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.

Introduction: The awareness of under-representation of female surgeons in orthopaedics has been increasing in this decade. We aim to investigate the reasons why female surgeons chose orthopaedic, the barriers that possibly hinder female surgeons into orthopaedics and analyse the obstacles that they encountered in their career in Malaysian context. Materials and methods: A total of 101 registered female orthopaedic surgeons registered with the Malaysian Medical Council, during the period 1980 to 2020, were contacted for a cross-sectional survey, consisting of thirty-four questions on their experience in the orthopaedic career. Eighty-two responses were received (81.2%). Questions in this survey consisted of four sections: (1) demographic details, (2) current clinical practice environment, (3) orthopaedics training experience, and (4) career experience. Results: A total of 49% of respondents had subspeciality training, highest in paediatric orthopaedic (30%). Enjoyment of manual tasks (64.6%) and professional satisfaction (64.6%) were the top reasons for choosing orthopaedic as a career. Primary barriers to orthopaedic were physical strength required (56.0%) and public gender bias (52.4%). Twenty-eight percent reported gender discrimination in career opportunities while 60% reported similar in daily work. Thirty-three percent reported verbal and 11% physical sexual harassment in their career. Forty-four percent of respondents reported benefits as female orthopaedic surgeon in their work. Conclusion: The reasons for Malaysian female orthopaedic surgeons to choose orthopaedic as their career and the barriers perceived to hinder other females from choosing orthopaedics were similar to reports worldwide with no exception to gender discrimination and sexual harassment. The support given by male colleagues to married female surgeons marked a unique phenomenon in Malaysian culture.

Amino Acid Sequence ; Animals ; Cell Culture Techniques ; Cell Line ; Chickens ; Eggs ; Erythrocytes ; Guinea Pigs ; Hand ; Hemagglutination ; Hemagglutinins ; Humans ; Influenza Vaccines ; Influenza, Human ; Neuraminidase ; Orthomyxoviridae ; Ovum ; Sequence Analysis ; Specific Pathogen-Free Organisms

Several gut commensals have been shown to modulate host immune response. Recently, many food derived microbes have also been reported to affect the immune system. However, a mechanism to identify immunostimulatory and immunoregulatory microbes is needed. Here, we successfully established an in vitro screening system and identified an immunoregulatory bacterium, Lactobacillus pentosus KF340 (LP340), present in various fermented foods. LP340 induced a regulatory phenotype in mice Ag presenting cells which, in turn, induced IL-10 and IFN-γ producing Type 1 regulatory T cells (Tr1 cells) from naïve CD4⁺ T cells. Naïve CD4⁺ T cells co-cultured with LP340 treated dendritic cells highly expressed cytokine receptor IL-27R and were CD49b and lymphocyte-activation gene 3 double positive. Oral administration of LP340 in mice with atopic dermatitis reduced cellular infiltration in affected ear lobes and serum IgE levels, thus, ameliorating the disease symptoms. This suggests a systemic immunoregulatory effect of LP340. These findings demonstrate that LP340, a bacterium derived from food, prevents systemic inflammation through the induction of IL-10 producing Tr1 cells.
Administration, Oral ; Animals ; Dendritic Cells ; Dermatitis, Atopic ; Ear ; Immune System ; Immunoglobulin E ; In Vitro Techniques ; Inflammation ; Interleukin-10 ; Lactobacillus ; Mass Screening ; Mice ; Phenotype ; Receptors, Cytokine ; T-Lymphocytes ; T-Lymphocytes, Regulatory

A 73-year-old Chinese man was admitted to the Accident and Emergency Premorbid Ward of a local hospital in Malaysia. The patient complained of shortness of breath with cough and was in a semi-conscious state. He was later admitted to an intensive care unit (ICU) of the hospital. Six days after admission 5-6 maggots were recoverd from the nasal cavity. The maggots were identifi ed as the third-instar larvae of Lucilia cuprina Wiedmann (Diptera: Calliphoridae) based on the morphological characteristics. This patient was classifi ed as having nosocomial myiasis. The presence of the third instar larvae indicated that the infestation was not more than three to four days. An adult sarcophagid identifi ed as Parasarcophaga rufi cornis (Fabricius) caught in the ICU where the patient was warded provided further evidence of the potential for the nosocomial infestation.

PURPOSE: Previously positive but currently negative HLA crossmatching is considered to be a risk factor not in the first renal transplantation but in the second renal transplantation. The aim of this study is to analyse the outcome of living-related renal transplantation with previously positive but currently negative HLA crossmatching. METHODS: The results of first HLA crossmatching, demographic characteristics, the outcome of renal trasplantation were examined in four patients undergoing renal transplantation with previously positive but currently negative HLA crossmatching. RESULTS: The acute rejection was occurred in 3 patients. Azotemia was improved with the immunosuppressive therapy containing tacrolimus. There were no graft failures in four patients for 1 year. In the first HLA crossmatching, anti-human globulin T cell HLA crossmatching was positive in all patients with acute rejection. The period that positive HLA crossmatchings were converted to negative was longer in patients with acute rejections than without acute rejections (177 days vs 22 days). CONCLUSION: There were 3 acute rejections in 6 patients undergoing living related renal transplantations with previously positive and current negative HLA crossmatching. There were no graft failure for 1 year.
Azotemia ; Humans ; Kidney Transplantation* ; Risk Factors ; Tacrolimus ; Transplants