Objectives: ：The coronavirus disease 2019 (COVID-19) outbreak is a global medical crisis imposing particular burden on public sector employees. The aim of this study was to investigate the psychiatric distress among public sector workers amid the COVID-19 pandemic. Methods: ：We conducted a cross-sectional study with 531 public sector workers in Gwangmyung city who completed Korean versions of the Perceived Stress Scale (PSS), Patient Health Questionnaire-9 (PHQ-9), and Impact of Event Scale-Revised-Korean (IES-R-K). Results: ：The results revealed more than moderate levels of stress (85.2%), depressive symptoms (22.2%), and posttraumatic stress symptoms (38.8%). PSS total score was significantly correlated with PHQ-9, IES-R-K total scores as well as IES-R-K subscale scores. Total scores on the PSS, PHQ-9, and IES-R-K were all inversely correlated with age. Conclusions ：COVID-19-related workers experience considerable stress and depressive symptoms, with self-rated stress correlating significantly with depression scores. Age may serve as a protective factor against oc-cupational stress and burnout. These findings highlight the need for adequate psychiatric screening and interven-tion for public sector workers.

Objectives: The coronavirus disease (COVID-19) pandemic has had various effects on mankind, especially children and adolescents.Because children and adolescents spend a lot of time at school, COVID-19 has had a great impact on school mental health. In this study, we investigated the effect of prolonged COVID-19 on school mental health. Methods: We prepared self-report questionnaires for depression (Children’s Depression Inventory, CDI), anxiety (Korean version of the Penn State Worry Questionnaire for Children; Generalized Anxiety Disorder-7, GAD-7), and post-traumatic stress (Primary Care Post-traumatic Stress Disorder, PC-PTSD) for administering to students aged between 7 and 18 years, recruited by a COVID-19 psychological prevention support group in the Gwangmyeong Mental Health Welfare Center for 2 years, in 2020 and 2021. Results: For children aged 7–12 years, there was no significant difference between the years 2020 and 2021 in the assessment of depression, anxiety, and post-traumatic stress. Conversely, for adolescents aged 13–18 years, there was a significant increase in the scale scores (CDI, PC-PTSD, and GAD-7). Conclusion Prolonged COVID-19 might have had a significant impact on the mental health of adolescents who spent a lot of time at school. When comparing the years 2020 and 2021, middle and high school students were more affected by COVID-19 than elementary school students.

Objective: Cyber addiction, which is more vulnerable in adolescents, is defined as the excessive use of computers and the Internet that causes serious psychological, social, and physical problems. In this study, we investigated the resting-state functional connectivity (rsFC) in adolescents with cyber addiction. Methods: We collected and analyzed resting-state functional neuroimaging data of 20 patients with cyber addiction, aged 13−18 years, and 27 healthy controls. Based on previous studies, the seed regions included the dorsolateral prefrontal cortex, medial orbitofrontal cortex, lateral orbitofrontal cortex, dorsal anterior cingulate cortex, insula, hippocampus, amygdala, nucleus accumbens, and the ventral tegmental area. Seed-to-voxel analyses were performed to investigate the differences between patients and healthy controls. A correlation analysis between rsFC and cyber addiction severity was also performed. Results: Patients with cyber addiction showed the following characteristics: increased positive rsFC between the left insular−right middle temporal gyrus; increased positive rsFC between the right hippocampus−right precentral gyrus;increased positive rsFC between the right amygdala−right precentral gyrus and right parietal operculum cortex; increased negative rsFC between the left nucleus accumbens−right cerebellum crus II and right cerebellum VI. Conclusion Adolescents with cyber addiction show altered functional connectivity during the resting state. The findings of this study may help us better understand the neuropathology of cyber addiction in adolescents.

OBJECTIVE: To analyze the relationship between seizure threshold (ST) and psychotropic drugs in patients treated with ECT. METHODS: We examined clinical data from 43 patients. ST was titrated at each treatment session. We examined associations between ST and psychotropic drugs using multivariate correlation analyses. Data are presented as initial ST, the difference in ST between the first and 10th sessions (ΔST(10th)), and the mean difference in ST between the first and last sessions (mean ΔST(last)). RESULTS: Multivariate regression analyses showed associations between initial ST and the total chlorpromazine-equivalent dose of antipsychotics (β=0.363, p<0.05). The total fluoxetine-equivalent dose of antidepressants was associated with ΔST(10th) (β=0.486, p<0.01) and mean ΔST(last) (β=0.472, p<0.01). CONCLUSION: Our study elucidated possible effects of psychotropic drugs on ST shifts. Larger doses of antipsychotics were associated with higher initial ST, whereas higher doses of antidepressants were associated with stronger shifts in ST.
Antidepressive Agents ; Antipsychotic Agents ; Electroconvulsive Therapy* ; Humans ; Psychotropic Drugs* ; Seizures*

BACKGROUND/AIMS: Toxoplasmic encephalitis (TE) is one of the most common causes of focal brain lesions, which complicate the course of acquired immunodeficiency syndrome (AIDS). There is wide geographic variation in the prevalence of toxoplasma infection. This study was performed to characterize toxoplasma infection in human immunodeficiency virus (HIV)-infected patients in South Korea. METHODS: We retrospectively examined the incidence and clinical characteristics of TE in 683 HIV-infected patients who were enrolled between 1990 and 2008 at four university hospitals in Busan, Korea. We also assessed the seroprevalence of IgG antibodies to Toxoplasma gondii, risk factors for toxoplasma seropositivity, and seroconversion rates during the course of HIV infection. RESULTS: Among 683 HIV-infected patients, six (0.9%) patients were diagnosed with TE. The incidence of TE was 0.34 per 100 person-years (py) during the study period. Of the 414 patients who had undergone serological examinations for Toxoplasma gondii, 35 (8.5%) patients were seropositive. Univariate analysis showed that the risk factors associated with toxoplasma seropositivity included increased age, heterosexual transmission, marriage, and a history of overseas residence (p<0.05). Of these factors, a history of overseas residence was a significant risk factor in a multivariate analysis (p<0.05). A total of 95 patients who were seronegative on their initial screen showed serial toxoplasma IgG antibodies (mean duration of follow-up, 2.1 years). Among these patients, only two (2.1%) acquired IgG antibodies to Toxoplasma gondii during the follow-up period. CONCLUSIONS: The seroprevalence of anti-toxoplasma IgG antibodies in HIV-infected patients in Korea was 8.5%. A history of overseas residence was a significant risk factor for toxoplasma seropositivity. The incidence of TE was 0.34/100 py, which is lower than that reported in other countries. Toxoplasma seroconversion was also uncommon (2.1%).
Acquired Immunodeficiency Syndrome ; Antibodies ; Brain ; Encephalitis ; Follow-Up Studies ; Heterosexuality ; HIV ; HIV Infections ; Hospitals, University ; Humans ; Immunoglobulin G ; Incidence ; Korea ; Marriage ; Multivariate Analysis ; Prevalence ; Republic of Korea ; Retrospective Studies ; Risk Factors ; Seroepidemiologic Studies ; Toxoplasma

BACKGROUND: Nucleophosmin gene (NPM1) mutation may be a good molecular marker for assessing the clinical status and predicting the outcomes in AML patients. We evaluated the applicability of NPM1 type A mutation (NPM1-mutA) quantitation for this purpose. METHODS: Twenty-seven AML patients with normal karyotype but bearing the mutated NPM1 were enrolled in the study, and real-time quantitative PCR of NPM1-mutA was performed on 93 bone marrow (BM) samples (27 samples at diagnosis and 56 at follow-up). The NPM1-mutA allele burdens (represented as the NPM1-mutA/Abelson gene (ABL) ratio) at diagnosis and at follow-up were compared. RESULTS: The median NPM1-mutA/ABL ratio was 1.3287 at diagnosis and 0.092 at 28 days after chemotherapy, corresponding to a median log10 reduction of 1.7061. Significant correlations were observed between BM blast counts and NPM1-mutA quantitation results measured at diagnosis (γ=0.5885, P=0.0012) and after chemotherapy (γ=0.5106, P=0.0065). Total 16 patients achieved morphologic complete remission at 28 days after chemotherapy, and 14 (87.5%) patients showed a >3 log10 reduction of the NPM1-mutA/ABL ratio. The NPM1-mutA allele was detected in each of five patients who had relapsed, giving a median increase of 0.91-fold of the NPM1-mutA/ABL ratio at relapse over that at diagnosis. CONCLUSIONS: The NPM1-mutA quantitation results corresponded to BM assessment results with high stability at relapse, and could predict patient outcomes. Quantitation of the NPM1-mutA burden at follow-up would be useful in the management of AML patients harboring this gene mutation.
Antineoplastic Agents/therapeutic use ; Bone Marrow/metabolism/pathology ; Cytarabine/therapeutic use ; Daunorubicin ; Humans ; Karyotype ; Leukemia, Myeloid, Acute/drug therapy/genetics/*pathology ; Mutation ; Nuclear Proteins/*genetics/metabolism ; Real-Time Polymerase Chain Reaction ; Recurrence ; Remission Induction ; Retrospective Studies ; Sequence Analysis, DNA ; fms-Like Tyrosine Kinase 3/genetics

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.