Hongyun Choi

Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal

Structural MR images concomitantly acquired with PET images can provide crucial anatomic information for precise quantitative analysis. However, in the clinical setting, not all the subjects have corresponding MR images. Here, we developed a model to generate structural MR images from amyloid PET using deep generative networks. We applied our model to quantification of cortical amyloid load without structural MR. <b>Methods:</b> We used florbetapir PET and structural MR data from the Alzheimer D

Extracellular vesicles play a crucial role in intercellular communication by transmitting biological materials from donor cells to recipient cells. They have pathophysiologic roles in cancer metastasis, neurodegenerative diseases, and inflammation. Extracellular vesicles also show promise as emerging therapeutics, with understanding of their physiology including targeting, distribution, and clearance therefore becoming an important issue. Here, we review recent advances in methods for tracking a

Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from

ApoE, ApoE receptors and APP cooperate in the pathogenesis of Alzheimer's disease. Intriguingly, the ApoE receptor LRP4 and APP are also required for normal formation and function of the neuromuscular junction (NMJ). In this study, we show that APP interacts with LRP4, an obligate co-receptor for muscle-specific tyrosine kinase (MuSK). Agrin, a ligand for LRP4, also binds to APP and co-operatively enhances the interaction of APP with LRP4. In cultured myotubes, APP synergistically increases agri

We suggest that deep learning model trained only by normal data was applicable for identifying wide-range of abnormalities in brain diseases, even uncommon ones, proposing its possible use for interpreting real-world clinical data.

Although metabolic modulation in the tumor microenvironment (TME) is one of the key mechanisms of cancer immune escape, there is a lack of understanding of the comprehensive immune landscape of the TME and its association with tumor metabolism based on clinical evidence. We aimed to investigate the relationship between the immune landscape in the TME and tumor glucose metabolism in lung adenocarcinoma. <b>Methods:</b> Using RNA sequencing and image data, we developed a transcriptome-based tumor

BACKGROUND: F]fluorodeoxyglucose (FDG) PET. METHODS: We used an AD animal model, 5xFAD, to analyze hippocampal glucose metabolism using both animal FDG PET and ex vivo FDG uptake test. Cells of the hippocampus were isolated to perform single-cell RNA-sequencing (scRNA-seq). The molecular features of cells associated with glucose metabolism were analyzed at a single-cell level. In order to apply our findings to human brain imaging study, brain FDG PET data obtained from the Alzheimer's Disease Ne

Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene expression patterns by deep learning of tissue images (SPADE), to identify important genes associated with morphological contexts by combining spatial transcriptomic data with coregistered images. SPADE incorporates deep learning-derived image patte

BACKGROUND: The principle of loss of iodine uptake and increased glucose metabolism according to dedifferentiation of thyroid cancer is clinically assessed by imaging. Though these biological properties are widely applied to appropriate iodine therapy, the understanding of the genomic background of this principle is still lacking. We investigated the association between glucose metabolism and differentiation in advanced thyroid cancer as well as papillary thyroid cancer (PTC). METHODS: We used R