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.

Background: The aim of this study was to determine if it was feasible and safe to perform total hip arthroplasty (THA) using the direct anterior approach (DAA) when compared with the conventional posterolateral approach (PA) in patients with femoral neck fractures. The time required to start walking was investigated to identify advantages of the muscle-sparing approach. Safety of the approach was judged based on the incidence and nature of all complications. Methods: We retrospectively reviewed 67 THA cases due to femoral neck fractures from October 2015 to January 2019. The PA was used in 31 cases, and the DAA was used in 36 cases. The average operative time and amount of bleeding were evaluated. Cup inclination, anteversion, and leg length discrepancy (LLD) were also measured on radiographs. The time to start walking and complications (e.g., intraoperative fracture, infection, and dislocation) were recorded. Results: The mean operative time was 84.35 ± 13.95 minutes in PA group and 99.22 ± 20.33 minutes in DAA group (p = 0.010). But after experiencing 20 cases using the DAA, there was no statistically significant difference in the operative time between the groups. The mean volume of bleeding was 428.73 ± 207.26 mL in the PA group and 482.47 ± 150.14 mL in the DAA group. There was no difference in the acetabular cup position between two groups. Ambulation was started at 3.94 days after surgery on average in the PA group and 3.14 days in the DAA group, showing a statistically significant difference. Intraoperative fracture and infection were not observed in either group. The incidence of LLD was 1 in each group. The dislocation rate was 3.2% (1 case) in the PA group and 5.5% (2 cases) in the DAA group. Conclusions Although the DAA for THA was similar to the PA in terms of operative time, volume of bleeding, and complications, the DAA showed a great advantage in early rehabilitation as a muscle-sparing procedure in the elderly with femoral neck fractures.

The ferret is an established animal model of influenza virus infection. Although viral replication in the upper respiratory tract is usually measured with consecutively collected nasal washes, daily evaluation of viral replication in the lung is limited because a large numbers of ferrets need to be sacrificed at consecutive time points. To overcome this limitation, we performed a virus quantification assay using bronchoalveolar lavage (BAL) fluid. This non-invasive BAL technique allows consecutive quantification of virus replication in the lungs of living ferrets. Our method can be used for the longitudinal evaluation of virus tropism in the lower respiratory tract.
Animals ; Bronchoalveolar Lavage/*veterinary ; Disease Models, Animal ; Female ; Ferrets/*virology ; Influenza A Virus, H3N2 Subtype/*physiology ; Orthomyxoviridae Infections/*veterinary/virology ; Respiratory System/*virology ; Virus Replication/*physiology

Thienopyridines are antiplatelet agents used in post-percutaneous coronary angioplasty patients and patients with acute coronary syndrome, stroke, and peripheral arterial disease. Ticlopidine has been shown to reduce the incidence of stent thrombosis, but it may also cause serious hematological side effects. Among the thienopyridines, clopidogrel is considered to be a safe alternative to ticlopidine because of its decreased incidence of hematological adverse effects. However, some hematological side effects can occur and may be fatal. In this case, a 47-year-old man complained of dyspnea and generalized edema. He had been taking clopidogrel after coronary angioplasty. His laboratory findings showed acute renal failure, microangiopathic hemolytic anemia, and thrombocytopenia, which were consistent with hemolytic uremic syndrome (HUS). After discontinuing clopidogrel and undergoing plasma exchange, he recovered fully. To our knowledge, this is the first reported case of clopidogrel-induced HUS in Korea.
Acute Coronary Syndrome ; Acute Kidney Injury ; Anemia, Hemolytic ; Angioplasty ; Dyspnea ; Edema ; Hemolytic-Uremic Syndrome ; Humans ; Incidence ; Korea ; Middle Aged ; Peripheral Arterial Disease ; Plasma Exchange ; Platelet Aggregation Inhibitors ; Stents ; Stroke ; Thienopyridines ; Thrombocytopenia ; Thrombosis ; Ticlopidine

The World Health Organization (WHO) announced the emergence of a novel influenza on April 24, 2009, and they declared pandemic on June 11. In Korea, the proportion of influenza-like illness and the consumption of antiviral agents peaked in early November. The government established the Central Headquarters for Influenza Control and operated the emergency response system. In the quarantine stations, we checked the body temperature and collected quarantine questionnaires from all the arrivals from infected countries. We also isolated the confirmed cases in the national isolation hospitals. However, as the community outbreaks were reported, we changed strategy from containment to mitigation. We changed the antiviral agent prescription guideline so that doctors could prescribe antiviral agents to all patients with acute febrile respiratory illness, without a laboratory diagnosis. Also the 470 designated hospitals were activated to enhance the efficacy of treatment. We vaccinated about 12 million people and manage the adverse event following the immunization management system. In 2010, we will establish additional national isolation wards and support hospitals to establish fever clinics and isolation intensive care unit (ICU) beds. We will also make a computer program for managing the national isolation hospitals and designated hospitals. We will establish isolation rooms and expand the laboratory in quarantine stations and we will construct a bio-safety level 3 laboratory in each province. In addition, we plan to construct a bio-safety level 4 laboratory at a new Korea Centers for Disease Control and Prevention (KCDC) facilities in Ossong.
Antiviral Agents/therapeutic use ; Disaster Planning/*organization & administration ; *Disease Outbreaks ; Humans ; *Influenza A Virus, H1N1 Subtype ; Influenza, Human/drug therapy/*epidemiology ; Quarantine/organization & administration ; Republic of Korea/epidemiology ; World Health Organization

The purpose of this study is to quantitate regional neurochemical profile of regional normal adult mice brain and assess regional metabolic differences by using ex vivo 1H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy (1H HR-MAS NMRS). The animals were matched in sex and age. The collected brain tissue included frontal cortex, temporal cortex, thalamus, and hippocampus. Quantitative 1D spectra were acquired on 40 samples with the CPMG pulse sequence (8 kHz spectral window, TR/TE = 5500/2.2 ms, NEX = 128, scan time: 17 min 20 sec). The mass of brain tissue and D2O+TSP solvent were 8~14 mg and 7~13 mg. A total of 16 metabolites were quantified as follow: Acet, NAA, NAAG, tCr, Cr, tCho, Cho, GPC + PC, mIns, Lac, GABA, Glu, Gln, Tau and Ala. As a results, Acet, Cho, NAA, NAAG and mIns were showed significantly different aspects on frontal cortex, hippocampus, temporal cortex and thalamus respectively. The present study demonstrated that absolute metabolite concentrations were significantly different among four brain regions of adult mice. Our finding might be helpful to investigate brain metabolism of neuro-disease in animal model.
Adult ; Animals ; Brain ; gamma-Aminobutyric Acid ; Hippocampus ; Humans ; Magic ; Magnetic Resonance Spectroscopy ; Mice ; Models, Animal ; Spectrum Analysis ; Thalamus