Clinical Features of Genetic Creutzfeldt-Jakob Disease with E200K Mutation

Although genetic Creutzfeldt-Jakob disease (CJD) is a rare neurodegenerative disorder, cases of genetic CJD with E200K mutation are being increasingly reported in Korea. However, the clinical features and course of genetic CJD with E200K mutation in Korea remain unclear. We describe the clinical features and course of genetic CJD with E200K mutation in a patient who initially presented with rapid progressive memory impairment and myoclonus.

Clinical Features of Genetic Creutzfeldt-Jakob Disease with E200K Mutation

Although genetic Creutzfeldt-Jakob disease (CJD) is a rare neurodegenerative disorder, cases of genetic CJD with E200K mutation are being increasingly reported in Korea. However, the clinical features and course of genetic CJD with E200K mutation in Korea remain unclear. We describe the clinical features and course of genetic CJD with E200K mutation in a patient who initially presented with rapid progressive memory impairment and myoclonus.

Quantification of Rapid Eye Movement Sleep without Atonia to Diagnose Rapid Eye Movement Sleep Behavior Disorder: A Retrospective and Case-Control Study

Objectives@#Rapid eye movement (REM) sleep without atonia (RSWA) fulfils one of the criteria for diagnosing REM sleep behavior disorder (RBD) according to the International Classification of Sleep Disorders, Third Edition. However, RSWA quantification is an unresolved issue, which is associated with the future direction of revising the diagnostic criteria. The purpose of this study was to evaluate the quantification of RSWA in patients with RBD and identify an optimal cut-off value of quantitative RSWA for RBD diagnosis. @*Methods@#Medical records and polysomnographic results were analyzed retrospectively to diagnose sleep disorders from June 2017 to May 2018 at Pusan National University Hospital. Nineteen subjects with idiopathic RBD were included in the present study. Propensity score matching was used to control age, gender, and anti-depressant factors, which influenced RSWA. RSWA was scored according to the American Academy of Sleep Medicine scoring manual. Cohen’s kappa coefficient was measured to test inter-rater reliability between two polysomnography raters. @*Results@#Cohen’s kappa coefficients were 0.755 (p<0.001) and 0.689 (p<0.001) for tonic and phasic activities, respectively. RSWA was significantly increased in subjects with RBD compared with controls [median and interquartile range: 16.5 (8.8–24.6) vs. 6.3 (4.1–7.2) p=0.001]. The optimal cut-off value was 8.0% for the proportion of RSWA (sensitivity 78.5%, specificity 85.7%, area under the receiver-operating characteristic curve 0.837). @*Conclusions@#Subjects with RBD had significantly increased RSWA compared to controls. The proportion of RSWA during REM sleep can be applied to discriminate subjects with RBD from controls.

Late-Onset Wilson’s Disease Mimics Hashimoto Encephalopathy

A 48-year-old woman presented with a 1-day history of aggressive behavior. Hashimoto encephalopathy was first suspected based on elevated levels of serum anti-thyroid peroxidase antibody. Her clinical symptoms did not improve despite treatment with intravenous corticosteroid. Abdominal computed tomography revealed liver cirrhosis, and brain T2-weighted magnetic resonance imaging revealed midbrain hyperintensity, and she was finally diagnosed with Wilson’s disease. The Wilson’s disease should be considered in the differential diagnosis in adults presenting with unexplained hepatic, neurological, or psychiatric abnormalities.

Diagnostic Accuracy of Different Machine Learning Algorithms for Obstructive Sleep Apnea

Objectives@#The objective of this study was to develop models for predicting obstructive sleep apnea (OSA) based on easily obtainable clinical information of patients using various machine learning techniques. @*Methods@#We used a data set that included the records of 1,368 patients, in which 1,074 patients were male (78.5 %), and 294 patients were female (21.5 %). We randomly divided the data into a training set (1,000) and test set (368). Five machine learning methods, i.e., support vector machine model, lasso logit model, naïve bayes, discriminant analysis, and K-nearest neighbor (KNN), with a 10-cross fold technique were used with the proposed model to predict OSA. We evaluated the accuracy, sensitivity, specificity, and precision of each model for three thresholds [Apnea-Hypopnea Index (AHI)≥5, AHI≥15, and AHI≥30]. @*Results@#Among the machine learning techniques, KNN showed the best results compared to the other techniques. The accuracy, sensitivity, specificity, and precision of OSA prediction were 87.0%, 91.0%, 74.4%, and 91.9%, respectively, based on AHI≥5. When the threshold of OSA was AHI≥15 or AHI≥30, KNN provided lower accuracy (79.6% each) and precision (79.0% and 68.7%), which were still higher than those of the other techniques. @*Conclusions@#The model derived from the KNN technique exhibited the best performance based on its highest level of accuracy. We demonstrate that this model is a useful tool for predicting OSA.

Diagnostic Accuracy of Different Machine Learning Algorithms for Obstructive Sleep Apnea

Objectives@#The objective of this study was to develop models for predicting obstructive sleep apnea (OSA) based on easily obtainable clinical information of patients using various machine learning techniques. @*Methods@#We used a data set that included the records of 1,368 patients, in which 1,074 patients were male (78.5 %), and 294 patients were female (21.5 %). We randomly divided the data into a training set (1,000) and test set (368). Five machine learning methods, i.e., support vector machine model, lasso logit model, naïve bayes, discriminant analysis, and K-nearest neighbor (KNN), with a 10-cross fold technique were used with the proposed model to predict OSA. We evaluated the accuracy, sensitivity, specificity, and precision of each model for three thresholds [Apnea-Hypopnea Index (AHI)≥5, AHI≥15, and AHI≥30]. @*Results@#Among the machine learning techniques, KNN showed the best results compared to the other techniques. The accuracy, sensitivity, specificity, and precision of OSA prediction were 87.0%, 91.0%, 74.4%, and 91.9%, respectively, based on AHI≥5. When the threshold of OSA was AHI≥15 or AHI≥30, KNN provided lower accuracy (79.6% each) and precision (79.0% and 68.7%), which were still higher than those of the other techniques. @*Conclusions@#The model derived from the KNN technique exhibited the best performance based on its highest level of accuracy. We demonstrate that this model is a useful tool for predicting OSA.

Cerebral Air Embolism Observed on Susceptibility-Weighted Magnetic Resonance Imaging

No abstract available.

Hypertensive Encephalopathy with Multiple Lacunar Infarcts and Microbleeds

No abstract available.

Anaplastic Astrocytoma Mimicking Herpes Simplex Encephalitis

No abstract available.

Warfarin-Induced Intracerebral Hemorrhage Associated with Microbleeds

BACKGROUND: Microbleeds (MBs), proposed as a biomarker for microangiopathy, have been suggested as a predictor of spontaneous or thrombolysis-related intracerebral hemorrhage (ICH) in acute ischemic stroke. However, the relationship between MBs and warfarin-induced ICH is not clear. CASE REPORT: We describe two patients who developed warfarin-induced ICH at the site of MBs documented in previous MRI. CONCLUSIONS: The presence of MBs might increase the risk of ICH after warfarin use in ischemic stroke patients. A large cohort study is required to confirm the relationship of MBs with warfarin-induced ICH.