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  1. Article ; Online: Interventions to reduce symptoms of common mental disorders and suicidal ideation in physicians.

    Na, Kyoung-Sae

    The lancet. Psychiatry

    2019  Volume 6, Issue 5, Page(s) 369

    MeSH term(s) Humans ; Mental Disorders ; Suicidal Ideation ; Surveys and Questionnaires
    Language English
    Publishing date 2019-04-21
    Publishing country England
    Document type Letter ; Comment
    ISSN 2215-0374
    ISSN (online) 2215-0374
    DOI 10.1016/S2215-0366(19)30084-7
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  2. Article ; Online: Incomplete discussion of bipolar disorder and comorbid substance use disorder.

    Na, Kyoung-Sae

    The Lancet. Global health

    2019  Volume 7, Issue 7, Page(s) e846

    MeSH term(s) Bipolar Disorder ; Cross-Sectional Studies ; Developing Countries ; Humans ; Prisoners ; Substance-Related Disorders
    Language English
    Publishing date 2019-06-11
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 2723488-5
    ISSN 2214-109X ; 2214-109X
    ISSN (online) 2214-109X
    ISSN 2214-109X
    DOI 10.1016/S2214-109X(19)30237-2
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  3. Article ; Online: Anxiety and Depression Prevalence in Children, Adolescents, and Young Adults With Life-Limiting Conditions.

    Na, Kyoung-Sae

    JAMA pediatrics

    2019  Volume 174, Issue 2, Page(s) 207–208

    MeSH term(s) Adolescent ; Anxiety/epidemiology ; Anxiety Disorders ; Child ; Depression/epidemiology ; Humans ; Incidence ; Prevalence ; Young Adult
    Language English
    Publishing date 2019-12-13
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 2701223-2
    ISSN 2168-6211 ; 2168-6203
    ISSN (online) 2168-6211
    ISSN 2168-6203
    DOI 10.1001/jamapediatrics.2019.4800
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  4. Article ; Online: More Information Needed to Understand Low Levels of TNFα and IFNγ in Chronic PTSD.

    Na, Kyoung-Sae

    The American journal of psychiatry

    2019  Volume 177, Issue 1, Page(s) 93

    MeSH term(s) Humans ; Prospective Studies ; Risk Factors ; Stress Disorders, Post-Traumatic ; Tumor Necrosis Factor-alpha
    Chemical Substances Tumor Necrosis Factor-alpha
    Language English
    Publishing date 2019-12-24
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 280045-7
    ISSN 1535-7228 ; 0002-953X
    ISSN (online) 1535-7228
    ISSN 0002-953X
    DOI 10.1176/appi.ajp.2019.19080792
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  5. Article ; Online: Prediction of future cognitive impairment among the community elderly: A machine-learning based approach.

    Na, Kyoung-Sae

    Scientific reports

    2019  Volume 9, Issue 1, Page(s) 3335

    Abstract: The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive impairment ... ...

    Abstract The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive impairment using easy-to-collect variables by non-invasive methods for community-dwelling elderly is useful prior to conducting such a comprehensive evaluation. This study aimed to develop a machine learning-based predictive model for future cognitive impairment. A total of 3424 community elderly without cognitive impairment were included from the nationwide dataset. The gradient boosting machine (GBM) was exploited to predict cognitive impairment after 2 years. The GBM performance was good (sensitivity = 0.967; specificity = 0.825; and AUC = 0.921). This study demonstrated that a machine learning-based predictive model might be used to screen future cognitive impairment using variables, which are commonly collected in community health care institutions. With efforts of enhancing the predictive performance, such a machine learning-based approach can further contribute to the improvement of the cognitive function in community elderly.
    MeSH term(s) Aged ; Cognition Disorders/physiopathology ; Female ; Humans ; Machine Learning ; Male
    Language English
    Publishing date 2019-03-04
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-019-39478-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Prevalence and Correlates of Comorbid Posttraumatic Stress Disorder in Schizophrenia-Spectrum Disorder: A Systematic Review and Meta-Analysis.

    Seong, Anna / Cho, Seo-Eun / Na, Kyoung-Sae

    Psychiatry investigation

    2023  Volume 20, Issue 6, Page(s) 483–492

    Abstract: Objective: Schizophrenia-spectrum disorders and posttraumatic stress disorder (PTSD) share common clinical manifestations, genetic vulnerability, and environmental risk factors. We aimed to conduct a systematic review and meta-analysis of the comorbid ... ...

    Abstract Objective: Schizophrenia-spectrum disorders and posttraumatic stress disorder (PTSD) share common clinical manifestations, genetic vulnerability, and environmental risk factors. We aimed to conduct a systematic review and meta-analysis of the comorbid prevalence of PTSD among schizophrenia-spectrum disorders.
    Methods: We performed a meta-analysis to identify possible contributing factors to the heterogeneity among these studies. We systematically searched electronic databases with no restrictions on language of articles.
    Results: We extracted 24 samples (18 for current prevalence and 6 for lifetime prevalence) from 22 studies and used a random effects model to estimate the pooled prevalence of PTSD among schizophrenia-spectrum disorders. The current and life prevalence of comorbid PTSD was 10.6% (95% confidence interval [CI]=6.3%-17.3%) and 13.0% (95% CI=5.3%-28.6%), respectively. Studies assessing psychotic experiences/involuntary admission reported the highest prevalence of comorbid PTSD (57.1%, 95% CI=43.6%-59.7%), whereas those assessing various anxiety disorders reported the lowest prevalence (1.1%, 95% CI=1.0%-5.5%). Heterogeneities of the subgroup analysis by similar objectives were largely homogeneous (I2=7.1-34.1). In the qualitative assessment, only two studies (9.1%) were evaluated as having a low risk of bias.
    Conclusion: Our results showed that a careful approach with particular attention to assessing PTSD is essential to reliably estimate the prevalence of PTSD comorbid with schizophrenia-spectrum disorders. The reason for the wide discrepancy in the prevalence of comorbid PTSD among the four groups of studies should be addressed in future research.
    Language English
    Publishing date 2023-05-30
    Publishing country Korea (South)
    Document type Journal Article
    ZDB-ID 2414364-9
    ISSN 1976-3026 ; 1738-3684
    ISSN (online) 1976-3026
    ISSN 1738-3684
    DOI 10.30773/pi.2022.0353
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    Zs.A 6598: Show issues Location:
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  7. Article ; Online: Prediction of future cognitive impairment among the community elderly

    Kyoung-Sae Na

    Scientific Reports, Vol 9, Iss 1, Pp 1-

    A machine-learning based approach

    2019  Volume 9

    Abstract: Abstract The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive ... ...

    Abstract Abstract The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive impairment using easy-to-collect variables by non-invasive methods for community-dwelling elderly is useful prior to conducting such a comprehensive evaluation. This study aimed to develop a machine learning-based predictive model for future cognitive impairment. A total of 3424 community elderly without cognitive impairment were included from the nationwide dataset. The gradient boosting machine (GBM) was exploited to predict cognitive impairment after 2 years. The GBM performance was good (sensitivity = 0.967; specificity = 0.825; and AUC = 0.921). This study demonstrated that a machine learning-based predictive model might be used to screen future cognitive impairment using variables, which are commonly collected in community health care institutions. With efforts of enhancing the predictive performance, such a machine learning-based approach can further contribute to the improvement of the cognitive function in community elderly.
    Keywords Medicine ; R ; Science ; Q
    Subject code 120
    Language English
    Publishing date 2019-03-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression.

    Na, Kyoung-Sae / Kim, Yong-Ku

    Advances in experimental medicine and biology

    2021  Volume 1305, Page(s) 57–69

    Abstract: Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on ... ...

    Abstract Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.
    MeSH term(s) Brain/diagnostic imaging ; Depression ; Depressive Disorder, Major/diagnostic imaging ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Neuroimaging
    Language English
    Publishing date 2021-04-08
    Publishing country United States
    Document type Journal Article
    ISSN 2214-8019 ; 0065-2598
    ISSN (online) 2214-8019
    ISSN 0065-2598
    DOI 10.1007/978-981-33-6044-0_4
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  9. Article ; Online: Associations between Melatonin, Neuroinflammation, and Brain Alterations in Depression

    Eunsoo Won / Kyoung-Sae Na / Yong-Ku Kim

    International Journal of Molecular Sciences, Vol 23, Iss 305, p

    2022  Volume 305

    Abstract: Pro-inflammatory systemic conditions that can cause neuroinflammation and subsequent alterations in brain regions involved in emotional regulation have been suggested as an underlying mechanism for the pathophysiology of major depressive disorder (MDD). ... ...

    Abstract Pro-inflammatory systemic conditions that can cause neuroinflammation and subsequent alterations in brain regions involved in emotional regulation have been suggested as an underlying mechanism for the pathophysiology of major depressive disorder (MDD). A prominent feature of MDD is disruption of circadian rhythms, of which melatonin is considered a key moderator, and alterations in the melatonin system have been implicated in MDD. Melatonin is involved in immune system regulation and has been shown to possess anti-inflammatory properties in inflammatory conditions, through both immunological and non-immunological actions. Melatonin has been suggested as a highly cytoprotective and neuroprotective substance and shown to stimulate all stages of neuroplasticity in animal models. The ability of melatonin to suppress inflammatory responses through immunological and non-immunological actions, thus influencing neuroinflammation and neurotoxicity, along with subsequent alterations in brain regions that are implicated in depression, can be demonstrated by the antidepressant-like effects of melatonin. Further studies that investigate the associations between melatonin, immune markers, and alterations in the brain structure and function in patients with depression could identify potential MDD biomarkers.
    Keywords melatonin ; neuroinflammation ; major depressive disorder ; biomarker ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
    Subject code 616
    Language English
    Publishing date 2022-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article: The Development of a Suicidal Ideation Predictive Model for Community-Dwelling Elderly Aged >55 Years.

    Na, Kyoung-Sae / Geem, Zong Woo / Cho, Seo-Eun

    Neuropsychiatric disease and treatment

    2022  Volume 18, Page(s) 163–172

    Abstract: Purpose: Suicide is an important health and social concern worldwide. Both suicidal ideation and suicide rates are higher in the elderly population than in other age groups; thus, more careful attention and targeted interventions are required. Therefore, ...

    Abstract Purpose: Suicide is an important health and social concern worldwide. Both suicidal ideation and suicide rates are higher in the elderly population than in other age groups; thus, more careful attention and targeted interventions are required. Therefore, we have developed a model to predict suicidal ideation in the community-dwelling elderly aged of >55 years.
    Patients and methods: A random forest algorithm was applied to those who participated in the Korea Welfare Panel. We used a total of 26 variables as potential predictors. To resolve the imbalance in the dataset resulting from the low frequency of suicidal ideation, training was performed by applying the synthetic minority oversampling technique. The performance index was calculated by applying the predictive model to the test set, which was not included in the training process.
    Results: A total of 6410 elderly Korean aged of >55 (mean, 71.48; standard deviation, 9.56) years were included in the analysis, of which 2.7% had suicidal ideation. The results for predicting suicidal ideation using the 26 chosen variables showed an AUC of 0.879, accuracy of 0.871, sensitivity of 0.750, and specificity of 0.874. The most significant variable in the predictive model was the severity of depression, followed by life satisfaction and self-esteem factors. Basic demographic variables such as age and gender demonstrated a relatively small effect.
    Conclusion: Machine learning can be used to create algorithms for predicting suicidal ideation in community-dwelling elderly. However, there are limitations to predicting future suicidal ideation. A predictive model that includes both biological and cognitive indicators should be created in the future.
    Language English
    Publishing date 2022-02-02
    Publishing country New Zealand
    Document type Journal Article
    ZDB-ID 2186503-6
    ISSN 1178-2021 ; 1176-6328
    ISSN (online) 1178-2021
    ISSN 1176-6328
    DOI 10.2147/NDT.S336947
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