Al2O3-Coated Si-Alloy Prepared by Atomic Layer Deposition as Anodes for Lithium-Ion Batteries

Silicon-based anodes can increase the energy density of Li-ion batteries (LIBs) owing to their large weights and volumetric capacities. However, repeated charging and discharging can rapidly deteriorate the electrochemical properties because of a large volume change in the electrode. In this study, a commercial Fe-Si powder was coated with Al₂O₃ layers of different thicknesses via atomic layer deposition (ALD) to prevent the volume expansion of Si and suppress the formation of crack-induced solid electrolyte interfaces. The Al₂O₃ content was controlled by adjusting the trimethyl aluminum exposure time, and higher Al₂O₃ contents significantly improved the electrochemical properties. In 300 cycles, the capacity retention rate of a pouch full-cell containing the fabricated anodes increased from 69.8% to 72.3% and 79.1% depending on the Al₂O₃ content. The powder characterization and coin and pouch cell cycle evaluation results confirmed the formation of an Al₂O₃ layer on the powder surface. Furthermore, the expansion rate observed during the charging/discharging of the pouch cell indicated that the deposited layer suppressed the powder expansion and improved the cell stability. Thus, the performance of an LIB containing Si-alloy anodes can be improved by coating an ALD-synthesized protective Al₂O₃ layer.     

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.     

Initial Abdominal CT and Laboratory Findings Prior to Diagnosis of Crohn’s Disease in Children

PurposeTo identify initial abdominal computed tomography (CT) and laboratory findings prior to a diagnosis of Crohn’s disease (CD) in children.Materials and MethodsIn this retrospective study, patients (≤18 year-old) who were diagnosed with CD from 2004 to 2019 and had abdominal CT just prior to being diagnosed with CD were included in the CD group. Patients (≤18 years old) who were diagnosed with infectious enterocolitis from 2018 to 2019 and had undergone CT prior to being diagnosed with enterocolitis were included as a control group. We assessed the diagnostic performances of initial CT and laboratory findings for the diagnosis of CD using logistic regression and the area under the curve (AUC).ResultsIn total, 107 patients (50 CD patients, 57 control patients) were included, without an age difference between groups (median 13 years old vs. 11 years old, p=0.119). On univariate logistic regression analysis, multisegmental bowel involvement, mesenteric vessel engorgement, higher portal vein/aorta diameter ratio, longer liver longitudinal diameter, lower hemoglobin (≤12.5 g/dL), lower albumin (≤4 g/dL), and higher platelet (>320×10³/µL) levels were significant factors for CD. On multivariate analysis, multisegmental bowel involvement [odds ratio (OR) 111.6, 95% confidence interval (CI) 4.778–2605.925] and lower albumin levels (OR 0.9, 95% CI 0.891–0.993) were significant factors. When these two features were combined, the AUC value was 0.985 with a sensitivity of 96% and specificity of 100% for differentiating CD.ConclusionMultisegmental bowel involvement on CT and decreased albumin levels can help differentiate CD from infectious enterocolitis in children prior to a definite diagnosis of CD.     

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.     

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.     

Growing Teratoma Syndrome in the Setting of Sarcoidosis: A Case Report and Literature Review

Growing teratoma syndrome (GTS) is rare and can mimic disease recurrence in patients with a history of immature teratoma. Benign hypermetabolic lymphadenopathy found on staging and surveillance computed tomography (CT) and positron emission tomography (PET) may lead to the presumption of metastatic malignancy. We report a case of a 38 year old with mixed mature and immature teratomas who developed new peritoneal masses after adjuvant chemotherapy despite a normalization of tumor markers. In addition to low FDG uptake observed in these peritoneal masses, a PET scan showed hypermetabolic lymphadenopathy and pulmonary and spleen lesions suggesting widespread metastases. Subsequent surgical resection confirmed a mixed pathology with GTS and sarcoidosis. We reviewed the current literature evidence of GTS and sarcoidosis as a benign cause of lymphadenopathy in cancer patients. We emphasize the importance of a tissue diagnosis before instituting therapy for presumed cancer recurrence to avoid potentially fatal diagnostic traps and management errors. A multiple disciplinary team approach is imperative in managing patients with suspected recurrent immature teratomas.     

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.     

Catechin-7-O-α-L-rhamnopyranoside can reduce α-MSH-induced melanogenesis in B16F10 melanoma cells through competitive inhibition of tyrosinase

Although melanogenesis is a defense mechanism against ultraviolet (UV)-induced skin damage, abnormally excessive melanin production causes pigmentation disorders. Tyrosinase, as a key factor for melanin synthesis, plays an important role in inducing skin pigmentation. Therefore, the inhibition of tyrosinase is crucial in preventing skin pigmentation in the cosmetics and medicine fields. However, the majority of well-known tyrosinase inhibitors have been discontinued due to toxic effects on the skin or lack of selectivity and/or stability. In this study, we evaluated possible anti-melanogenic effects of catechin-7-O-α-L-rhamnopyranoside (C7R) isolated from the stem bark of Ulmus parvifolia, to discover a new tyrosinase inhibitor that has both safety and stability. When C7R was pretreated in B16F10 melanoma cells stimulated by α-melanocyte-stimulating hormone, this compound reduced melanin accumulation and murine tyrosinase activity. In line with these results, C7R inhibits tyrosinase purified from a mushroom in vitro like kojic acid and arbutin. Furthermore, C7R exhibited a competitive inhibition on a Lineweaver-Burk plot. Next, the underlying mechanisms of the C7R-mediated tyrosinase inhibitory effect were sought through docking simulation and pharmacophore analysis between tyrosinase residues and C7R. The results of these analyses showed that C7R had binding energy of -14.5kcal/mol, and indicated that C7R interacts with tyrosinase through an aromatic ring and various hydrophobic and hydrogen bonds. Together, our results suggest that C7R can be applied as a novel natural anti-melanogenic agent that inhibits tyrosinase.