Association between private health insurance and medical use by linking subjective health and chronic diseases

This empirical study identifies the negative aspects of private health insurance (PHI) by analyzing the association between subjective health conditions, 2 weeks of outpatient care, chronic diseases, and hospitalizations for 1 year. We used frequency analysis, χ² testing, an analysis of variance, and logistic and multiple logistic regression models to analyze the association between PHI and subjective health conditions, outpatient care, chronic disease status, and hospitalization. The PHI group had good subjective health but had more outpatient care for 2 weeks. There were few chronic diseases in the private insurance group, and there was no significant difference in hospitalizations for 1 year. Hospitalization may occur when essential medical care is required, regardless of health insurance type. This study confirmed that as the PHI lowers the burden of personal medical expenses, the PHI can lead to an increase in the medical resource expenditures on the outpatient medical service and higher public health costs. The government should work to redefine the role of private and national health insurance. Also, the effectiveness of PHI should be reevaluated so that it does not lead to indiscriminate use of medical services by minimizing the burden of private insurance.     

Interleukin-1alpha polymorphism -889C/T related to obesity in Korean Taeumin women

Interleukin-1 (IL-1), a cytokine produced predominantly by cells from the macrophage lineage, can affect multiple neuroendocrine and metabolic functions. IL-1alpha production by cultured peripheral blood mononuclear cells from an obese group was significantly elevated in comparison to a control group. The aim of this study was to investigate whether the IL-1alpha polymorphism and Sasang constitution, a major branch in Korean traditional medicine, were related to obesity. Genotyping was done in 182 healthy females with a marked variation in body mass index (BMI) by a PCR-restriction fragment length polymorphism assay. The T allele was associated with decreased BMI (p = 0.020). In a subgroup with BMI values ranging from 27 approximately 29 kg/m(2), the frequency of the T allele was significantly decreased (p = 0.004, odds ratio, OR = 0.141 compared to a subgroup with a BMI values less than 25 Kg/m(2)). In addition, in Taeumin female subjects, the frequency of the IL-1alpha T allele was markedly decreased in a subgroup with BMI values in the range of 27 approximately 29 kg/m(2) compared to a lean group with BMI values less than 25 kg/m(2) (p = 0.004, OR = 0.139). In Korean women, an association was found between -889C/T polymorphism in the regulatory region of the IL-1alpha gene and BMI values. In addition, an association was found among IL-1alpha polymorphism, obesity, and the Sasang constitution.     

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.