https://www.db-thueringen.de/receive/dbt_mods_00062941https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00065197/s00216-021-03800-y.pdfhttps://www.db-thueringen.de/rsc/thumbnail/dbt_mods_00062941.png text article Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy Nakar, Amir Nakar Amir Research Campus Infectognostics Jena E.V, Jena, Germany aut Author 1298477557 article Pistiki, Aikaterini Pistiki Aikaterini Research Campus Infectognostics Jena E.V, Jena, Germany aut Author 1244950157 Ryabchykov, Oleg Ryabchykov Oleg Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany aut Author 1194391788 Bocklitz, Thomas Bocklitz Thomas Research Campus Infectognostics Jena E.V, Jena, Germany aut Author 101788207X Rösch, Petra Rösch Petra Research Campus Infectognostics Jena E.V, Jena, Germany aut Author 0000-0001-6179-3719 urn:nbn:de:gbv:27-dbt-62941-8 Popp, Jürgen Popp Jürgen Jena Biophotonics and Imaging Laboratory, Jena, Germany aut Author 131701819 10.1007/s00216-021-03800-y 2022-02 414 4 1481 1492 continuingjournal journal Analytical and Bioanalytical Chemistry a merger of Fresenius' journal of analytical chemistry and Analusis 1618-2642 1618-2650 Springer 2002 Berlin, Heidelberg text en 540 Abstract In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli . Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health. Graphical abstract Antibiotic resistance Raman spectroscopy Bacteria Label-free Diagnostic Machine learning Medical and Health Sciences en 12 Seiten 540 his Host institution Zweitveröffentlichung dbt_mods_00062941https://www.db-thueringen.de/receive/dbt_mods_00062941