On the 4th, Ko Kyung-cheol, head of the National Preclinical Trial Support Center at the Korea Research Institute of Bioscience and Biotechnology (KRIBB), and Kwon O-seok, a professor at Sungkyunkwan University, announced that they have developed a new diagnostic platform technology called "plasmonic photothermal-based digital polymerase chain reaction (PCR)" that can analyze genetic information at ultra-fast and high precision.
Currently, real-time PCR used in medical settings takes a long time in the heating and cooling processes, requires large equipment, and incurs significant expenses. Additionally, it has shown limitations in quickly responding to infectious diseases, periodontal diseases, and cancers, where early diagnosis and rapid treatment are key, as it is difficult to use directly on-site.
To overcome these limitations, the researchers utilized metallic materials (plasmonic materials) that convert light into heat. In particular, they covered a gold nanoparticle film with a compound called OPE (oligo(phenylene-ethynylene)), which rapidly converts light energy into heat and maximizes heat transfer speed and efficiency, thus improving the accuracy and stability of the gene amplification process.
Using this technology, verification experiments were conducted on the genetic material of four types of bacteria, including Streptococcus mutans, the main causative bacteria of periodontal disease, and Porphyromonas gingivalis. As a result, the genetic amplification was completed in 14 minutes, and the presence of the gene could be confirmed through high-resolution fluorescence scanning within 9 minutes thereafter. It was able to detect extremely low amounts of genetic material, making it about four times faster and about ten times more sensitive than conventional PCR methods.
Ko Kyung-cheol noted, "This platform technology can be utilized not only for infectious diseases but also for diagnosing various illnesses such as cancers and food poisoning, and it will help improve the prediction rate of new drug development based on computer simulation using high-quality big data."
The results of this study were published online in the journal "ACS Nano" on the 24th of last month. The technology is expected to be transferred to SNBTech for commercialization.
References
ACS Nano (2025), DOI: https://doi.org/10.1021/acsnano.5c04674