Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Background/Aims: Besifovir dipivoxil maleate (BSV) and tenofovir alafenamide fumarate (TAF) have been recently approved in Korea as the initial antiviral agents for chronic hepatitis B (CHB).However, the real-world outcome data for these drugs remain limited. Therefore, we conducted a noninferiority analysis using real-world data to compare the clinical outcomes of the two nucleotide analogs in treatment-naïve patients with CHB. Methods: We retrospectively investigated a cohort of patients with CHB who received BSV or TAF as first-line antiviral agents. The endpoints were virological response (VR) and liver-related clinical outcomes. Results: A total of 537 patients, consisting of 202 and 335 patients administered BSV and TAF, respectively, were followed up for 42 months. No significant difference was observed between the VRs of the patients from the two groups. The rates of biochemical response, virologic breakthrough, and incidence rates of hepatocellular carcinoma did not differ between the groups. However, the hepatitis B e antigen seroclearance rate was higher and the renal function declined less in the BSV group. Multivariable analysis indicated older age, alcohol abuse, cirrhosis and ascites, and lower serum HBV DNA level to be independently associated with increased hepatocellular carcinoma risk. The 1:1 propensity score-matched analysis with 400 patients showed VR rates of 85.0% and 88.7% in the BSV and TAF group patients, respectively, at 2 years. The absolute value of the 95% confidence interval for the difference (–0.04 to 0.12) satisfied the a priori limit of a noninferiority of 0.15. Conclusions BSV is noninferior to TAF in terms of VR, and their clinical outcomes are comparable to CHB.

Purpose: This study investigated correlations between the actual sleep time 24 hours prior to an examination and the time to achieve chloral hydrate sedation in pediatric patients. Methods: With parental consent, 84 children who were placed under moderate or deep sedation with chloral hydrate for examinations from November 19, 2020 to July 9, 2022 were recruited. Results: Patients' average age was 19.9 months. Pediatric neurology patients and those who underwent electroencephalography took significantly longer to achieve sedation with chloral hydrate. There was a negative correlation between the time to achieve sedation and actual sleep time within 24 hours prior to the examination. Positive correlations were found between the actual sleep time 24 hours prior to the examination and the second dose per weight, as well as between the sedation recovery time and awake hours before the examination. Conclusion Sleep restriction is not an effective adjuvant therapy for chloral hydrate sedation in children, and sedation effects vary according to pediatric patients' characteristics. Therefore, it would be possible to reduce the unnecessary efforts of caregivers who restrict children's sleep for examinations. It is more important to educate parents about safe sedation than about sleep restriction.

Concerns about the effectiveness of current vaccines against the rapidly spreading severe acute respiratory syndrome-coronavirus-2 omicron (B.1.1.529) variant are increasing. This study aimed to assess neutralizing antibody activity against the wild-type (BetaCoV/Korea/ KCDC03/2020), delta, and omicron variants after full primary and booster vaccinations with BNT162b2. A plaque reduction neutralization test was employed to determine 50% neutralizing dilution (ND 50 ) titers in serum samples. ND 50 titers against the omicron variant (median [interquartile range], 5.3 [< 5.0–12.7]) after full primary vaccination were lower than those against the wild-type (144.8 [44.7–294.0]) and delta (24.3 [14.3–81.1]) variants.Furthermore, 19/30 participants (63.3%) displayed lower ND 50 titers than the detection threshold (< 10.0) against omicron after full primary vaccination. However, the booster vaccine significantly increased ND 50 titers against BetaCoV/Korea/KCDC03/2020, delta, and omicron, although titers against omicron remained lower than those against the other variants (P < 0.001). Our study suggests that booster vaccination with BNT162b2 significantly increases humoral immunity against the omicron variant.

Background/Aims: Efficient anti-fibrotic therapies are required for the treatment of liver cirrhosis. Hydroxymethylglutaryl-coenzyme A reductase inhibitors (statins) and cyclooxygenase-2 (COX-2) inhibitors have been reported to have anti-fibrotic effects. Here, we investigated whether combined treatment with a statin and a COX-2 inhibitor has synergistic anti-fibrotic effects. Methods: The effects of treatment strategies incorporating both simvastatin and a COX-2 inhibitor, NS-398, were investigated using an immortalized human hepatic stellate cell line (LX-2) and a hepatic fibrosis mouse model developed using thioacetamide (TAA) in drinking water. Cellular proliferation was investigated via 5-bromo-2-deoxyuridine uptake. Pro- and anti-apoptotic factors were investigated through Western blotting and real-time polymerase chain reaction analysis. Results: The evaluation of the anti-proliferative effects on LX-2 cells showed that the observed effects were more pronounced with combination therapy than with single-drug therapy. Moreover, hepatic fibrosis and collagen deposition decreased significantly in TAA-treated mice in response to the combined treatment strategy. The mechanisms underlying the anti-fibrotic effects of the combination therapy were investigated. The effects of the combination therapy were correlated with increased expression levels of extracellular signal-regulated kinase 1/2 signaling molecules, upregulation of the Bax/Bcl-2 signaling pathway, inhibition of the transforming growth factor-β signaling pathway, and inhibition of tissue inhibitor of matrix metalloproteinases 1 and 2. Conclusions The combination of simvastatin and NS-398 resulted in a synergistic anti-fibrotic effect through multiple pathways. These findings offer a theoretical insight into the possible clinical application of this strategy for the treatment of advanced liver diseases with hepatic fibrosis.

Background: Although respiratory tract infection is one of the most important factors triggering acute exacerbation of chronic obstructive pulmonary disease (AE-COPD), limited data are available to suggest an epidemiologic pattern of microbiology in South Korea. Methods: A multicenter observational study was conducted between January 2015 and December 2018 across 28 hospitals in South Korea. Adult patients with moderate-to-severe acute exacerbations of COPD were eligible to participate in the present study. The participants underwent all conventional tests to identify etiology of microbial pathogenesis. The primary outcome was the percentage of different microbiological pathogens causing AE-COPD. A comparative microbiological analysis of the patients with overlapping asthma–COPD (ACO) and pure COPD was performed. Results: We included 1,186 patients with AE-COPD. Patients with pure COPD constituted 87.9% and those with ACO accounted for 12.1%. Nearly half of the patients used an inhaled corticosteroid-containing regimen and one-fifth used systemic corticosteroids. Respiratory pathogens were found in 55.3% of all such patients. Bacteria and viruses were detected in 33% and 33.2%, respectively. Bacterial and viral coinfections were found in 10.9%. The most frequently detected bacteria were Pseudomonas aeruginosa (9.8%), and the most frequently detected virus was influenza A (10.4%). Multiple bacterial infections were more likely to appear in ACO than in pure COPD (8.3% vs. 3.6%, p=0.016). Conclusion Distinct microbiological patterns were identified in patients with moderate-to-severe AE-COPD in South Korea. These findings may improve evidence-based management of patients with AE-COPD and represent the basis for further studies investigating infectious pathogens in patients with COPD.