Your browser doesn't support javascript.
loading
Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.
Dahms, Marcel; Eiserloh, Simone; Rödel, Jürgen; Makarewicz, Oliwia; Bocklitz, Thomas; Popp, Jürgen; Neugebauer, Ute.
Afiliación
  • Dahms M; Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies), Jena, Germany.
  • Eiserloh S; Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany.
  • Rödel J; Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies), Jena, Germany.
  • Makarewicz O; Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany.
  • Bocklitz T; Institute for Medical Microbiology, Jena University Hospital, Jena, Germany.
  • Popp J; Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany.
  • Neugebauer U; Institute of Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany.
Front Cell Infect Microbiol ; 12: 930011, 2022.
Article en En | MEDLINE | ID: mdl-35937698
Streptococcus pneumoniae, commonly referred to as pneumococci, can cause severe and invasive infections, which are major causes of communicable disease morbidity and mortality in Europe and globally. The differentiation of S. pneumoniae from other Streptococcus species, especially from other oral streptococci, has proved to be particularly difficult and tedious. In this work, we evaluate if Raman spectroscopy holds potential for a reliable differentiation of S. pneumoniae from other streptococci. Raman spectra of eight different S. pneumoniae strains and four other Streptococcus species (S. sanguinis, S. thermophilus, S. dysgalactiae, S. pyogenes) were recorded and their spectral features analyzed. Together with Raman spectra of 59 Streptococcus patient isolates, they were used to train and optimize binary classification models (PLS-DA). The effect of normalization on the model accuracy was compared, as one example for optimization potential for future modelling. Optimized models were used to identify S. pneumoniae from other streptococci in an independent, previously unknown data set of 28 patient isolates. For this small data set balanced accuracy of around 70% could be achieved. Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones Estreptocócicas / Streptococcus pneumoniae Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Front Cell Infect Microbiol Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones Estreptocócicas / Streptococcus pneumoniae Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Front Cell Infect Microbiol Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Suiza