Three-vessel view of the fetal heart: in utero development of the great vessels

OBJECTIVES: The purpose of our study is to provide reference values for the great vessels obtained from images of the three-vessel view of the fetal heart, with an emphasis on the size discrepancy of the great vessels. METHODS: From February 2003 to May 2003, the main pulmonary artery (MPA), ascending aorta (AA), and SVC were measured in well-dated, nonanomalous fetuses scanned at 14-38 weeks of gestation. RESULTS: The size of each great vessel had a significant positive relationship with advance in gestation (P < 0.001); MPA (mm) = -2.76 + 0.34 x GA, ascending aorta (AA) (mm) = -1.73 + 0.26 x GA - 1.18E - 05 x GA(3), and SVC (mm) = 0.33 + 0.01 x GA(2) - 4.12E - 05 x GA(3). The AA/MPA ratio was significantly decreased with advance in gestation, while the SVC/AA ratio was significantly increased; AA/MPA ratio = -1.24 - 0.03 x GA + 3.88E - 04 x GA(2); P < 0.001, SVC/AA ratio = 0.63 - 5.43E - 03 x GA + 1.96E - 04 x GA(2); P < 0.001. CONCLUSION: On the three-vessel view of the fetal heart, the interpretation of the size discrepancy of the great vessels needs to be adjusted according to fetal growth.     

Immunogenicity of COVID-19 Vaccination in Patients With End-Stage Renal Disease Undergoing Maintenance Hemodialysis: The Efficacy of a Mix-and-Match Strategy

BackgroundThe objective of this study was to evaluate the immunogenicity of coronavirus disease 2019 (COVID-19) vaccination in patients with end-stage renal disease (ESRD) on hemodialysis.MethodsESRD patients at the hemodialysis center of a tertiary-care university-affiliated hospital and healthy employees at the clinical laboratory center were prospectively recruited between March and June 2021. For severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody analysis, blood samples were collected serially on days 0, 14, 28, and 56 after the first vaccine dose, and on days 7 and 50 after the second dose. Antibodies against the SARS-CoV-2 spike protein were quantified, and SARS-CoV-2 neutralizing antibodies were measured in the serum and plasma.ResultsThirty-one ESRD patients and 55 healthy employees were regularly monitored. Twenty-five (80.6%) ESRD patients on hemodialysis received a mix-and-match strategy with ChAdOx1-BNT162b2 (AZ–Pf group) and six (19.4%) received two doses of ChAdOx1 (AZ–AZ group). ESRD patients on hemodialysis showed lower binding antibody titers and neutralizing antibody activities compared to healthy participants following the first vaccination with ChAdOx1. After the second dose, AZ-Pf group had higher immunogenicity than healthy people on days 7 and 50. The binding antibody titer and neutralizing antibody activities on days 7 and 50 were significantly higher in the AZ–Pf group than in the AZ–AZ group.ConclusionESRD patients on hemodialysis receiving the mix-and-match strategy (ChAdOx1–BNT162b2) have COVID-19 vaccine immunogenicity comparable to healthy individuals receiving two doses of ChAdOx1.Trial RegistrationClinicalTrials.gov Identifier: {"type":"clinical-trial","attrs":{"text":"NCT04871945","term_id":"NCT04871945"}}NCT04871945     

pH and Redox-Dual Sensitive Chitosan Nanoparticles Having Methyl Ester and Disulfide Linkages for Drug Targeting against Cholangiocarcinoma Cells

The aim of this study is to prepare pH- and redox-sensitive nanoparticles for doxorubicin (DOX) delivery against DOX-resistant HuCC-T1 human cholangiocarcinoma (CCA) cells. For this purpose, L-histidine methyl ester (HIS) was attached to chitosan oligosaccharide (COS) via dithiodipropionic acid (abbreviated as ChitoHISss). DOX-incorporated nanoparticles of ChitoHISss conjugates were fabricated by a dialysis procedure. DOX-resistant HuCC-T1 cells were prepared by repetitive exposure of HuCC-T1 cells to DOX. ChitoHISss nanoparticles showed spherical morphology with a small diameter of less than 200 nm. The acid pH and glutathione (GSH) addition induced changes in the size distribution pattern of ChitoHISss nanoparticles from a narrow/monomodal distribution pattern to a wide/multimodal pattern and increased the fluorescence intensity of the nanoparticle solution. These results indicate that a physicochemical transition of nanoparticles can occur in an acidic pH or redox state. The more acidic the pH or the higher the GSH concentration the higher the drug release rate was, indicating that an acidic environment or higher redox states accelerated drug release from ChitoHISss nanoparticles. Whereas free DOX showed decreased anticancer activity at DOX-resistant HuCC-T1 cells, DOX-incorporated ChitoHISss nanoparticles showed dose-dependent anticancer activity. Intracellular delivery of DOX-incorporated ChitoHISss nanoparticles was relatively increased at an acidic pH and in the presence of GSH, indicating that DOX-incorporated ChitoHISss nanoparticles have superior acidic pH- and redox-sensitive behavior. In an in vivo tumor xenograft model, DOX-incorporated ChitoHISss nanoparticles were specifically delivered to tumor tissues and then efficiently inhibited tumor growth. We suggest that ChitoHISss nanoparticles are a promising candidate for treatment of CCA.     

Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants

PurposeThe aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants.Materials and MethodsOf 10425 VLBW infants registered in the Korean Neonatal Network between 2013 and 2017, 7954 infants were included. PGF was defined as a decrease in Z score >1.28 at discharge, compared to that at birth. Six metrics [area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score] were obtained at five time points (at birth, 7 days, 14 days, 28 days after birth, and at discharge). Machine learning models were built using four different techniques [extreme gradient boosting (XGB), random forest, support vector machine, and convolutional neural network] to compare against the conventional multiple logistic regression (MLR) model.ResultsThe XGB algorithm showed the best performance with all six metrics across the board. When compared with MLR, XGB showed a significantly higher AUROC (p=0.03) for Day 7, which was the primary performance metric. Using optimal cut-off points, for Day 7, XGB still showed better performances in terms of AUROC (0.74), accuracy (0.68), and F1 score (0.67). AUROC values seemed to increase slightly from birth to 7 days after birth with significance, almost reaching a plateau after 7 days after birth.ConclusionWe have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such models may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.     

Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

PurposeFetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability. This study developed a machine learning model that can classify fetal cardiotocography results as normal or abnormal.Materials and MethodsIn total, 17492 fetal cardiotocography results were obtained from Ajou University Hospital and 100 fetal cardiotocography results from Czech Technical University and University Hospital in Brno. Board-certified physicians then reviewed the fetal cardiotocography results and labeled 1456 of them as gold-standard; these results were used to train and validate the model. The remaining results were used to validate the clinical effectiveness of the model with the actual outcome.ResultsIn a test dataset, our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 and area under the precision-recall curve (AUPRC) of 0.73 in an internal validation dataset. An average AUROC of 0.73 and average AUPRC of 0.40 were achieved in the external validation dataset. Fetus abnormality score, as calculated from the continuous fetal cardiotocography results, was significantly associated with actual clinical outcomes [intrauterine growth restriction: odds ratio, 3.626 (p=0.031); Apgar score 1 min: odds ratio, 9.523 (p<0.001), Apgar score 5 min: odds ratio, 11.49 (p=0.001), and fetal distress: odds ratio, 23.09 (p<0.001)].ConclusionThe machine learning model developed in this study showed precision in classifying FHR signals. This suggests that the model can be applied to medical devices as a screening tool for monitoring fetal status.     

Monthly and seasonal variations in vestibular neuritis

Seasonal variations in vestibular neuritis (VN) could support the etiology of viral infection. However, several recent studies revealed no significant seasonal variations in VN. Further studies are necessary to determine the etiology of VN. We analyzed patients with VN to evaluate monthly and seasonal variations. Patients with VN who visited our otorhinolaryngology department or were referred to our department from the emergency department between March 2014 and February 2019 were included retrospectively in this study. Differences among the months and seasons of VN visits were analyzed. Patients were divided into 2 groups according to sex and age (65 years or older and younger than 65 years). Differences among the months and seasons of VN visits were analyzed between groups. There were no significant differences in monthly and seasonal distributions in 248 patients with VN. There were also no significant differences in monthly and seasonal distributions in male and female patients or in older and younger patients. There were no significant differences in monthly or seasonal distributions of patients with VN. Factors other than viruses, such as vascular ischemia, should also be considered in the incidence of VN, especially in older patients.     

Clinical Characteristics and Risk Factors for Mortality in Critical Coronavirus Disease 2019 Patients 50 Years of Age or Younger During the Delta Wave: Comparison With Patients > 50 Years in Korea

BackgroundNumerous patients around the globe are dying from coronavirus disease 2019 (COVID-19). While age is a known risk factor, risk analysis in the young generation is lacking. The present study aimed to evaluate the clinical features and mortality risk factors in younger patients (≤ 50 years) with a critical case of COVID-19 in comparison with those among older patients (> 50 years) in Korea.MethodsWe analyzed the data of adult patients only in critical condition (requiring high flow nasal cannula oxygen therapy or higher respiratory support) hospitalized with PCR-confirmed COVID-19 at 11 hospitals in Korea from July 1, 2021 to November 30, 2021 when the delta variant was a dominant strain. Patients’ electronic medical records were reviewed to identify clinical characteristics.ResultsDuring the study period, 448 patients were enrolled. One hundred and forty-two were aged 50 years or younger (the younger group), while 306 were above 50 years of age (the older group). The most common pre-existing conditions in the younger group were diabetes mellitus and hypertension, and 69.7% of the patients had a body mass index (BMI) > 25 kg/m². Of 142 younger patients, 31 of 142 patients (21.8%, 19 women) did not have these pre-existing conditions. The overall case fatality rate among severity cases was 21.0%, and it differed according to age: 5.6% (n = 8/142) in the younger group, 28.1% in the older group, and 38% in the ≥ 65 years group. Age (odds ratio [OR], 7.902; 95% confidence interval [CI], 2.754–18.181), mechanical ventilation therapy (OR, 17.233; 95% CI, 8.439–35.192), highest creatinine > 1.5 mg/dL (OR, 17.631; 95% CI, 8.321–37.357), and combined blood stream infection (OR, 7.092; 95% CI, 1.061–18.181) were identified as independent predictors of mortality in total patients. Similar patterns were observed in age-specific analyses, but most results were statistically insignificant in multivariate analysis due to the low number of deaths in the younger group. The full vaccination rate was very low among study population (13.6%), and only three patients were fully vaccinated, with none of the patients who died having been fully vaccinated in the younger group. Seven of eight patients who died had a pre-existing condition or were obese (BMI > 25 kg/m²), and the one remaining patient died from a secondary infection.ConclusionAbout 22% of the patients in the young critical group did not have an underlying disease or obesity, but the rate of obesity (BMI > 25 kg/m²) was high, with a fatality rate of 5.6%. The full vaccination rate was extremely low compared to the general population of the same age group, showing that non-vaccination has a grave impact on the progression of COVID-19 to a critical condition. The findings of this study highlight the need for measures to prevent critical progression of COVID-19, such as vaccinations and targeting young adults especially having risk factors.     

Fimasartan Ameliorates Deteriorations in Glucose Metabolism in a High Glucose State by Regulating Skeletal Muscle and Liver Cells

PurposeSince diabetes and hypertension frequently occur together, it is thought that these conditions may have a common pathogenesis. This study was designed to evaluate the anti-diabetic function of the anti-hypertensive drug fimasartan on C2C12 mouse skeletal muscle and HepG2 human liver cells in a high glucose state.Materials and MethodsThe anti-diabetic effects and mechanism of fimasartan were identified using Western blot, glucose uptake tests, oxygen consumption rate (OCR) analysis, adenosine 5′-triphosphate (ATP) enzyme-linked immunosorbent assay (ELISA), and immunofluorescence staining for diabetic biomarkers in C2C12 cells. Protein biomarkers for glycogenolysis and glycogenesis were evaluated by Western blotting and ELISA in HepG2 cells.ResultsThe protein levels of phosphorylated 5′ adenosine monophosphate-activated protein kinase (p-AMPK), p-AKT, insulin receptor substrate-1 (IRS-1), and glucose transporter type 4 (Glut4) were elevated in C2C12 cells treated with fimasartan. These increases were reversed by peroxisome proliferator-activated receptor delta (PPARδ) antagonist. ATP, OCR, and glucose uptake were increased in cells treated with 200 µM fimasartan. Protein levels of glycogen phosphorylase, glucose synthase, phosphorylated glycogen synthase, and glycogen synthase kinase-3 (GSK-3) were decreased in HepG2 cells treated with fimasartan. However, these effects were reversed following the addition of the PPARδ antagonist GSK0660.ConclusionIn conclusion, fimasartan ameliorates deteriorations in glucose metabolism as a result of a high glucose state by regulating PPARδ in skeletal muscle and liver cells.     

Significant accumulation of KRAS mutations in bronchiolar metaplasia-associated honeycomb lesions of interstitial pneumonia

Interstitial pneumonia (IP) is a major risk factor for lung adenocarcinoma (LADC). IP-related LADC predominantly develops in the bronchiolar metaplasia lining in honeycomb lesions. Kirsten rat sarcoma virus (KRAS) is the most common oncogene mutated in IP-related LADC. The present study examined the metaplastic epithelia in honeycomb lesions for KRAS mutations using digital droplet polymerase chain reaction (ddPCR), a sensitive method used to detect infrequent mutations. Significantly higher KRAS mutation variant allele frequencies (VAFs) were detected in the metaplastic lung epithelia from 13 patients with IP compared with those in 46 non-lesioned lung samples from patients without IP (G12V, P=0.0004, G12C, P=0.0181, and G12A, P=0.0234; Mann Whitney U test). Multivariate analyses revealed that higher KRAS G12V (logistic regression model; P=0.0133, odds ratio=7.11) and G12C (P=0.0191, odds ratio=5.81) VAFs in patients with IP were independent of confounding variables, such as smoking and age. In patients with IP, metaplastic epithelia exhibited significantly higher KRAS G12V and G12C VAFs compared with the non-lesioned counterparts (paired t-test; G12V, P=0.0158, G12C, P=0.0465). These results suggested that IP could increase KRAS mutations and supported the hypothesis that bronchiolar metaplasia could be a precursor for IP-related LADC.     

Development of the Korean Standardized Antimicrobial Administration Ratio as a Tool for Benchmarking Antimicrobial Use in Each Hospital

BackgroundThe Korea National Antimicrobial Use Analysis System (KONAS), a benchmarking system for antimicrobial use in hospitals, provides Korean Standardized Antimicrobial Administration Ratio (K-SAAR) for benchmarking. This article describes K-SAAR predictive models to enhance the understanding of K-SAAR, an important benchmarking strategy for antimicrobial usage in KONAS.MethodsWe obtained medical insurance claims data for all hospitalized patients aged ≥ 28 days in all secondary and tertiary care hospitals in South Korea (n = 347) from January 2019 to December 2019 from the Health Insurance Review & Assessment Service. Modeling was performed to derive a prediction value for antimicrobial use in each institution, which corresponded to the denominator value for calculating K-SAAR. The prediction values of antimicrobial use were modeled separately for each category, for all inpatients and adult patients (aged ≥ 15 years), using stepwise negative binomial regression.ResultsThe final models for each antimicrobial category were adjusted for different significant risk factors. In the K-SAAR models of all aged patients as well as adult patients, most antimicrobial categories included the number of hospital beds and the number of operations as significant factors, while some antimicrobial categories included mean age for inpatients, hospital type, and the number of patients transferred from other hospitals as significant factors.ConclusionWe developed a model to predict antimicrobial use rates in Korean hospitals, and the model was used as the denominator of the K-SAAR.