During the three the coronavirus disease 2019 (COVID-19) surges in South Korea, there was a shortage of hospital beds for COVID-19 patients, and as a result, there were cases of death while waiting for hospitalization. To minimize the risk of death and to allow those confirmed with COVID-19 to safely wait for hospitalization at home, the local government of Gyeonggido in South Korea developed a novel home management system (HMS). The HMS team, comprised of doctors and nurses, was organized to operate HMS. HMS provided a two-way channel for the taskforce and patients to monitor the severity of patient's condition and to provide healthcare counseling as needed. In addition, the HMS team cooperated with a triage/bed assignment team to expedite the response in case of an emergency, and managed a database of severity for real-time monitoring of patients. The HMS became operational for the first time in August 2020, initially managing only 181 patients; it currently manages a total of 3,707 patients. The HMS supplemented the government's COVID-19 confirmed case management framework by managing patients waiting at home for hospitalization due to lack of hospital and residential treatment center beds. HMS also could contribute a sense of psychological stability in patients and prevented the situation from worsening by efficient management of hospital beds and reduction of workloads on public healthcare centers. To stabilize and improve the management of COVID-19 confirmed cases, governments should organically develop self-treatment and HMS, and implement a decisive division of roles within the local governments.

During the three the coronavirus disease 2019 (COVID-19) surges in South Korea, there was a shortage of hospital beds for COVID-19 patients, and as a result, there were cases of death while waiting for hospitalization. To minimize the risk of death and to allow those confirmed with COVID-19 to safely wait for hospitalization at home, the local government of Gyeonggido in South Korea developed a novel home management system (HMS). The HMS team, comprised of doctors and nurses, was organized to operate HMS. HMS provided a two-way channel for the taskforce and patients to monitor the severity of patient's condition and to provide healthcare counseling as needed. In addition, the HMS team cooperated with a triage/bed assignment team to expedite the response in case of an emergency, and managed a database of severity for real-time monitoring of patients. The HMS became operational for the first time in August 2020, initially managing only 181 patients; it currently manages a total of 3,707 patients. The HMS supplemented the government's COVID-19 confirmed case management framework by managing patients waiting at home for hospitalization due to lack of hospital and residential treatment center beds. HMS also could contribute a sense of psychological stability in patients and prevented the situation from worsening by efficient management of hospital beds and reduction of workloads on public healthcare centers. To stabilize and improve the management of COVID-19 confirmed cases, governments should organically develop self-treatment and HMS, and implement a decisive division of roles within the local governments.

Although microRNAs have emerged as key regulators in diverse cellular processes, the roles of microRNAs are poorly understood in human embryonic stem cells (hESCs) during differentiation into specialized cell types. In this study, we used a microRNA array with 799 human microRNA probes to examine the expression profiles of microRNAs in hESCs during differentiation into endodermal and mesodermal lineages in vitro. Among the microRNAs analyzed, 7 and 20 microRNAs were enriched in the developmental process of hESCs into mesodermal and endodermal lineages, respectively. In particular, the expression levels of miR-200 family, which is known to regulate the epithelial to mesenchymal transition (EMT), gradually increased in hESCs during differentiation into hepatocytes while they gradually decreased during differentiation into vascular endothelial cells. Downregulation of ZEB1, a direct target of miR-200 family, and E-CADHERIN, a target protein of ZEB1, was observed in hESCs during differentiation into endodermal and mesodermal lineages, respectively. These results indicate that miR-200 family has an important role in determining the cell fate between endodermal and mesodermal lineages from the pluripotent state.
Cadherins ; Down-Regulation ; Endoderm ; Endothelial Cells ; Hepatocytes ; Human Embryonic Stem Cells* ; Humans ; Humans* ; In Vitro Techniques* ; Mesoderm ; MicroRNAs

A substantial proportion of adrenal incidentalomas demonstrates subtle hormonal hypersecretion; however, adenomas that cosecrete aldosterone and cortisol are rare. We here report a case of an adrenal mass that was incidentally detected on a computed tomography scan in a 57-year-old man. The patient had a 10-year history of diabetes mellitus and a 5-year history of hypertension. Evaluation revealed hyperaldosteronemia with an elevated plasma aldosterone-to-renin ratio, hypokalemia, unsuppressed cortisol after dexamethasone administration, and elevated urinary free cortisol concentration. The appearance of the right adrenalectomy specimen indicated adrenal adenoma. Postoperatively, the blood glucose and blood pressure control improved and the urinary cortisol and aldosterone-to-renin ratio normalized. A complete endocrine evaluation in patients with incidentally discovered adrenal masses should be performed, even if the patient has a long-standing history of hypertension and diabetes, to avoid any postoperative adrenal crises.
Adenoma ; Adrenalectomy ; Adrenocortical Adenoma* ; Aldosterone* ; Blood Glucose ; Blood Pressure ; Dexamethasone ; Diabetes Mellitus ; Humans ; Hydrocortisone* ; Hyperaldosteronism ; Hypertension ; Hypokalemia ; Middle Aged ; Plasma

BACKGROUND/OBJECTIVES: Lactobacillus brevis G101 suppresses the absorption of monosodium glutamate (MSG) from the intestine into the blood in mice. Therefore, the attenuating effect of orally administered G101 on monosodium glutamate (MSG) symptom complex was investigated in humans. MATERIALS/METHODS: Capsules (300 mg) containing Lactobacillus brevis G101 (1x1010 CFU/individual) or maltodextrin (placebo) was orally administered in 30 respondents with self-recognized monosodium glutamate (MSG) symptom complex for 5 days and the rice with black soybean sauce containing 6 g MSG (RBSM) was ingested 30 min after the final administration. Thereafter, the MSG symptom complex (rated on a 5-point scale: 1, none; 5, strong) was investigated in a double blind placebo controlled study. The intensity of the MSG symptom complex was significantly reduced in respondents of the G101 intake group (2.87 +/- 0.73) compared to that in those treated with the placebo (3.63 +/- 1.03) (P = 0.0016). Respondents in the placebo group exhibited more of the various major conditions of the MSG symptom complex than in the G101 intake group. Although there was no significant difference in the appearance time of the MSG symptom complex between subjects orally administered G101 and those administered the placebo, its disappearance in < 3 h was observed in 69.9% of subjects in the G101 treatment group and in 38.0% of subjects in the placebo group (P = 0.0841). CONCLUSIONS: Oral administration of Lactobacillus brevis G101 may be able to reduce the intensity of the MSG symptom complex.
Absorption ; Administration, Oral ; Animals ; Capsules ; Surveys and Questionnaires ; Humans ; Intestines ; Lactobacillus brevis* ; Lactobacillus* ; Mice ; Sodium Glutamate* ; Soybeans

A new sustained-release (SR) pregabalin tablet, YHD1119, was formulated for once-daily dosing. In the current study, we aimed to evaluate the pharmacokinetics of YHD1119 tablets in patients with reduced renal function. Subjects were grouped by creatinine clearance:> 60 mL/min/1.73m 2 (Cohort A) and 30–60 mL/min/1.73m 2 (Cohort B). Eight subjects in Cohort A received a YHD1119 75 mg tablet (Y75T) and a YHD1119 150 mg tablet (Y150T) in each period, and eight subjects in Cohort B received a Y75T. Non-compartment analysis and population pharmacokinetic analysis using a one-compartment model with first-order elimination and first-order absorption with lag time were performed. Sixteen subjects completed the study. The geometric mean ratio (GMR) (90% confidence intervals [CI]) for maximum concentration (C max ), and area under the concentration-time profile from 0 to the last measurable time (AUC last ) after Y75T of Cohort B to those of Y75T of Cohort A were 1.2273 (1.0245–1.4701), and 2.4146 (1.8142–3.2138), respectively. The GMR (90% CI) for C max , and AUC last after Y75T of Cohort B to those of Y150T of Cohort A were 0.6476 (0.5229–0.8021), and 1.1471 (0.8418–1.5632), respectively. Simulated steady-steady pregabalin concentrations after once-daily Y75T dosing in subjects with eGFR 45 mL/min/1.73 m 2 were within the range of steady-state concentrations simulated after once-daily Y150T dosing in subjects with eGFR 90 mL/min/1.73 m 2 . The total pregabalin exposure of Y75T in patients with moderate renal impairment was comparable with that of Y150T in subjects with near-normal renal function.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.