김영재 교수

BACKGROUND: Streptococcus mutans and Streptococcus sobrinus are closely associated with the development of early childhood caries (ECC). Recently, quantitative real-time polymerase chain reaction (qRT-PCR) has been used for rapid and accurate quantification of these bacterial species. AIM: This study aims to detect quantitatively the levels of S. mutans and S. sobrinus in plaque samples by qRT-PCR, and to assess their association with the prevalence of ECC in Korean preschool children. DESIGN: O

The orphan nuclear receptor ESRRA (estrogen related receptor alpha) is critical in mitochondrial biogenesis and macroautophagy/autophagy function; however, the roles of ESRRA in intestinal function remain uncharacterized. Herein we identified that ESRRA acts as a key regulator of intestinal homeostasis by amelioration of colonic inflammation through activation of autophagic flux and control of host gut microbiota. Esrra-deficient mice presented with increased susceptibility to dextran sodium sul

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The proposed web-based deep learning approach can be very practical and accurate for spine segmentation as a diagnostic method.

The purpose of this study was to explore the effects of CT slice thickness, reconstruction algorithm, and radiation dose on quantification of CT features to characterize lung nodules using a chest phantom. Spherical lung nodule phantoms of known densities (−630 and + 100 HU) were inserted into an anthropomorphic thorax phantom. CT scan was performed ten times with relocations. CT data were reconstructed using 12 different imaging settings; three different slice thicknesses of 1.25, 2.5, and 5.0

The association between tumor-associated macrophages (TAMs) and the expression of immune checkpoint molecules has not been well described in cutaneous melanoma. We evaluated the correlations between the expression of markers of TAMs, cluster of differentiation 163 (CD163), and immune checkpoint molecules, programmed cell death protein-1 (PD-1), and lymphocyte activating gene-3 (LAG-3). We also determined their relationships with the clinicopathological features and disease outcomes in melanoma.

This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, n = 213; no OSA, n = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, n = 149; no OSA, n = 46) and test data (OSA, n = 64; no OSA, n = 20). Using the seven clinical indices, the OSA pr

As a standalone method, the algorithm analyzed photographs taken by non-physician and showed comparable accuracy for the diagnosis of onychomycosis to that made by experienced dermatologists and by dermoscopic examination. Large sample size and world-wide, multicentered studies should be investigated to prove the performance of the algorithm.

While colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the Image

The vertebral compression is a significant factor for determining the prognosis of osteoporotic vertebral compression fractures and is generally measured manually by specialists. The consequent misdiagnosis or delayed diagnosis can be fatal for patients. In this study, we trained and evaluated the performance of a vertebral body segmentation model and a vertebral compression measurement model based on convolutional neural networks. For vertebral body segmentation, we used a recurrent residual U-