Your browser doesn't support javascript.
loading
Dark-Field Microscopic Detection of Bacteria using Bacteriophage-Immobilized SiO2@AuNP Core-Shell Nanoparticles.
Imai, Masashi; Mine, Kouhei; Tomonari, Haruna; Uchiyama, Jumpei; Matuzaki, Shigenobu; Niko, Yosuke; Hadano, Shingo; Watanabe, Shigeru.
Afiliação
  • Uchiyama J; School of Veterinary Medicine , Azabu University , 1-17-71 Fuchinobe , Sagamihara-shi 229-8501 , Kanagawa , Japan.
  • Matuzaki S; Department of Microbiology and Infection, Kochi Medical School , Kochi University , Kohasu, Okoh-cho , Nankoku-shi 780-8505 , Kochi , Japan.
Anal Chem ; 91(19): 12352-12357, 2019 10 01.
Article em En | MEDLINE | ID: mdl-31464422
ABSTRACT
To replace molecular biological and immunological methods, biosensors have recently been developed for the rapid and sensitive detection of bacteria. Among a wide variety of biological materials, bacteriophages have received increasing attention as promising alternatives to antibodies in biosensor applications. Thus, we herein present a rapid and highly selective detection method for pathogenic bacteria, which combines dark-field light scattering imaging with a plasmonic biosensor system. The plasmonic biosensor system employs bacteriophages as the biorecognition element and the aggregation-induced light scattering signal of gold nanoparticle-assembled silica nanospheres as a signal transducer. Using Staphylococcus aureus strain SA27 as a model analyte, we demonstrated that the plasmonic biosensor system detects S. aureus in the presence of excess Escherichia coli in a highly selective manner. After the sample and the S. aureus phage S13'-conjugated plasmon scattering probe were mixed, S. aureus detection was completed within 15-20 min with a detection limit of 8 × 104 colony forming units per milliliter.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article