In this study, almost complete genomic sequences of PPV1 and PPV4 circulating in commercial pig farms in South Korea were obtained and analyzed. Important mutations that may be precursors to host changes, such as premature stop codons of PPV1 and frameshift mutations of PPV4, were observed in these sequences. A 27a-like strain of PPV1, known to show a lack of cross-neutralization against existing commercial vaccine strains, was identified by phylogenetic analysis. Given the active genetic evolution, the additional precursors to host changes and emerging new genotypes of PPVs need to be monitored through continuous sampling and genetic analysis.

BACKGROUND: We analyzed the long-term results of ascending aortic replacement and arch aortic replacement in acute DeBakey type I aortic dissections to measure the differences in the distal aortic changes with extension of the aortic replacement. METHODS: We reviewed 142 cases of acute DeBakey type I aortic dissections (1996–2015). Seventy percent of the cases were ascending aortic replacements, and 30% of the cases underwent total arch aortic replacement, which includes the aorta from the root to the beginning of the descending aorta with the 3 arch branches. Fourteen percent (20 cases) resulted in surgical mortality and 86% of cases that survived had a mean follow-up period of 6.6±4.6 years. Among these cases, 64% of the patients were followed up with computed tomography (CT) angiograms with the duration of the final CT check period of 4.9±2.9 years. RESULTS: There were 15 cases of reoperation in 13 patients. Of these 15 cases, 13 cases were in the ascending aortic replacement group and 2 cases were in the total arch aortic replacement group. Late mortality occurred in 13 cases; 10 cases were in the ascending aortic replacement group and 3 cases were in the total arch aortic replacement group. Eight patients died of a distal aortic problem in the ascending aortic replacement group, and 1 patient died of distal aortic rupture in the total arch aortic replacement group. The follow-up CT angiogram showed that 69.8% of the ascending aortic replacement group and 35.7% of the total arch aortic replacement group developed distal aortic dilatation (p=0.0022). CONCLUSION: The total arch aortic replacement procedure developed fewer distal remnant aortic problems from dilatation than the ascending aortic replacement procedure in acute type I aortic dissections.
Aorta* ; Aorta, Thoracic* ; Aortic Rupture ; Dilatation ; Follow-Up Studies ; Humans ; Mortality ; Recurrence ; Reoperation

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: CycloZ, a combination of cyclo-His-Pro and zinc, has anti-diabetic activity. However, its exact mode of action remains to be elucidated. Methods: KK-Ay mice, a type 2 diabetes mellitus (T2DM) model, were administered CycloZ either as a preventive intervention, or as a therapy. Glycemic control was evaluated using the oral glucose tolerance test (OGTT), and glycosylated hemoglobin (HbA1c) levels. Liver and visceral adipose tissues (VATs) were used for histological evaluation, gene expression analysis, and protein expression analysis. Results: CycloZ administration improved glycemic control in KK-Ay mice in both prophylactic and therapeutic studies. Lysine acetylation of peroxisome proliferator-activated receptor gamma coactivator 1-alpha, liver kinase B1, and nuclear factor-κB p65 was decreased in the liver and VATs in CycloZ-treated mice. In addition, CycloZ treatment improved mitochondrial function, lipid oxidation, and inflammation in the liver and VATs of mice. CycloZ treatment also increased the level of β-nicotinamide adenine dinucleotide (NAD+), which affected the activity of deacetylases, such as sirtuin 1 (Sirt1). Conclusion Our findings suggest that the beneficial effects of CycloZ on diabetes and obesity occur through increased NAD+ synthesis, which modulates Sirt1 deacetylase activity in the liver and VATs. Given that the mode of action of an NAD+ booster or Sirt1 deacetylase activator is different from that of traditional T2DM drugs, CycloZ would be considered a novel therapeutic option for the treatment of T2DM.