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.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Auricular vagus nerve stimulation (aVNS) is one of the promising neuromodulation techniques due to its non-invasiveness, convenience, and effectiveness. aVNS has been suggested as a potential treatment for neurodegenerative diseases showing impaired cerebrospinal fluid (CSF) dynamics. Improving CSF flow has been proposed as a key mechanism of the therapeutic effect on neurodegenerative diseases. However, aVNS parameters have been set empirically and the effective parameter that maximize the effect remains elusive. Here we show that 30 minutes of low-frequency aVNS increased arterial vasomotion events and enhanced cortical CSF influx along the branches of middle cerebral arteries. By using in vivo two photon imaging or widefield fluorescence microscopy with plasma and CSF tracers for visualizing blood vessels and perivascular spaces, arterial vasomotion and cortical CSF influx dynamics were acquired. The low-frequency (2 Hz) aVNS, but not middleand high-frequency (40 and 100 Hz) aVNS, significantly increased the number of vasomotion events compared to the sham group. Accordingly, in the CSF imaging, 2 Hz of aVNS markedly enhanced the CSF influx. Our findings demonstrate that lowfrequency aVNS is the effective parameter in respect to modulating vasomotion and CSF influx, resulting in brain clearance effect.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

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.

Auricular vagus nerve stimulation (aVNS) is one of the promising neuromodulation techniques due to its non-invasiveness, convenience, and effectiveness. aVNS has been suggested as a potential treatment for neurodegenerative diseases showing impaired cerebrospinal fluid (CSF) dynamics. Improving CSF flow has been proposed as a key mechanism of the therapeutic effect on neurodegenerative diseases. However, aVNS parameters have been set empirically and the effective parameter that maximize the effect remains elusive. Here we show that 30 minutes of low-frequency aVNS increased arterial vasomotion events and enhanced cortical CSF influx along the branches of middle cerebral arteries. By using in vivo two photon imaging or widefield fluorescence microscopy with plasma and CSF tracers for visualizing blood vessels and perivascular spaces, arterial vasomotion and cortical CSF influx dynamics were acquired. The low-frequency (2 Hz) aVNS, but not middleand high-frequency (40 and 100 Hz) aVNS, significantly increased the number of vasomotion events compared to the sham group. Accordingly, in the CSF imaging, 2 Hz of aVNS markedly enhanced the CSF influx. Our findings demonstrate that lowfrequency aVNS is the effective parameter in respect to modulating vasomotion and CSF influx, resulting in brain clearance effect.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

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.

Auricular vagus nerve stimulation (aVNS) is one of the promising neuromodulation techniques due to its non-invasiveness, convenience, and effectiveness. aVNS has been suggested as a potential treatment for neurodegenerative diseases showing impaired cerebrospinal fluid (CSF) dynamics. Improving CSF flow has been proposed as a key mechanism of the therapeutic effect on neurodegenerative diseases. However, aVNS parameters have been set empirically and the effective parameter that maximize the effect remains elusive. Here we show that 30 minutes of low-frequency aVNS increased arterial vasomotion events and enhanced cortical CSF influx along the branches of middle cerebral arteries. By using in vivo two photon imaging or widefield fluorescence microscopy with plasma and CSF tracers for visualizing blood vessels and perivascular spaces, arterial vasomotion and cortical CSF influx dynamics were acquired. The low-frequency (2 Hz) aVNS, but not middleand high-frequency (40 and 100 Hz) aVNS, significantly increased the number of vasomotion events compared to the sham group. Accordingly, in the CSF imaging, 2 Hz of aVNS markedly enhanced the CSF influx. Our findings demonstrate that lowfrequency aVNS is the effective parameter in respect to modulating vasomotion and CSF influx, resulting in brain clearance effect.