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.

Humans ; Adolescent ; Pandemics ; Substance-Related Disorders/epidemiology* ; Smoking ; Ethanol ; Republic of Korea/epidemiology* ; Alcohol Drinking/epidemiology*

AIM:To investigate the factors affecting patients with post traumatic infectious endophthalmitis(PTIE)relieving from blindness.METHODS: A retrospective study was conducted on 169 patients(169 eyes)with PTIE from January 2010 to December 2020 in the department of ophthalmology, the Affiliated Hospital of Nantong University. After treatment of intravitreal injection of antibiotics(IVIA)and/or pars plana vitrectomy(PPV), the patients were divided into the getting rid of blindness group(103 eyes)and unilateral blindness group(66 eyes)according to the last follow-up of best correct visual acuity(BCVA)≥0.05. The factors affecting the patients to get rid of blindness were analyzed.RESULTS: The rate of relieving from blindness was 53.5%. Univariate analysis showed that BCVA before treatment ≥ hand movement, no retinal detachment, fundus grade of endophthalmitis < grade 3 and no strong virulence of infected microorganisms were beneficial for patients to get rid of blindness(P<0.05). Multivariate Logistic regression analysis identified that BCVA before treatment ≥ hand movement(OR=0.253, 95%CI: 0.108-0.592)and no retinal detachment(OR=0.241, 95%CI: 0.103-0.564)were favorable factors for patient to get rid of blindness.CONCLUSION: Better BCVA before treatment, no retinal detachment, endophthalmitis fundus grade < 3, and no strong virulence of infected microorganisms are favorable factors for patients with PTIE to get rid of blindness finally.

Purpose: The aim of this study was to estimate the effect of targeting risk factors for the control of central line-associated bloodstream infection (CLABSI) among high-risk infants in a tertiary neonatal intensive care unit (NICU). Methods: Infants admitted to the NICU and diagnosed with CLABSI from January to December 2013 were eligible for inclusion to the study. The CLABSI group (n=47) was matched in a 1:2 ratio to the control group (n=94) based on gestational age, birth weight, and Score for Neonatal Acute Physiology-II. Risk factors for CLABSI were identified using the Cox proportional hazard model, and analysis of the effect of these risk factors targeting infection control was performed. Results: The risk factors associated with CLABSI were prolonged central line dwell days (adjusted hazard ratio [HR], 1.028; 95% confidence interval [CI], 1.011 to 1.045; P=0.001), use of a silicone catheter (adjusted HR, 5.895; 95% CI, 1.893 to 18.355; P=0.002), surgical treatment (adjusted HR, 3.793; 95% CI, 1.467 to 9.805; P=0.006), and less probiotic supplementation (adjusted HR, 0.254; 95% CI, 0.068 to 0.949; P=0.042). By targeting these risk factors with a quality improvement initiative, the mean CLABSI incidence rate per 1,000 catheter-days decreased from 6.6 to 3.1 (P=0.004). Conclusion Targeting risk factors for infection control significantly reduced the rate of CLABSI among high-risk infants in the NICU.

Purpose: The aim of this study was to estimate the effect of targeting risk factors for the control of central line-associated bloodstream infection (CLABSI) among high-risk infants in a tertiary neonatal intensive care unit (NICU). Methods: Infants admitted to the NICU and diagnosed with CLABSI from January to December 2013 were eligible for inclusion to the study. The CLABSI group (n=47) was matched in a 1:2 ratio to the control group (n=94) based on gestational age, birth weight, and Score for Neonatal Acute Physiology-II. Risk factors for CLABSI were identified using the Cox proportional hazard model, and analysis of the effect of these risk factors targeting infection control was performed. Results: The risk factors associated with CLABSI were prolonged central line dwell days (adjusted hazard ratio [HR], 1.028; 95% confidence interval [CI], 1.011 to 1.045; P=0.001), use of a silicone catheter (adjusted HR, 5.895; 95% CI, 1.893 to 18.355; P=0.002), surgical treatment (adjusted HR, 3.793; 95% CI, 1.467 to 9.805; P=0.006), and less probiotic supplementation (adjusted HR, 0.254; 95% CI, 0.068 to 0.949; P=0.042). By targeting these risk factors with a quality improvement initiative, the mean CLABSI incidence rate per 1,000 catheter-days decreased from 6.6 to 3.1 (P=0.004). Conclusion Targeting risk factors for infection control significantly reduced the rate of CLABSI among high-risk infants in the NICU.