Glucose homeostasis is tightly controlled by the regulation of glucose production in the liver and glucose uptake into peripheral tissues, such as skeletal muscle and adipose tissue. Under prolonged fasting, hepatic gluconeogenesis is mainly responsible for glucose production in the liver, which is essential for tissues, organs, and cells, such as skeletal muscle, the brain, and red blood cells. Hepatic gluconeogenesis is controlled in part by the concerted actions of transcriptional regulators. Fasting signals are relayed by various intracellular enzymes, such as kinases, phosphatases, acetyltransferases, and deacetylases, which affect the transcriptional activity of transcription factors and transcriptional coactivators for gluconeogenic genes. Protein arginine methyltransferases (PRMTs) were recently added to the list of enzymes that are critical for regulating transcription in hepatic gluconeogenesis. In this review, we briefly discuss general aspects of PRMTs in the control of transcription. More specifically, we summarize the roles of four PRMTs: PRMT1, PRMT 4, PRMT 5, and PRMT 6, in the control of hepatic gluconeogenesis through specific regulation of FoxO1- and CREB-dependent transcriptional events.
Acetyltransferases ; Adipose Tissue ; Arginine* ; Brain ; Erythrocytes ; Fasting ; Gluconeogenesis ; Glucose* ; Homeostasis ; Liver ; Metabolism* ; Methyltransferases* ; Muscle, Skeletal ; Phosphoric Monoester Hydrolases ; Phosphotransferases ; Protein-Arginine N-Methyltransferases ; Transcription Factors

This study compares biomarker levels among environmentally vulnerable residents in Korea, the general Korean population, and Asians in the United States. We selected 953 exposed residents and 204 controls from the Forensic Research via Omics Markers in Environmental Health Vulnerable Areas (FROM) study (2021-2023), 4,239 participants from the fourth Korean National Environmental Health Survey (2018-2020), and 996 Asians from the U.S. National Health and Nutrition Examination Survey (2017-March 2020). The analyzed biomarkers included blood and urinary metals, urinary metabolites of polycyclic aromatic hydrocarbons, nicotine, volatile organic compounds, and serum perfluorocarbon metabolites. The highest median biomarker levels varied by pollution source among older adults. In refineries, blood lead and cadmium (Cd), as well as urinary Cd and 2-hydroxyfluorene, were highest. Abandoned metal mines exhibited the highest blood and urinary mercury, urinary Cd, total arsenic (As), 2-naphthol, and cotinine levels. Coal-fired power plants showed the highest urinary 1- hydroxyphenanthrene levels, while cement factories had the highest urinary As3+ levels. Sprawls demonstrated the highest urinary monomethylarsonic acid, 1-hydroxypyrene, and phenylglyoxylic acid levels, and industrial areas recorded the highest levels of trans, trans-muconic acid, benzylmercapturic acid, and 2-methylhippuric acid. In general, biomarker levels were higher among exposed residents in the FROM study than in the general population; however, urinary 2-hydroxyfluorene and As5+ levels did not differ significantly. Exposure to pollution sources in environmentally vulnerable areas may elevate biomarker levels in residents.

This study compares biomarker levels among environmentally vulnerable residents in Korea, the general Korean population, and Asians in the United States. We selected 953 exposed residents and 204 controls from the Forensic Research via Omics Markers in Environmental Health Vulnerable Areas (FROM) study (2021-2023), 4,239 participants from the fourth Korean National Environmental Health Survey (2018-2020), and 996 Asians from the U.S. National Health and Nutrition Examination Survey (2017-March 2020). The analyzed biomarkers included blood and urinary metals, urinary metabolites of polycyclic aromatic hydrocarbons, nicotine, volatile organic compounds, and serum perfluorocarbon metabolites. The highest median biomarker levels varied by pollution source among older adults. In refineries, blood lead and cadmium (Cd), as well as urinary Cd and 2-hydroxyfluorene, were highest. Abandoned metal mines exhibited the highest blood and urinary mercury, urinary Cd, total arsenic (As), 2-naphthol, and cotinine levels. Coal-fired power plants showed the highest urinary 1- hydroxyphenanthrene levels, while cement factories had the highest urinary As3+ levels. Sprawls demonstrated the highest urinary monomethylarsonic acid, 1-hydroxypyrene, and phenylglyoxylic acid levels, and industrial areas recorded the highest levels of trans, trans-muconic acid, benzylmercapturic acid, and 2-methylhippuric acid. In general, biomarker levels were higher among exposed residents in the FROM study than in the general population; however, urinary 2-hydroxyfluorene and As5+ levels did not differ significantly. Exposure to pollution sources in environmentally vulnerable areas may elevate biomarker levels in residents.

This study compares biomarker levels among environmentally vulnerable residents in Korea, the general Korean population, and Asians in the United States. We selected 953 exposed residents and 204 controls from the Forensic Research via Omics Markers in Environmental Health Vulnerable Areas (FROM) study (2021-2023), 4,239 participants from the fourth Korean National Environmental Health Survey (2018-2020), and 996 Asians from the U.S. National Health and Nutrition Examination Survey (2017-March 2020). The analyzed biomarkers included blood and urinary metals, urinary metabolites of polycyclic aromatic hydrocarbons, nicotine, volatile organic compounds, and serum perfluorocarbon metabolites. The highest median biomarker levels varied by pollution source among older adults. In refineries, blood lead and cadmium (Cd), as well as urinary Cd and 2-hydroxyfluorene, were highest. Abandoned metal mines exhibited the highest blood and urinary mercury, urinary Cd, total arsenic (As), 2-naphthol, and cotinine levels. Coal-fired power plants showed the highest urinary 1- hydroxyphenanthrene levels, while cement factories had the highest urinary As3+ levels. Sprawls demonstrated the highest urinary monomethylarsonic acid, 1-hydroxypyrene, and phenylglyoxylic acid levels, and industrial areas recorded the highest levels of trans, trans-muconic acid, benzylmercapturic acid, and 2-methylhippuric acid. In general, biomarker levels were higher among exposed residents in the FROM study than in the general population; however, urinary 2-hydroxyfluorene and As5+ levels did not differ significantly. Exposure to pollution sources in environmentally vulnerable areas may elevate biomarker levels in residents.

This study compares biomarker levels among environmentally vulnerable residents in Korea, the general Korean population, and Asians in the United States. We selected 953 exposed residents and 204 controls from the Forensic Research via Omics Markers in Environmental Health Vulnerable Areas (FROM) study (2021-2023), 4,239 participants from the fourth Korean National Environmental Health Survey (2018-2020), and 996 Asians from the U.S. National Health and Nutrition Examination Survey (2017-March 2020). The analyzed biomarkers included blood and urinary metals, urinary metabolites of polycyclic aromatic hydrocarbons, nicotine, volatile organic compounds, and serum perfluorocarbon metabolites. The highest median biomarker levels varied by pollution source among older adults. In refineries, blood lead and cadmium (Cd), as well as urinary Cd and 2-hydroxyfluorene, were highest. Abandoned metal mines exhibited the highest blood and urinary mercury, urinary Cd, total arsenic (As), 2-naphthol, and cotinine levels. Coal-fired power plants showed the highest urinary 1- hydroxyphenanthrene levels, while cement factories had the highest urinary As3+ levels. Sprawls demonstrated the highest urinary monomethylarsonic acid, 1-hydroxypyrene, and phenylglyoxylic acid levels, and industrial areas recorded the highest levels of trans, trans-muconic acid, benzylmercapturic acid, and 2-methylhippuric acid. In general, biomarker levels were higher among exposed residents in the FROM study than in the general population; however, urinary 2-hydroxyfluorene and As5+ levels did not differ significantly. Exposure to pollution sources in environmentally vulnerable areas may elevate biomarker levels in residents.

OBJECTIVES: Electrocardiogram (ECG) data are important for the study of cardiovascular disease and adverse drug reactions. Although the development of analytical techniques such as machine learning has improved our ability to extract useful information from ECGs, there is a lack of easily available ECG data for research purposes. We previously published an article on a database of ECG parameters and related clinical data (ECG-ViEW), which we have now updated with additional 12-lead waveform information. METHODS: All ECGs stored in portable document format (PDF) were collected from a tertiary teaching hospital in Korea over a 23-year study period. We developed software which can extract all ECG parameters and waveform information from the ECG reports in PDF format and stored it in a database (meta data) and a text file (raw waveform). RESULTS: Our database includes all parameters (ventricular rate, PR interval, QRS duration, QT/QTc interval, P-R-T axes, and interpretations) and 12-lead waveforms (for leads I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6) from 1,039,550 ECGs (from 447,445 patients). Demographics, drug exposure data, diagnosis history, and laboratory test results (serum calcium, magnesium, and potassium levels) were also extracted from electronic medical records and linked to the ECG information. CONCLUSIONS: Electrocardiogram information that includes 12 lead waveforms was extracted and transformed into a form that can be analyzed. The description and programming codes in this case report could be a reference for other researchers to build ECG databases using their own local ECG repository.
Calcium ; Cardiovascular Diseases ; Demography ; Diagnosis ; Drug-Related Side Effects and Adverse Reactions ; Electrocardiography* ; Electronic Health Records ; Hospitals, Teaching ; Korea ; Machine Learning ; Magnesium ; Potassium

To this date, the criteria to distinguish peritoneal macrophages and dendritic cells (DCs) are not clear. Here we delineate the subsets of myeloid mononuclear cells in the mouse peritoneal cavity. Considering phenotypical, functional, and ontogenic features, peritoneal myeloid mononuclear cells are divided into 5 subsets: large peritoneal macrophages (LPMs), small peritoneal macrophages (SPMs), DCs, and 2 MHCII⁺CD11c⁺CD115⁺ subpopulations (i.e., MHCII⁺CD11c⁺CD115⁺CD14⁻CD206⁻ and MHCII⁺CD11c⁺CD115⁺CD14⁺CD206⁺). Among them, 2 subsets of competent Ag presenting cells are demonstrated with distinct functional characteristics, one being DCs and the other being MHCII⁺CD11c⁺CD115⁺CD14⁻CD206⁻ cells. DCs are able to promote fully activated T cells and superior in expanding cytokine producing inflammatory T cells, whereas MHCII⁺CD11c⁺CD115⁺CD14⁻CD206⁻ cells generate partially activated T cells and possess a greater ability to induce Treg under TGF-β and retinoic acid conditions. While the development of DCs and MHCII⁺CD11c⁺CD115⁺CD14⁻CD206⁻ cells are responsive to the treatment of FLT3 ligand and GM-CSF, the number of LPMs, SPMs, and MHCII⁺CD11c⁺CD115⁺CD14⁺CD206⁺ cells are only influenced by the injection of GM-CSF. In addition, the analysis of gene expression profiles among MHCII⁺ peritoneal myeloid mononuclear cells reveals that MHCII⁺CD11c⁺CD115⁺CD14⁺CD206⁺ cells share high similarity with SPMs, whereas MHCII⁺CD11c⁺CD115⁺CD14⁻CD206⁻ cells are related to peritoneal DC2s. Collectively, our study identifies 2 distinct subpopulations of MHCII⁺CD11c⁺CD115⁺ cells, 1) MHCII⁺CD11c⁺CD115⁺CD14⁻CD206⁻ cells closely related to peritoneal DC2s and 2) MHCII⁺CD11c⁺CD115⁺CD14⁺CD206⁺ cells to SPMs.
Animals ; Antigen Presentation ; Dendritic Cells ; Granulocyte-Macrophage Colony-Stimulating Factor ; Macrophages ; Macrophages, Peritoneal ; Mice ; Peritoneal Cavity ; T-Lymphocytes ; Transcriptome ; Tretinoin

Background and Objectives: Spermatogonial stem cells (SSCs) are the most primitive cells in spermatogenesis and are the only adult stem cells capable of passing on the genome of a given species to the next generation. SSCs are the only adult stem cells known to exhibit high Oct4 expression and can be induced to self-reprogram into pluripotent cells depending on culture conditions. Epigenetic modulation is well known to be involved in the induction of pluripotency of somatic cells. However, epigenetic modulation in self-reprogramming of SSCs into pluripotent cells has not been studied. Methods: and Results: In this study, we examined the involvement of epigenetic modulation by assessing whether selfreprogramming of SSCs is enhanced by treatment with epigenetic modulators. We found that second-generation selective class I HDAC inhibitors increased SSC reprogramming efficiency, whereas non-selective HDAC inhibitors had no effect. Conclusions We showed that pluripotent stem cells derived from adult SSCs by treatment with small molecules with epigenetic modulator functions exhibit pluripotency in vitro and in vivo. Our results suggest that the mechanism of SSC reprogramming by epigenetic modulator can be used for important applications in epigenetic reprogramming research.

Background: South Korea has been experiencing a third wave of coronavirus disease 2019 (COVID-19) since mid-November 2020. Our hospital in Gwangju metropolitan city experienced a healthcare-associated COVID-19 outbreak early in the third wave. The first confirmed COVID-19 patient was a symptomatic neurosurgery resident with high mobility throughout the hospital. We analyzed the transmission routes of nosocomial COVID-19 and discussed infection control strategies. Methods: We retrospectively analyzed the severe acute respiratory syndrome coronavirus 2 reverse transcription-polymerase chain reaction (RT-PCR) testing results according to time point and evaluated transmission routes. Results: Since COVID-19 was first confirmed in a healthcare worker (HCW) on 11/13/2020, we performed RT-PCR tests for all patients and caregivers and four complete enumeration surveys for all HCWs. We detected three clusters of nosocomial spread and several sporadic cases. The first cluster originated from the community outbreak spot, where an asymptomatic HCW visited, which led to a total of 22 cases. The second cluster, which included patient-to-patient transmission, originated from a COVID-19 positive caregiver before diagnosis and the third cluster involved a radiologist and a banker. We took measures to isolate Building 1 of the hospital for 17 days and controlled the outbreak during a period of increasing community COVID-19 prevalence. Universal screening of all inpatients upon admission and resident caregivers was made mandatory and hospital-related employees are now screened monthly. Conclusion Infection control strategies to prevent the nosocomial transmission of emerging infectious diseases must correspond with community disease prevalence. Our data reinforce the importance of multi-time point surveillance of asymptomatic HCWs and routine surveillance of patients and caregivers during an epidemic.

Background: South Korea has been experiencing a third wave of coronavirus disease 2019 (COVID-19) since mid-November 2020. Our hospital in Gwangju metropolitan city experienced a healthcare-associated COVID-19 outbreak early in the third wave. The first confirmed COVID-19 patient was a symptomatic neurosurgery resident with high mobility throughout the hospital. We analyzed the transmission routes of nosocomial COVID-19 and discussed infection control strategies. Methods: We retrospectively analyzed the severe acute respiratory syndrome coronavirus 2 reverse transcription-polymerase chain reaction (RT-PCR) testing results according to time point and evaluated transmission routes. Results: Since COVID-19 was first confirmed in a healthcare worker (HCW) on 11/13/2020, we performed RT-PCR tests for all patients and caregivers and four complete enumeration surveys for all HCWs. We detected three clusters of nosocomial spread and several sporadic cases. The first cluster originated from the community outbreak spot, where an asymptomatic HCW visited, which led to a total of 22 cases. The second cluster, which included patient-to-patient transmission, originated from a COVID-19 positive caregiver before diagnosis and the third cluster involved a radiologist and a banker. We took measures to isolate Building 1 of the hospital for 17 days and controlled the outbreak during a period of increasing community COVID-19 prevalence. Universal screening of all inpatients upon admission and resident caregivers was made mandatory and hospital-related employees are now screened monthly. Conclusion Infection control strategies to prevent the nosocomial transmission of emerging infectious diseases must correspond with community disease prevalence. Our data reinforce the importance of multi-time point surveillance of asymptomatic HCWs and routine surveillance of patients and caregivers during an epidemic.