tetano
Editor, Senior Moderator
Biotechnol Rep (Amst). 2015 May 19;7:64-71. doi: 10.1016/j.btre.2015.05.004. eCollection 2015 Sep.
[h=1]Highly sensitive detection of influenza virus in saliva by real-time PCR method using sugar chain-immobilized gold nanoparticles; application to clinical studies.[/h] Suda Y[SUP]1,[/SUP][SUP]2[/SUP], Nagatomo M[SUP]2[/SUP], Yokoyama R[SUP]2[/SUP], Ohzono M[SUP]2[/SUP], Aoyama K[SUP]2[/SUP], Zhang X[SUP]2[/SUP], Nakajima K[SUP]3[/SUP], Murakami N[SUP]4[/SUP], Shinoda T[SUP]5[/SUP], Hirota T[SUP]6[/SUP], Yanagihara S[SUP]6[/SUP], Nishi JI[SUP]7[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] A highly sensitive and convenient method for detecting influenza virus was developed using modified end-point melt curve analysis of a RT-qPCR SYBR Green method and influenza virus-binding sugar chain-immobilized gold-nanoparticles (SGNP). Because SGNPs capture influenza viruses, the virus-SGNP complex was separated easily by centrifugation. Viral RNA was detected at very low concentrations, suggesting that SGNP increased sensitivity compared with standard methods. This method was applied to clinical studies. Influenza viruses were detected in saliva of patients or inpatients who had been considered influenza-free by a rapid diagnostic assay of nasal swabs. Furthermore, the method was applied to a human trial of prophylactic anti-influenza properties of yogurt containing Lactobacillus acidophilus L-92. The incidence of influenza viruses in saliva of the L-92 group was found to be significantly lower compared to the control group. Thus, this method was useful for monitoring the course of anti-influenza treatment or preventive measures against nosocomial infection.
[h=4]KEYWORDS:[/h] Detecting influenza virus; HA, hemagglutinin; Lactobacillus acidophilus L-92; RT-qPCR-SYBR, reverse transcription real-time quantitative PCR SYBR Green method; SC, Sugar Chip; SGNP, sugar chain-immobilized gold nanoparticles; SPR, surface plasmon resonance; Sugar chain-immobilized gold-nanoparticle
PMID: 28626716 PMCID: PMC5466045 DOI: 10.1016/j.btre.2015.05.004
Free full text
[h=1]Highly sensitive detection of influenza virus in saliva by real-time PCR method using sugar chain-immobilized gold nanoparticles; application to clinical studies.[/h] Suda Y[SUP]1,[/SUP][SUP]2[/SUP], Nagatomo M[SUP]2[/SUP], Yokoyama R[SUP]2[/SUP], Ohzono M[SUP]2[/SUP], Aoyama K[SUP]2[/SUP], Zhang X[SUP]2[/SUP], Nakajima K[SUP]3[/SUP], Murakami N[SUP]4[/SUP], Shinoda T[SUP]5[/SUP], Hirota T[SUP]6[/SUP], Yanagihara S[SUP]6[/SUP], Nishi JI[SUP]7[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] A highly sensitive and convenient method for detecting influenza virus was developed using modified end-point melt curve analysis of a RT-qPCR SYBR Green method and influenza virus-binding sugar chain-immobilized gold-nanoparticles (SGNP). Because SGNPs capture influenza viruses, the virus-SGNP complex was separated easily by centrifugation. Viral RNA was detected at very low concentrations, suggesting that SGNP increased sensitivity compared with standard methods. This method was applied to clinical studies. Influenza viruses were detected in saliva of patients or inpatients who had been considered influenza-free by a rapid diagnostic assay of nasal swabs. Furthermore, the method was applied to a human trial of prophylactic anti-influenza properties of yogurt containing Lactobacillus acidophilus L-92. The incidence of influenza viruses in saliva of the L-92 group was found to be significantly lower compared to the control group. Thus, this method was useful for monitoring the course of anti-influenza treatment or preventive measures against nosocomial infection.
[h=4]KEYWORDS:[/h] Detecting influenza virus; HA, hemagglutinin; Lactobacillus acidophilus L-92; RT-qPCR-SYBR, reverse transcription real-time quantitative PCR SYBR Green method; SC, Sugar Chip; SGNP, sugar chain-immobilized gold nanoparticles; SPR, surface plasmon resonance; Sugar chain-immobilized gold-nanoparticle
PMID: 28626716 PMCID: PMC5466045 DOI: 10.1016/j.btre.2015.05.004
Free full text