In this paper, the title was described incorrectly.

No abstract available.
Hernia, Diaphragmatic* ; Stomach Volvulus*

No abstract available.
Humans

No abstract available.

No Abstract Available.
Arthroplasty* ; Knee Joint* ; Knee*

No abstract available.
Leiomyoma* ; Vagina*

PURPOSE: To evaluate the correlation between lower urinary tract symptoms (LUTS) and premature ejaculation (PE) in Korean men older than 40 years. MATERIALS AND METHODS: In total, 258 men older than 40 years completed the International Prostate Symptom Score (IPSS; total score, storage symptoms [ST], and voiding symptoms [VD]), a 5-item version of the International Index of Erectile Function (IIEF-5), and the Premature Ejaculation Diagnostic Tool (PEDT). The study examined the relationship between LUTS and PE. In the PEDT, PE is defined as a score > or =11. RESULTS: The prevalence of PE was 29.1% with the PEDT versus a self-reported value of 49.5%. The prevalence of PE was 30.9% in 40 to 59-year-old men (21.3%) and 28.1% in 60 to 79 year-old men (78.7%). In men 40 to 59 and 60 to 79 years old, the mean PEDT, IPSS, and IIEF-5 scores were 8.65 and 7.88, 13.5 and 12.38, and 15.83 and 13.69, respectively. No significant correlations were observed between the total and subscale scores of the IPSS (p=0.204) and the PEDT (p=0.309) with increasing age, whereas a significant negative correlation was detected between the IIEF-5 and age (p=0.002). The PEDT score was significantly correlated with the IPSS-ST (r=0.326, p<0.001), IPSS-VD (r=0.183, p=0.005), IPSS-total (r=0.310, p<0.001), and IIEF-5 total (r=-0.248, p<0.001). CONCLUSIONS: LUTS, especially storage symptoms, were related to PE. In elderly men, control of both erectile dysfunction and LUTS may play an important role in managing PE.
Aged ; Ejaculation ; Erectile Dysfunction ; Female ; Humans ; Lower Urinary Tract Symptoms ; Male ; Middle Aged ; Premature Ejaculation ; Prevalence ; Prostate ; Quality of Life

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.