Researchers from the University of Tokyo, among others, developed Deep Nanometry. This analytical technique combines advanced optical equipment with an algorithm for noise removal based on unsupervised learning. Deep Nanometry is able to analyze nanoparticles at high speeds, allowing it to detect even small amounts of rare particles. This technology has shown its potential to detect extracellular vesicles that indicate early signs of colon carcinoma. It is hoped that this can be applied in other medical and industrial fields.
Do you know that your body contains microscopic particles smaller in size than cells? Extracellular vesicles are one of these. They can be used to detect disease early and for drug delivery. EVs, however, are extremely rare and finding them in a sea of millions of particles was a time-consuming and expensive process. Researchers, such as Yuichiro iwamoto, a postdoctoral research fellow at the Research Center for Advanced Science and Technology, and his team were prompted by this to develop a way to detect EVs rapidly and reliably. Iwamoto said
“Conventional measurement techniques often have limited throughput, making it difficult to reliably detect rare particles in a short space of time,” . “To address this, we developed Deep Nanometry (DNM), a new nanoparticle detection device and an unsupervised deep learning noise-reduction method to boost its sensitivity. This allows for high throughput, making it possible to detect rare particles such as EVs.”
The core of DNM’s ability to detect particles smaller than 30 nanometers (billionths a meter), while also being capable of detecting more than 100,000 particles in a second, is its ability. DNM can detect weak signals, which are often missed by conventional high-speed detectors. It’s like searching for a small vessel in a turbulent ocean with crashing waves. It would be much easier to find the boat if the waves dissipated, leaving a calmer ocean. Artificial intelligence (AI), which helps to filter out the waves’ behavior, can help in this regard.
The technology can be extended to a range of clinical diagnostics that rely upon particle detection. It also has potential for fields such as vaccine research and environmental monitoring. AI-based signal denoising could also be applied to electrical signals. Iwamoto said
“The development of DNM has been a very personal journey for me,” . “It is not only a scientific advancement, but also a tribute to my late mother, who inspired me to research the early detection of cancer. Our dream is to make life-saving diagnostics faster and more accessible to everyone.”
