Your browser doesn't support javascript.
loading
Differential mobility spectrometry classification of bacteria.
Hokkinen, Lauri; Kesti, Artturi; Lepomäki, Jaakko; Anttalainen, Osmo; Kontunen, Anton; Karjalainen, Markus; Aittoniemi, Janne; Vuento, Risto; Lehtimäki, Terho; Oksala, Niku; Roine, Antti.
Afiliação
  • Hokkinen L; Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland.
  • Kesti A; Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland.
  • Lepomäki J; Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland.
  • Anttalainen O; Vascular & interventional Center, Tampere University Hospital, Tampere, Finland.
  • Kontunen A; Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland.
  • Karjalainen M; Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland.
  • Aittoniemi J; Fimlab Laboratories, Tampere, Finland.
  • Vuento R; Fimlab Laboratories, Tampere, Finland.
  • Lehtimäki T; Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland.
  • Oksala N; Fimlab Laboratories, Tampere, Finland.
  • Roine A; Finnish Cardiovascular Research Center - Tampere, Tampere University, Tampere, Finland.
Future Microbiol ; 15: 233-240, 2020 03.
Article em En | MEDLINE | ID: mdl-32271111
ABSTRACT

Aim:

Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost-effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials &

methods:

Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation.

Results:

Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%.

Conclusion:

Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bactérias / Técnicas de Tipagem Bacteriana Tipo de estudo: Evaluation_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Future Microbiol Assunto da revista: MICROBIOLOGIA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Finlândia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bactérias / Técnicas de Tipagem Bacteriana Tipo de estudo: Evaluation_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Future Microbiol Assunto da revista: MICROBIOLOGIA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Finlândia