopy could be used to determine the pathogenicity of E. coli strains. We used Raman spectra of seven non-pathogenic and seven pathogenic E. coli strains to train a PCA-SVM model. Then, the obtained model was tested by identifying the pathogenicity of three additional E. coli strains. The pathogenicity of these three strains could be correctly identified with a mean sensitivity of 77%, which is suitable for a fast screening of pathogenicity of single bacterial cells. [Figure not available: see fulltext.]. © 2020, The Author(s).
Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo
2021-1-28, Schleusener, Johannes, Guo, Shuxia, Darvin, Maxim E., Thiede, Gisela, Chernavskaia, Olga, Knorr, Florian, Lademann, Jürgen, Popp, Jürgen, Bocklitz, Thomas W.
Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.