YongKeun Park

This paper develops a label-free 3D holotomography method to track lipid droplet dynamics in hepatic organoids under different fatty-acid treatments, revealing distinct accumulation strategies.

This paper introduces holotomography (HT) for label-free volumetric imaging of collagen architecture, showing its ability to resolve fiber morphology, orientation, and mass without exogenous labels.

This paper uses holotomography to analyze 3D biophysical changes in mouse oocytes during post-ovulatory aging, showing that non-morphological features better distinguish aged from fresh oocytes.

DeepHOPE uses deep learning and 3D imaging to assess pluripotency in hPSCs, detecting structural changes before molecular loss of pluripotency.

This paper introduces a label-free method using holotomography and AI to quantify hepatic stellate cell activation in live cells, distinguishing quiescent and activated states.

This paper develops a label-free deep learning framework using holotomography to classify cell death pathways with high accuracy.

This paper develops a 3D virtual H&E staining method using holotomography and deep learning for cancer tissues, validated on colon and gastric cancer samples.

This study uses low-coherence holotomography to track adipocyte differentiation and lipid droplet dynamics over 42 days without labeling.

This paper introduces a label-free 3D imaging technique to predict blastocyst formation in mouse embryos using machine learning.

This study compares microfluidic and centrifugation methods for isolating white blood cells, finding microfluidic separation better preserves cell functionality.

This study introduces low-coherence holotomography for long-term live imaging of unlabeled mouse small intestinal organoids, capturing high-resolution morphological details and differentiating viable from non-viable organoids.

This study uses holotomography and machine learning to classify functional immune states of human monocytes, achieving high accuracy in identifying hyper-inflammatory and immune-suppressed states.

This paper introduces a label-free method to monitor 3D neuronal growth in vitro using optical diffraction tomography, enabling visualization of neurite outgrowth without fixation.

This paper introduces a deep learning framework for 3D label-free tracking of immunological synapses using optical diffraction tomography and segmentation.

This paper introduces a rapid, non-invasive method using laser speckles to monitor bacterial growth and perform antimicrobial susceptibility tests in real time, reducing testing time by 4-8 hours without compromising accuracy.

This paper introduces holotomography as a 3-D label-free imaging technique using refractive index to study live cell dynamics across various biological fields.

This study presents 3D refractive index imaging of angiosperm pollen grains using optical diffraction tomography, enabling quantitative analysis of structural features without labeling.

This paper uses optical diffraction tomography to create 3D refractive index maps of Pinus pollen grains, revealing morphological features and interspecific differences.

The paper uses optical diffraction tomography to measure morphological and biochemical changes in individual neurons from an in vitro Parkinson’s disease model, revealing early apoptotic features.