Thiol-linked peroxidase activity of human ceruloplasmin

AbstractHuman ceruloplasmin exhibited different antioxidant effects according to the electron donors in a metal-catalyzed oxidation system. Purified ceruloplasmin did not play a significant role in the protection of DNA strand breaks in the ascorbate/Fe3+/O2 system. However, when ascorbates were replaced with a thiol-reducing equivalent such as dithiothreitol, DNA strand breaks were significantly prevented by the same amount of ceruloplasmin. Ceruloplasmin did not catalyze the decomposition of H2O2 in the absence of reduced glutathione. On the contrary, ceruloplasmin showed a potent peroxidase ability to destroy H2O2 in the presence of reduced glutathione. In conclusion, the removal of H2O2 by human ceruloplasmin is not simply stoichiometric but thiol-dependent

Growth Mechanism of Lithium Clusters on the Surface of Porous Carbon Framework for Lithium Metal Batteries

Carbon-based frameworks as a metallic lithium (Li) host have been widely developed to overcome the drawbacks associated with bare Li metal anode. Achieving a complete understanding of the growth mechanism of the Li clusters in the carbon host remains controversial, however, and requires determining the factors involved and their clear causes. Herein, we have carried out density functional theory calculations to predict the growth mechanism of Li clusters by employing different heteroatoms (pyridinic N, pyrrolic N, quaternary N, and Co-N4). As a key feature, the Co-N4 affects the Li deposition behavior with axial Li growth on the surfaces of the carbon frameworks, while the other heteroatoms (i.e., nitrogen defects) induce vertical Li growth. By combining theoretical calculations and experiments, this detailed investigation widens the scope of future research on carbon host materials for practical usage of Li metal batteries

Prevalence and Clinical Impact of Electrocardiographic Abnormalities in Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is a strong risk factor for cardiovascular disease. An electrocardiogram (ECG) is a basic test for screening cardiovascular disease. However, the impact of ECG abnormalities on cardiovascular prognosis in patients with CKD is largely unknown. A total of 2442 patients with CKD (stages 3–5) who underwent ECG between 2013 and 2015 were selected from the electronic health record database of the Korea University Anam Hospital. ECG abnormalities were defined using the Minnesota classification. The five-year major adverse cerebrocardiovascular event (MACCE), the composite of death, myocardial infarction (MI), and stroke were analyzed. The five-year incidences for MACCE were 27.7%, 20.8%, and 17.2% in patients with no, minor, and major ECG abnormality (p < 0.01). Kaplan–Meier curves also showed the highest incidence of MI, death, and MACCE in patients with major ECG abnormality. Multivariable Cox regression analysis revealed age, sex, diabetes, CKD stage, hsCRP, antipsychotic use, and major ECG abnormality as independent risk predictors for MACCE (adjusted HR of major ECG abnormality: 1.39, 95% CI: 1.09–1.76, p < 01). Among the detailed ECG diagnoses, sinus tachycardia, myocardial ischemia, atrial premature complex, and right axis deviation were proposed as important ECG diagnoses. The accuracy of cardiovascular risk stratification was improved when the ECG results were added to the conventional SCORE model (net reclassification index 0.07). ECG helps to predict future cerebrocardiovascular events in CKD patients. ECG diagnosis can be useful for cardiovascular risk evaluation in CKD patients when applied in addition to the conventional risk stratification model

Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms

Background and ObjectiveIdentifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. MethodsWe included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. ResultsOf the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. ConclusionsTree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.N