Adrenoleukodystrophy 1례

We describe a 9-year-old boy who showed typical neurologic manifestations i.e., progressive behavioral changes, intellectual impairment, visual disturbances and hearing loss, cerebellar and pyramidal signs with characteristic neuroimaging features, which led us to make a clinical deagnosis of ALD. It was confirmed later by demonstration of increased VLCFA levels in RBC membrane using HPLC. He has no family history of neurologic or endocrine disorder. Prophylactic antiepileptic medicaion could not prevent the development of seizure disorder

Potential Role of Sirtuin as a Therapeutic Target for Neurodegenerative Diseases

The sirtuins (SIRTs) are protein-modifying enzymes that are distributed ubiquitously in all organisms. SIRT1 is a mammalian homologue of yeast nicotinamide-adenine-dinucleotide-dependent deacetylase silent information regulator 2 (known as Sir2), which is the best-characterized SIRT family member. It regulates longevity in several model organisms and is involved in several processes in mammalian cells including cell survival, differentiation, and metabolism. SIRT1 induction, either by SIRT-activating compounds such as resveratrol, or metabolic conditioning associated with caloric restriction, could have neuroprotective qualities and thus delay the neurodegenerative process, thereby promoting longevity. However, the precise mechanistic liaison between the activation of SIRT and extended healthy aging or delaying age-related diseases in humans has yet to be established

Clinical Characteristics of a Nationwide Hospital-based Registry of Mild-to-Moderate Alzheimer's Disease Patients in Korea: A CREDOS (Clinical Research Center for Dementia of South Korea) Study

With rapid population aging, the socioeconomic burden caused by dementia care is snowballing. Although a few community-based studies of Alzheimer's disease (AD) have been performed in Korea, there has never been a nationwide hospital-based study thereof. We aimed to identify the demographics and clinical characteristics of mild-to-moderate AD patients from the Clinical Research Center for Dementia of Korea (CREDOS) registry. A total of 1,786 patients were consecutively included from September 2005 to June 2010. Each patient underwent comprehensive neurological examination, interview for caregivers, laboratory investigations, neuropsychological tests, and brain MRI. The mean age was 74.0 yr and the female percentage 67.0%. The mean period of education was 7.1 yr and the frequency of early-onset AD (< 65 yr old) was 18.8%. Among the vascular risk factors, hypertension (48.9%) and diabetes mellitus (22.3%) were the most frequent. The mean score of the Korean version of Mini-Mental State Examination (K-MMSE) was 19.2 and the mean sum of box scores of Clinical Dementia Rating (CDR-SB) 5.1. Based on the well-structured, nationwide, and hospital-based registry, this study provides the unique clinical characteristics of AD and emphasizes the importance of vascular factors in AD in Korea

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Background Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimers disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The time orientation and 3-word recall score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.The publication costs, design of the study, data management and writing the manuscript for this article were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538), Korea Ministry of Health & Welfare, and from the Original Technology Research Program for Brain Science through the National Research Foundation of Korea funded by the Korean Government (MSIP; No. 2014M3C7A1064752)