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Predicting the course of depression is necessary for personalized treatment. Impaired glucose metabolism (IGM) was introduced as a promising depression biomarker, but no consensus was made. This study aimed to predict IGM at the time of depression diagnosis and examine the relationship between long-term prognosis and predicted results.
Clinical data were extracted from four electronic health records in South Korea. The study population included patients with depression, and the outcome was IGM within 1 year. One database was used to develop the model using three algorithms. External validation was performed using the best algorithm across the three databases. The area under the curve (AUC) was calculated to determine the model’s performance. Kaplan–Meier and Cox survival analyses of the risk of hospitalization for depression as the long-term outcome were performed. A meta-analysis of the long-term outcome was performed across the four databases.
A prediction model was developed using the data of 3,668 people, with an AUC of 0.781 with least absolute shrinkage and selection operator (LASSO) logistic regression. In the external validation, the AUCs were 0.643, 0.610, and 0.515. Through the predicted results, survival analysis and meta-analysis were performed; the hazard ratios of risk of hospitalization for depression in patients predicted to have IGM was 1.20 (95% confidence interval [CI] 1.02–1.41, p = 0.027) at a 3-year follow-up.
We developed prediction models for IGM occurrence within a year. The predicted results were related to the long-term prognosis of depression, presenting as a promising IGM biomarker related to the prognosis of depression.
Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.
The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy.
Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively.
We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.
In this review, we introduce our recent applications of deep learning to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) generation of solar farside/backside magnetograms and global field extrapolation based on them, (2) generation of solar UV/EUV images from other UV/EUV images and magnetograms, (3) denoising solar magnetograms using supervised learning, (4) generation of UV/EUV images and magnetograms from Galileo sunspot drawings, (5) improvement of global IRI TEC maps using IGS TEC ones, (6) one-day forecasting of global TEC maps through image translation, (7) generation of high-resolution magnetograms from Ca II K images, (8) super-resolution of solar magnetograms, (9) flare classification by CNN and visual explanation by attribution methods, and (10) forecasting GOES solar X-ray profiles. We present major results and discuss them. We also present future plans for integrated space weather models based on deep learning.
The explosive outbreak of COVID-19 led to a shortage of medical resources, including isolation rooms in hospitals, healthcare workers (HCWs) and personal protective equipment. Here, we constructed a new model, non-contact community treatment centres to monitor and quarantine asymptomatic and mildly symptomatic COVID-19 patients who recorded their own vital signs using a smartphone application. This new model in Korea is useful to overcome shortages of medical resources and to minimise the risk of infection transmission to HCWs.
There is limited evidence on the interaction by alcohol dehydrogenase 2 (ADH1B) (rs1229984) and aldehyde dehydrogenase 2 (ALDH2) (rs671) regarding the associations of alcohol and a methyl diet (low folate and high alcohol intake) with cancer risk, partly because of rare polymorphisms in Western populations.
In a case–control study, we estimated the ORs and 95 % CIs to evaluate the associations of ADH1B and ALDH2 genotypes with colorectal cancer (CRC) and the joint association between methyl diets and ADH1B and ALDH2 polymorphisms with CRC risk using logistic regression models.
A hospital-based case–control study.
In total, 1001 CRC cases and 899 cancer-free controls admitted to two university hospitals.
We found that alcohol intake increased the risk of CRC; OR (95 % CI) was 2·02 (1·41, 2·87) for ≥60 g/d drinkers compared with non-drinkers (Ptrend < 0·001). The associations for two polymorphisms with CRC were not statistically significant. However, we found a potential interaction of ALDH2 with methyl diets and CRC. We observed a 9·08-fold (95 % CI 1·93, 42·60) higher risk of CRC for low-methyl diets compared with high-methyl diets among individuals with an A allele of ALDH2, but the association was not apparent among those with ALDH2 GG (Pinteraction = 0·02).
Our data support the evidence that gene–methyl diet interactions may be involved in CRC risk in East Asian populations, showing that a low-methyl diet increased the risk of CRC among individuals with an A allele of ALDH2.
We trace Sn nanoparticles (NPs) produced from SnO2 nanotubes (NTs) during lithiation initialized by high energy e-beam irradiation. The growth dynamics of Sn NPs is visualized in liquid electrolytes by graphene liquid cell transmission electron microscopy. The observation reveals that Sn NPs grow on the surface of SnO2 NTs via coalescence and the final shape of agglomerated NPs is governed by surface energy of the Sn NPs and the interfacial energy between Sn NPs and SnO2 NTs. Our result will likely benefit more rational material design of the ideal interface for facile ion insertion.
A vertebrate burrow-bearing layer of late Pleistocene age is commonly found at many Paleolithic archaeological sites in Korea. The burrows are straight to slightly curved in horizontal (plan) view and gently inclined in lateral (sectional) view. They are interpreted as having been produced by rodent-like mammals based on their size and architecture. The significance of such burrow-bearing layers as a characteristic stratigraphic marker unit is demonstrated by high burrow abundance, consistent stratigraphic position, lack of stratigraphic recurrence at these sites, and widespread geographic extent. Three dating methods, tephrochronology, radiocarbon, and OSL dating, were used to infer the age of these burrow-bearing layers. The dating results indicate that they were formed between ca. 40,000 and 25,000 yr (MIS 3−2), and this suggests that this layer can be used as a stratigraphic time-marker in late Pleistocene paleosol sequences for this region.
Personality may predispose family caregivers to experience caregiving differently in similar situations and influence the outcomes of caregiving. A limited body of research has examined the role of some personality traits for health-related quality of life (HRQoL) among family caregivers of persons with dementia (PWD) in relation to burden and depression.
Data from a large clinic-based national study in South Korea, the Caregivers of Alzheimer's Disease Research (CARE), were analyzed (N = 476). Path analysis was performed to explore the association between family caregivers’ personality traits and HRQoL. With depression and burden as mediating factors, direct and indirect associations between five personality traits and HRQoL of family caregivers were examined.
Results demonstrated the mediating role of caregiver burden and depression in linking two personality traits (neuroticism and extraversion) and HRQoL. Neuroticism and extraversion directly and indirectly influenced the mental HRQoL of caregivers. Neuroticism and extraversion only indirectly influenced their physical HRQoL. Neuroticism increased the caregiver's depression, whereas extraversion decreased it. Neuroticism only was mediated by burden to influence depression and mental and physical HRQoL.
Personality traits can influence caregiving outcomes and be viewed as an individual resource of the caregiver. A family caregiver's personality characteristics need to be assessed for tailoring support programs to get the optimal benefits from caregiver interventions.
Devastating disasters around the world directly contribute to significant increases in human mortality and economic costs. The objective of this study was to examine the current state of the Korea Disaster Relief Team that participated in an international training module.
The whole training period was videotaped in order to observe and evaluate the respondents. The survey was carried out after completion of the 3-day training, and the scores were reported by use of a 5-point Likert scale.
A total of 43 respondents were interviewed for the survey, and the results showed that the overall preparedness score for international disasters was 3.4±1.6 (mean±SD). The awareness of the Incident Command System for international disasters was shown to be low (3.5±1.1). Higher scores were given to personnel who took on leadership roles in the team and who answered “I knew my duty” (4.4±0.6) in the survey, as well as to the training participants who answered “I clearly knew my duty” (4.5±0.5).
The preparedness level of the Korea Disaster Relief Team was shown to be insufficient, whereas understanding of the roles of leaders and training participants in the rescue team was found to be high. It is assumed that the preparedness level for disaster relief must be improved through continued training. (Disaster Med Public Health Preparedness. 2016;1–5)