東京大学 · Biochemistry, Genetics and Molecular Biology
Keisuke Goda 교수의 연구실은 초고속 이미징 및 광학 분석 기술을 기반으로 한 혁신적인 생물의학 및 나노소재 연구를 수행하고 있습니다. 특히, 유체 속에서의 단일 입자 실시간 관측, 광학 스펙트로스코피, 그리고 머신러닝 기반 레이저 프리 이미징 기술을 접목해 약물 스クリ닝과 생체 진단의 정밀도와 속도를 획기적으로 향상시키는 데 주력하고 있습니다. 또한, 생물학적 신뢰성과 내구성을 확보한 메탈 프리 SERS 기반 센서 개발을 통해 임상 적용 가능성을 높이고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Optical microscopy is one of the most widely used diagnostic methods in scientific, industrial, and biomedical applications. However, while useful for detailed examination of a small number (< 10,000) of microscopic entities, conventional optical microscopy is incapable of statistically relevant screening of large populations (> 100,000,000) with high precision due to its low throughput and limited digital memory size. We present an automated flow-through single-particle optical microscope that
Amplified dispersive Fourier transformation (ADFT) is a powerful technique that maps the spectrum of an optical pulse into a time-domain waveform using group-velocity dispersion (GVD) and simultaneously amplifies it in the optical domain. It replaces a diffraction grating and detector array with a dispersive fiber and single photodetector, greatly simplifying the system and, more importantly, enabling ultrafast real-time spectroscopic measurements. Here we present a theory of ADFT by deriving th
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for vibrational spectroscopy as it provides several orders of magnitude higher sensitivity than inherently weak spontaneous Raman scattering by exciting localized surface plasmon resonance (LSPR) on metal substrates. However, SERS can be unreliable for biomedical use since it sacrifices reproducibility, uniformity, biocompatibility, and durability due to its strong dependence on "hot spots", large photothermal heat generation, and eas
In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imagi
