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.

Sensitive skin (SS) is increasingly recognized as a complex syndrome characterized by discomfort and heightened sensitivity to otherwise harmless stimuli, such as environmental changes, physical contact, and cosmetic products. This condition poses challenges in both diagnosis and treatment due to its variable presentation and subjective nature. The pathophysiological features of SS include neurogenic inflammation and small fiber neuropathy, largely driven by the hyperactivation of sensory nerves. This hyperactivation is closely associated with transient receptor potential (TRP) channels, particularly TRPV1, which contribute to the exaggerated sensory responses seen in SS. Furthermore, psychological factors like stress and anxiety, along with environmental stressors such as pollution and ultraviolet exposure, play significant roles in exacerbating symptoms. The diverse and individualized responses to stimuli make it difficult to establish standardized diagnostic criteria for SS, necessitating a combination of subjective diagnostic tools (e.g., the Sensitive Scale-10) and objective assessments (e.g., transepidermal water loss and lactic acid sting test) to accurately identify and assess SS. This paper provides a comprehensive review of SS, covering its definition, prevalence, pathogenesis, diagnostic challenges, and management strategies, and highlights the importance of personalized care in effectively managing SS and improving patient quality of life.

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.

Since severe acute respiratory syndrome-coronavirus-2 variant B.1.1.529 (omicron) was first reported to the World Health Organization on November 24, 2021, the cases of the omicron variant have been detected in more than 90 countries over the last month. We investigated the clinical and epidemiological characteristics of the first 40 patients with the omicron variant who had been isolated at the National Medical Center in South Korea during December 4–17, 2021. The median age of the patients was 39.5 years. Twenty-two patients (55%) were women. Seventeen patients (42.5%) were fully vaccinated, and none were reinfected with the omicron. Eighteen (45%) had recent international travel history. Half of the patients (19, 47.5%) were asymptomatic, while the others had mild symptoms. Six patients (15%) showed lung infiltrations on chest image; however, none required supplemental oxygen. These mild clinical features are consistent with recent case reports on the omicron variant from other countries.

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.