ObjectiveThe Ubiquitous Spaced Retrieval-based Memory Advancement and Rehabilitation Training (USMART) program was developed by transforming the spaced retrieval-based memory training which consisted of 24 face-to-face sessions into a self-administered program with an iPAD app. The objective of this study was to evaluate the feasibility and efficacy of USMART in elderly subjects with mild cognitive impairment (MCI).MethodsFeasibility was evaluated by checking the satisfaction of the participants with a 5-point Likert scale. The efficacy of the program on cognitive functions was evaluated by the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Assessment Battery before and after USMART.ResultsAmong the 10 participants, 7 completed both pre- and post-USMART assessments. The overall satisfaction score was 8.0±1.0 out of 10. The mean Word List Memory Test (WLMT) scores significantly increased after USMART training after adjusting for age, educational levels, baseline Mini-Mental Status Examination scores, and the number of training sessions (pre-USMART, 16.0±4.1; post-USMART, 17.9±4.5; p=0.014, RM-ANOVA). The magnitude of the improvements in the WLMT scores significantly correlated with the number of training sessions during 4 weeks (r=0.793; p=0.033).ConclusionUSMART was effective in improving memory and was well tolerated by most participants with MCI, suggesting that it may be a convenient and cost-effective alternative for the cognitive rehabilitation of elderly subjects with cognitive impairments. Further studies with large numbers of participants are necessary to examine the relationship between the number of training sessions and the improvements in memory function.

PurposeTo analyze the psychosocial factors associated with central serous chorioretinopathy (CSC) according to its phases and subtypes and to correlate the factors with the extent of choroidal hyperpermeability.MethodsAge- and sex-matched CSC patients and controls (n = 37 in each group) were enrolled, and their psychosocial factors were compared. CSC was divided into two phases (active and inactive), and active CSC was further divided into two subtypes (acute and chronic). The correlations between the size of the hyperpermeable choroidal lesion identified on indocyanine green angiography and psychosocial factors were examined.ResultsActive CSC patients experienced more stressful events (p = 0.030), were more depressive (p = 0.037), and felt less emotional (p = 0.014) and informational (p = 0.014) support than the matched controls, whereas inactive CSC patients were comparable to the matched controls in all psychosocial factors. Among the active CSC patients, acute patients were more depressive (p = 0.029), while chronic patients experienced more stressful events (p = 0.024) than their matched controls. The size of the hyperpermeable choroidal lesion was correlated with the severity of depression in acute patients.ConclusionsAssociation of CSC with psychosocial factors was dependent on the phase and subtype of CSC. Psychosocial factors were associated with CSC in the active phase, and severity of depression was correlated with the size of the choroidal pathology in acute active CSC. Further prospective studies to investigate if psychosocial factors can trigger CSC are warranted.

Sweet taste is an important factor that regulates calorie intake and contributes to food preferences in humans and animals. Therefore, the evaluation of sweet substances is essential for various fields such as healthcare, food, and pharmaceutical industries. Sweet tastants are detected by sweet taste receptors which are class C G-protein-coupled receptors (GPCRs). T1R2 venus flytrap (VFT) of sweet taste receptor is known as a primary ligand-binding domain for sweet tastants. In this study, we developed an ultrasensitive artificial sweet taste bioelectronic tongue based on the T1R2 VFT of a human sweet taste receptor. Here, the T1R2 VFT of a human sweet taste receptor was successfully overexpressed in a bacterial expression system. A T1R2 VFTimmobilized carbon nanotube field-effect transistor with floating electrodes was exploited as an artificial sweet taste sensory system. Significantly, our T1R2 VFT-functionalized bioelectronic tongue could be used to detect solutions of sweet tastants down to 0.1 fM and selectively discriminate sweet substances from other taste substances. Furthermore, our device could be used to monitor the response of T1R2 VFT domain of a sweet taste receptor to sweet substances in real food environments such as apple juice and chamomile herb tea. Moreover, our device was used to evaluate the inhibition and enhancement effects on sweet taste receptors by zinc ions and chamomile tea, respectively. In addition, our device demonstrated a long-term storability and reusability. In this respect, our sweet taste bioelectronic tongue could be a promising tool for various basic research and industrial applications.

BackgroundThe conventional scores of the neuropsychological batteries are not fully optimized for diagnosing dementia despite their variety and abundance of information. To achieve low-cost high-accuracy diagnose performance for dementia using a neuropsychological battery, a novel framework is proposed using the response profiles of 2666 cognitively normal elderly individuals and 435 dementia patients who have participated in the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD).MethodsThe key idea of the proposed framework is to propose a cost-effective and precise two-stage classification procedure that employed Mini Mental Status Examination (MMSE) as a screening test and the KLOSCAD Neuropsychological Assessment Battery as a diagnostic test using deep learning. In addition, an evaluation procedure of redundant variables is introduced to prevent performance degradation. A missing data imputation method is also presented to increase the robustness by recovering information loss. The proposed deep neural networks (DNNs) architecture for the classification is validated through rigorous evaluation in comparison with various classifiers.ResultsThe k-nearest-neighbor imputation has been induced according to the proposed framework, and the proposed DNNs for two stage classification show the best accuracy compared to the other classifiers. Also, 49 redundant variables were removed, which improved diagnostic performance and suggested the potential of simplifying the assessment. Using this two-stage framework, we could get 8.06% higher diagnostic accuracy of dementia than MMSE alone and 64.13% less cost than KLOSCAD-N alone.ConclusionThe proposed framework could be applied to general dementia early detection programs to improve robustness, preciseness, and cost-effectiveness.

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