Hyun Ju Lee’s research while affiliated with Harvard Medical School and other places

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices showed the potential to detect fetuses with VM. The purpose of this study examines the diagnostic performance of deep learning-based fetal brain age prediction model to distinguish fetuses with VM ( n = 317) from typically developing fetuses ( n = 183), the severity of VM, and the presence of associated CNS abnormalities. The predicted age difference (PAD) was measured by subtracting predicted brain age from gestational age in fetuses with VM and typically development. PAD and absolute value of PAD (AAD) were compared between VM and typically developing fetuses. In addition, PAD and AAD were compared between subgroups by VM severity and the presence of associated CNS abnormalities in VM. Fetuses with VM showed significantly larger AAD than typically developing ( P < .001), and fetuses with severe VM showed larger AAD than those with moderate VM ( P = .004). Fetuses with VM and associated CNS abnormalities had significantly lower PAD than fetuses with isolated VM ( P = .005). These findings suggest that fetal brain age prediction using the 2D single-channel CNN method has the clinical ability to assist in identifying not only the enlargement of the ventricles but also the presence of associated CNS abnormalities. ©RSNA, 2025

Background Corneal transplantation is the most common transplant procedure worldwide. Despite immune and angiogenic privilege of the cornea, 50% to 70% of corneal transplants fail in high-risk recipients, primarily because of immune rejection. Therefore, it is crucial to identify predictive biomarkers of rejection to improve transplant survival. Methods In search for predictive biomarkers, we performed proteomics analysis of serum extracellular vesicles (EVs) in a fully major histocompatibility complex–mismatched (C57BL/6-to-BALB/c) murine corneal transplantation model, wherein 50% of transplants undergo rejection by day 28 following transplantation. Results Our time course study revealed a decrease in the number of serum EVs on day 1, followed by a gradual increase by day 7. A comparative analysis of proteomics profiles of EVs from transplant recipients with rejection (rejectors) and without rejection (nonrejectors) found a distinct enrichment of histocompatibility 2, Q region locus 2, which is a part of major histocompatibility complex-class I of donor C57BL/6 mice, in day 7 EVs of rejectors, compared with nonrejectors, syngeneic controls, or naïve mice. In contrast, serum amyloid A2, a protein induced in response to injury, was increased in day 7 EVs of nonrejectors. Conclusions Our findings offer noninvasive EV-based potential biomarkers for predicting corneal allograft rejection or tolerance.

Our previous study demonstrated that mesenchymal stem/stromal cells (MSCs) induce the differentiation of myeloid-derived suppressor cells (MDSCs) in the bone marrow (BM) under inflammatory conditions. In this study, we aimed to investigate the signaling pathway involved. RNA-seq revealed that the mitogen-activated protein kinase (MAPK) pathway exhibited the highest number of upregulated genes in MSC-induced MDSCs. Western blot analysis confirmed the strong phosphorylation of c-Jun N-terminal kinase (JNK) in BM cells cocultured with MSCs under granulocyte-macrophage colony-stimulating factor stimulation, whereas p38 kinase activation remained unchanged in MSC-cocultured BM cells. JNK inhibition by SP600125 abolished the expression of Arg1 and Nos2, hallmark genes of MDSCs, as well as Hif1a, a molecule mediating monocyte functional reprogramming toward a suppressive phenotype, in MSC-cocultured BM cells. JNK inhibition also abrogated the effects of MSCs on the production of TGF-β1, TGF-β2 and IL-10 in BM cells. Furthermore, JNK inhibition increased Tnfa expression, while suppressing IL-10 production, in MSC-cocultured BM cells in response to lipopolysaccharides. Collectively, our results suggest that MSCs induce MDSC differentiation and promote immunoregulatory cytokine production in BM cells during inflammation, at least in part, through the activation of the JNK–MAPK signaling pathway.

Our previous study demonstrated that mesenchymal stem/stromal cells (MSCs) induced the differentiation of myeloid-derived suppressor cells (MDSCs) in the bone marrow (BM) under inflammatory conditions. In the present study, we aimed to explore the signaling pathway in BM cells responsible for MDSC generation. RNA-seq revealed that the mitogen-activated protein kinase (MAPK) pathways, among biochemical pathways, exhibited the highest number of differentially expressed genes in BM cells with MSC coculture relative to those without. Western blot confirmed strong phosphorylation of c-Jun N-terminal kinase (JNK) in BM cells cocultured with MSCs under GM-CSF stimulation, whereas p38 kinase activation remained unaltered by MSCs. Inhibition of JNK by SP600125 significantly abolished the expression of Arg1 and Nos2, the hallmark genes of MDSCs, as well as Hif1α, a molecule mediating monocyte functional re-programming into suppressive phenotype, in MSC-cocultured BM cells, while increasing TNF-α in response to lipopolysaccharides stimulation. Furthermore, JNK inhibition markedly abrogated the effect of MSCs on the production of TGF-β1, TGF-β2 and IL-10 in BM cells. These results suggest that MSCs induce MDSC differentiation in BM during inflammation, at least in part, through the activation of the JNK MAPK signaling pathway.

Objectives Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. Methods In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. Results Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. Conclusions Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. Clinical relevance statement Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. Key Points •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

Normal cortical growth and the resulting folding patterns are crucial for normal brain function. Although cortical development is largely influenced by genetic factors, environmental factors in fetal life can modify the gene expression associated with brain development. As the placenta plays a vital role in shaping the fetal environment, affecting fetal growth through the exchange of oxygen and nutrients, placental oxygen transport might be one of the environmental factors that also affect early human cortical growth. In this study, we aimed to assess the placental oxygen transport during maternal hyperoxia and its impact on fetal brain development using MRI in identical twins to control for genetic and maternal factors. We enrolled 9 pregnant subjects with monochorionic diamniotic twins (30.03 ± 2.39 gestational weeks [mean ± SD]). We observed that the fetuses with slower placental oxygen delivery had reduced volumetric and surface growth of the cerebral cortex. Moreover, when the difference between placenta oxygen delivery increased between the twin pairs, sulcal folding patterns were more divergent. Thus, there is a significant relationship between placental oxygen transport and fetal brain cortical growth and folding in monochorionic twins.