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.     

Dieckol Inhibits the Effects of Particulate Matter 10 on Sebocytes,Outer Root Sheath Cells,and Cutibacterium Acnes-Pretreated Mice

BackgroundParticulate matter (PM) is an air pollutant that can impair the human skin. Antioxidants have been tested to improve PM-induced skin inflammation.ObjectiveIn this study, we investigated the effects of dieckol on PM-induced inflammation on cultured human sebocytes, outer root sheath (ORS) cells, and mice pretreated with Cutibacterium acnes.MethodsWe cultured and treated the sebocytes and ORS cells with 5 µM of dieckol and 100 µg/ml of PM10 for 24 h. The C. acnes-pretreated mice received 5 µM of dieckol and 100 µg/ml of PM10. We measured cell viability using MTT assay. Real-time PCR and measurement of reactive oxygen species (ROS) and sebum production analyzed the effects.ResultsDieckol inhibited the upregulation of the gene expression of the inflammatory cytokines, matrix metalloproteinase (MMP), aryl hydrocarbon receptor, and nuclear factor kappa-light-chain-enhancer of activated B cells by PM10 in the cultured sebocytes and ORS cells and inhibited an increase in ROS production by PM10 in the cultured sebocytes. In addition, dieckol decreased the inflammatory cytokines, MMP, and sebum production in C. acnes-pretreated mice.ConclusionDieckol effectively reduced the expression of inflammatory biomarkers and the production of sebum in cultured sebocytes, ORS cells, and C. acnes-pretreated mice.     

Comparison of Initial Presentation of Pediatric Diabetes Before and During the Coronavirus Disease 2019 Pandemic Era

BackgroundHospital visitation has become challenging during the coronavirus disease 2019 pandemic because of quarantine measures and fear of infection. Consequently, newly diagnosed patients may present with more severe diseases during the pandemic. The present study analyzed the differences in the initial clinical presentations of newly diagnosed patients with type 1 diabetes (T1D) and type 2 diabetes (T2D), comparing pre-pandemic and pandemic periods.MethodsNewly diagnosed patients with T1D or T2D and aged < 18 years during 2018–2020 were included in the study. Data were collected retrospectively from four academic centers in Gyeonggi-do, South Korea. Initial clinical data were compared between the pre-pandemic (2018–2019) and pandemic (2020) periods.ResultsIn the pre-pandemic and pandemic periods, 99 patients (41 T1D and 58 T2D patients) and 84 patients (51 T1D and 33 T2D patients) were identified, respectively. During the pandemic, the proportion of diabetic ketoacidosis (DKA) cases increased compared to the pre-pandemic period (21.2% during 2018–2019 vs. 38.1% in 2020; P = 0.012). In the pre-pandemic and pandemic periods, initial pH was 7.32 ± 0.14 and 7.27 ± 0.15, respectively (P = 0.040), and HbA1c values were 11.18 ± 2.46% and 12.42 ± 2.87%, respectively (P = 0.002). During the pandemic, there was an increased risk of DKA in patients with T1D (odds ratio, 2.42; 95% confidence interval, 1.04–5.62; P = 0.040).ConclusionDuring the pandemic, the proportion of DKA in newly diagnosed patients with T1D increased and clinical parameters showed a deteriorating pattern. Increased awareness of pediatric diabetes, especially DKA, could facilitate visit to the hospital for an early diagnosis; thus, reducing the number of DKA cases during the pandemic era.     

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.     

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.