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Background Presenilin-1 (PSEN1) is one of the causative genes for early onset Alzheimer's disease (EOAD). Recently, emerging studies reported several novel PSEN1 mutations among Asian. We describe a male with EOAD had a pathogenic PSEN1 mutation. Case presentation A 53-year-old male presented with memory decline, followed by difficulty in finding ways. Patient had positive family history, since his mother and one of his brother was also affected with dementia. Brain magnetic resonance imaging (MRI) scan showed mild degree of atrophy of bilateral hippocampus and parietal lobe. 18F-Florbetaben-PET (FBB-PET) revealed increased amyloid deposition in bilateral frontal, parietal, temporal lobe and precuneus. Whole exome analysis revealed a heterozygous, probably pathogenic PSEN1 (c.695G > T, p.W165C) mutation. Interestingly, Trp165Cys mutation is located in trans membrane (TM)-III region, which is conserved between PSEN1/PSEN2. In vitro studies revealed that PSEN1 Trp165Cys could result in disturbances in amyloid metabolism. This prediction was confirmed by structure predictions and previous in vitro studies that the p.Trp165Cys could result in decreased Aβ42/Aβ40 ratios. Conclusion We report a case of EOAD having a pathogenic PSEN1 (Trp165Cys) confirmed with in silico and in vitro predictions. ; This work was supported by a National Research Foundation of Korea (NRF) Grants, awarded by the Korean government (MEST, No. 2017R1A2B4012636 & 2017R1C1B5017807). Dr. An SS receives the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST, No. 2017R1A2B4012636). Dr. Eva Bagyinszky receives the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST, No. 2017R1C1B5017807). Dr. Giau VV reports no disclosure. Dr. Pyun JM reports no disclosure. Dr. Suh J reports no disclosure. Dr. Kim SY reports no disclosure.

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 Alzheimer's 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).

We have provided an overview on the profound impact of COVID-19 upon older people with Alzheimer's disease and other dementias and the challenges encountered in our management of dementia in different health-care settings, including hospital, out-patient, care homes, and the community during the COVID-19 pandemic. We have also proposed a conceptual framework and practical suggestions for health-care providers in tackling these challenges, which can also apply to the care of older people in general, with or without other neurological diseases, such as stroke or parkinsonism. We believe this review will provide strategic directions and set standards for health-care leaders in dementia, including governmental bodies around the world in coordinating emergency response plans for protecting and caring for older people with dementia amid the COIVD-19 outbreak, which is likely to continue at varying severity in different regions around the world in the medium term.