Seminar

Date 2019-10-29 
Time 16:00 
Title Dr. Joonseok Lee (Molecular Recognition Research Center, KIST) 

■ Title: Near-infrared Emitting Materials for Biosensing Applications

 

 Speaker: Dr. Joonseok Lee (Molecular Recognition Research Center, KIST) 

 

■ Date and time: 10/29(Tue) 16:00

 

■ Venue:  Applied Engineering Building (W1) 1st Floor, Multimedia Lecture Room

 

 Host : Prof. Chan Beum Park

  

 Abstract :

Rapid and sensitive on-site detection of avian influenza viruses (AIV) is the key for achieving near real-time surveillance of AIV and reducing the risk of dissemination. However, unlike the laboratory-prepared transparent buffer solutions containing a single type of influenza virus, distinction between real- and false- positive outputs and detection of low concentrations of AIV in stool specimens or cloacal swabs are difficult. Here, we developed a rapid and background-free lateral flow immunoassay (LFA) platform that utilizes near-infrared (NIR)-to-NIR upconversion nanoparticles (UCNPs) to yield a sensor that detects AIV nucleoproteins from clinical samples within 20 minutes. Ca2+ as a heterogeneous dopant ion in the shell enhanced the NIR-to-NIR upconversion photoluminescence (PL) emission without inducing significant changes in the morphology and size of the UCNPs. In a mixture of opaque stool samples and gold nanoparticles (GNPs), which are components of commercial AIV LFA, the background signal of the stool samples masked the absorption peak of GNPs. However, UCNPs dispersed in the stool samples still show strong emission centered at 800 nm when excited at 980 nm, which enables the NIR-to-NIR upconversion nanoparticle-based lateral flow immunoassay (NNLFA) platform to detect 10-times lower viral load than a commercial GNP-based AIV LFA. The detection limit of NNLFA for LPAI H5N2 and HPAI H5N6 viruses was 10and 103.5 EID50/mL, respectively. Moreover, the viruses were successfully detected within dark brown-colored samples using the NNLFA but not the commercial AIV LFA. Therefore, the rapid and background-free NNLFA platform can be used for sensitive on-site detection of AIV.