Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: This study aimed to investigate the prevalence, clinical characteristics, and the correlates of nonsuicidal self-injury (NSSI) in firefighters. We also investigated the mediating role of NSSI frequency in the association between posttraumatic stress disorder (PTSD), depression, and suicidal behavior. Methods: A total of 51,505 Korean firefighters completed a web-based self-reported survey, including demographic and occupational characteristics, NSSI, PTSD, depression, and suicidal behavior. Multivariable logistic regression analyses and serial mediation analyses were performed. Results: The 1-year prevalence of NSSI was 4.67% in Korean firefighters. Female gender, the presence of recent traumatic experience, and PTSD and depression symptoms were correlated with NSSI. Serial mediation analyses revealed that NSSI frequency mediated the association between PTSD, depression, and suicidal behavior; it indicates more severe PTSD was sequentially associated with more severe depression symptoms and more frequent NSSI, leading to higher risk of suicidal behavior. Conclusion NSSI is prevalent and may play a significant mediating role when PTSD is associated with suicidal behavior in firefighters. Our results imply the need for screening and early intervention of NSSI in firefighters.

Objective: We aimed to investigate the annual prevalence of intellectual disabilities (ID) and autism spectrum disorder employing claims data registered in Korean National Health Insurance Service. We also estimated the annual average of medical costs incurred from these disorders using same datasets. Methods: In order to obtain the prevalence, we selected patients diagnosed with ID and pervasive and specific developmental disorders (PDD) from 2007 to 2019. The ensuing annual average of medical costs was also estimated from these patients. Results: The annual prevalence of ID and PDD (per 100,000) between 2007 and 2019 ranged from 56.7 to 78.6 and from 22.0 to 44.6 respectively. Regarding the annual average of total medical expenditure per a patient, the expenditure of the ID group was higher than that of PDD throughout the years, as shown that the ID expenditure ranged from 769.7 to 1,501.2 US dollars as opposed to the PDD expenditure in the range of 312.5 to 570.7 US dollars. The further comparison in relation to income levels elaborated that the medical aid beneficiary group constitutes the highest one and the difference of the expenditure across the remaining income groups was not prominent although the very low group generally ranked the highest over the years. Conclusion The prevalence of ID and PDD constantly increased and the same trend was displayed in ensuing health expenditures throughout the period. This implies that increasing needs exist across these patients with regards to therapeutic interventions, thereby contributing to prioritizing medical policies on national perspectives.

Objective: The correlation among the thyroid-related hormones, Apolipoprotein E ε4 (APOE ε4) and cognitive function has been reported despite controversial results. This study was designed to investigate this correlation among cognitively-normal elderly dwellers. Methods: This study assessed 507 cognitively normal individuals aged over 60 who underwent comprehensive hematological and neuropsychological assessments including the quantification of serum free thyroxine and thyroid stimulating hormone (TSH) as well as the Korean version of the Consortium Establish a Registry for Alzheimer’s disease. The Korean version of Geriatric Depression Scale was also employed to evaluate the severity of depression. Age, gender, education, and the presence of APOE ε4 were taken into account as covariates. Results: There was a significant positive association between verbal fluency test (VFT), Word List Memory Test (WLMT), and Word List Recall Test (WLRT) score and serum TSH levels (p=0.007, 0.031, and 0.023 respectively). The further analysis adding the interaction between APOE ε4 and TSH level, however, revealed only VFT score was significantly influenced by this interaction (p=0.026). Conclusion Lower serum TSH levels had impacts on both semantic memory (VFT) and episodic memory (WLMT, WLRT) among cognitively-normal elderly, whereas the interaction of TSH and APOE ε4 influenced only the task of semantic memory (VFT) in this group.

A bibliometric study is performed to analyze publication patterns in a specific research area and to establish a landscape model that can be used to quantitatively weigh publications. This study aimed to investigate AML research networks and to conduct a trend-related keyword analysis. We analyzed 48,202 studies about AML published from 1999 to 2019 in the Web of Science Core Collection. The network analysis was conducted using the R&R studio software. The journal Blood had the highest number of published articles with an h-index of 410. The USA had the highest number of total publications (18,719, 38.3%) and research funded by the government, institutions, and pharmaceutical companies (5,436, 10.8%). The institute with the largest number of publications was the MD Anderson Cancer Center. Kantarjian H, Garcia-Manero G, and Ravandi F were the leading authors of publications about AML. Keyword analysis revealed that FLT 3, micro-RNA, and NK cell topics were the hotspots in the cell and gene area in all publications. The overall AML research landscape is popular in the field of translational research as it can identify molecular, cell, and gene studies conducted by different funding agencies, countries, institutions, and author networks. With active funding and support from the Chinese government, the productivity of scientific research is increasing not only in the AML field but also in the medical/health-related science field.

This study was performed to investigate the effect of a concentrate of fermented wild ginseng root culture (HLJG0701) on memory improvement in the scopolamine (SPL)-induced memory-deficient mouse model. Eight-week-old male ICR mice were used to evaluate the protective effect of HLJG0701 against the SPL-induced memory loss animal model. The Morris water maze test, which measures hippocampus-dependent learning ability, and the Y-maze test, a short-term memory assessment test, were performed and related markers were analyzed. HLJG0701-treated groups displayed significantly reduced acetylcholinesterase activity and increased acetylcholine level compared with the SPL-administered group (SPL-G) (P < 0.05). In the Y-maze test, the spontaneous alternation in al HLJG0711-treated groups was significantly increased compared with that in SPL-G (P < 0.05). In the Morris water maze test, the escape latency and time spent in the target quadrant in all HLJG0701-treated groups were significantly decreased and increased, respectively, compared with those in SPL-G (P < 0.05). In addition, the brain-derived neurotrophic factor level in groups treated with HLJG0701 300 and 600 mg/kg body weight was significantly increased compared with that in SPL-G (P < 0.05). These results suggest that the HLJG0701 may protect against memory loss by inhibiting acetylcholinesterase activity and preventing acetylcholine deficiency.
Acetylcholine ; Acetylcholinesterase ; Animals ; Body Weight ; Brain-Derived Neurotrophic Factor ; Ginsenosides ; Humans ; Learning ; Male ; Memory Disorders* ; Memory* ; Memory, Short-Term ; Mice* ; Mice, Inbred ICR ; Models, Animal ; Panax* ; Scopolamine Hydrobromide ; United Nations ; Water