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Roche Diagnostics Launches Next-Generation Digital PCR Equipment in Korea

Roche Diagnostics Korea announced on the 27th that its digital gene amplification reaction (PCR) device, the Digital LightCycler Analyzer, has recently received certification from the Korea Medical Device Safety Information Center and has been officially launched in the domestic market.


Roche Diagnostics Launches Next-Generation Digital PCR Equipment in Korea Korea Roche Diagnostics' digital PCR equipment 'Digital LightCycler Analyzer'
Photo by Korea Roche Diagnostics

The Digital LightCycler Analyzer quantifies trace amounts of specific DNA and ribonucleic acid (RNA) targets that cannot be detected by the conventional real-time PCR method for disease diagnosis. This helps clinicians and researchers better understand the characteristics of cancer diagnosis, genetic disorders, or infections.


Since this product uses chip-based digital PCR technology, unlike droplet-based digital PCR methods, it does not require the droplet generation process, allowing examiners to experience a more simplified workflow.


Three types of nanowell plates are also available, which can divide DNA or RNA extracted from specimens into 20,000 to 100,000 micro reaction units. This technology enables highly precise genetic analysis by partitioning samples into nanowell plates, conducting PCR tests, automatically converting images into data, providing fast and accurate values, and processing more data.


In terms of test result management, analysis results can be directly extracted as Excel files, making result management convenient. This allows researchers to easily organize analysis results, track samples effortlessly, and efficiently analyze test outcomes.


Kit Tang, CEO of Roche Diagnostics Korea, stated, “Digital PCR technology offers new opportunities by enabling early diagnosis of cancer genetic disorders and detecting minute cancer cells that may remain after treatment through excellent sensitivity in oncology and infectious disease fields, making it possible to predict treatment effectiveness and risk of recurrence.”


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