No abstract available.
Drug Therapy* ; Methotrexate* ; Uterine Cervical Neoplasms*

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.

Liver cancer is more complex to treat compared to cancers in other organs, since liver function should be considered. In addition, only a few patients can be applied curative treatment due to advanced stage at diagnosis. Therefore, early stage detection is important and has been increased through screening and surveillance programs using image modalities recently. However, it is still difficult to diagnose small or hypovascular hepatocellular carcinoma (HCC) even using advanced image modalties. In particular, hypovascular HCCs do not show arterial contrast enhancement which is a typical finding of HCC on computed tomography (CT) and magnetic resonance imaging (MRI). Those also account for a considerable portion of early HCC. We present 54 yearsold man who had recurrent hypervascular and hypovascular nodules on three phase CT and gadoxetic acid-enhanced MRI. The nodules were removed by surgical resection and confirmed as combined hepatocellular-cholangiocarcinoma and well differentiated HCC respectively.
Carcinoma, Hepatocellular* ; Cholangiocarcinoma* ; Diagnosis ; Early Diagnosis ; Humans ; Liver ; Liver Neoplasms ; Magnetic Resonance Imaging ; Mass Screening

PURPOSE: With in vitro fertilization (IVF) becoming a common treatment for infertility, there has been an increasing number of studies on perinatal complications related to IVF. This study compares the clinical characteristics of twins at gestational age less than 34 weeks, between IVF and spontaneous conceived. METHODS: We retrospectively reviewed the medical records of 138 preterm twins at gestational age less than 34 weeks, admitted between January 2009 and December 2011 to the neonatal intensive care units of two hospitals. Maternal and preterm infant's clinical characteristics of 58 IVF and 80 spontaneous conceived twins were compared. RESULTS: Maternal age was older in IVF twins (33.3+/-2.8 vs. 31.3+/-4.2, P-value=0.007), and there was no other significant difference between the two maternal groups. Gestational age was lesser in IVF twins (30.6+/-3.2 vs. 31.2+/-2.7, P-value=0.048). Age at the day of full enteral feeding (24.2+/-12.1 vs. 18.2+/-13.2, P-value<0.001) and age at day of full oral feeding (30.1+/-18.5 vs. 25.3+/-19.2, P-value<0.001) were significantly longer in IVF twins as adjusted by gestational age. Retinopathy of prematurity (ROP) showed higher incidence in IVF twins (P-value=0.011), but there was no significant difference between the two groups after adjusting gestational age. CONCLUSION: The clinical characteristics in IVF twins at gestational age less than 34 weeks were not significantly different from those of spontaneously conceived twins except age at the day of full enteral feeding and age at the day of full oral feeding after adjusting by gestational age.
Enteral Nutrition ; Fertilization in Vitro ; Gestational Age ; Humans ; Incidence ; Infant, Newborn ; Infertility ; Intensive Care Units, Neonatal ; Maternal Age ; Medical Records ; Retinopathy of Prematurity ; Retrospective Studies ; Risk Factors ; Seizures ; Twins

Objective: Despite a high prevalence of dementia in older adults hospitalized with severe acute respiratory syndrome coronavirus 2 infection (SARS-CoV-2), or so called COVID-19, research investigating association between preexisting diagnoses of dementia and prognosis of COVID-19 is scarce. We aimed to investigate treatment outcome of patients with dementia after COVID-19. Methods: We explored a nationwide cohort with a total of 2,800 subjects older than 50 years who were diagnosed with COVID-19 between January and April 2020. Among them, 223 patients had underlying dementia (dementia group). We matched 1:1 for each dementia- non-dementia group pair yielding 223 patients without dementia (no dementia group) using propensity score matching. Results: Mortality rate after COVID-19 was higher in dementia group than in no dementia group (33.6% vs. 20.2%, p=0.002). Dementia group had higher proportion of patients requiring invasive ventilatory support than no dementia group (34.1% vs. 22.0%, p=0.006). Multivariable analysis showed that dementia group had a higher risk of mortality than no dementia group (odds ratio=3.05, p<0.001). We also found that patients in dementia group had a higher risk of needing invasive ventilatory support than those in no dementia group. Conclusion Our results suggest that system including strengthen quarantines are required for patients with dementia during the COVID- 19 pandemic.

Objective: Despite a high prevalence of dementia in older adults hospitalized with severe acute respiratory syndrome coronavirus 2 infection (SARS-CoV-2), or so called COVID-19, research investigating association between preexisting diagnoses of dementia and prognosis of COVID-19 is scarce. We aimed to investigate treatment outcome of patients with dementia after COVID-19. Methods: We explored a nationwide cohort with a total of 2,800 subjects older than 50 years who were diagnosed with COVID-19 between January and April 2020. Among them, 223 patients had underlying dementia (dementia group). We matched 1:1 for each dementia- non-dementia group pair yielding 223 patients without dementia (no dementia group) using propensity score matching. Results: Mortality rate after COVID-19 was higher in dementia group than in no dementia group (33.6% vs. 20.2%, p=0.002). Dementia group had higher proportion of patients requiring invasive ventilatory support than no dementia group (34.1% vs. 22.0%, p=0.006). Multivariable analysis showed that dementia group had a higher risk of mortality than no dementia group (odds ratio=3.05, p<0.001). We also found that patients in dementia group had a higher risk of needing invasive ventilatory support than those in no dementia group. Conclusion Our results suggest that system including strengthen quarantines are required for patients with dementia during the COVID- 19 pandemic.