Background: Collaborative robots have emerged as a solution for complex and precision-driven tasks. However, there is no comprehensive risk assessment model for collaborative robotic work in the Republic of Korea. Thus, we aim to develop a risk assessment model tailored to the unique characteristics of human-robot collaboration. Methods: In this study, we examine the risk assessment factors in three key fields: worker, robotic systems, and work environment. The items adopted in the existing models of HFACS, USUS, and human–robot trust were initially selected to extract items for the risk assessment model. The analytic hierarchy process methodology was applied to refine and prioritize the evaluation items. Score scales were constructed for the six checklists. A five-level score ranging from one to five was given to each question in the checklist, and the average score was used to quantitatively evaluate each area. Results: The six evaluation checklists coalesced to constitute a comprehensive risk assessment model was adjusted for collaborative robot operations. Implementing the proposed model in six robot-operated workplaces yielded consistent results. Companies involved in previous robot-related accidents exhibited deficiencies in these risks than accident-free robot workplaces. Thus, comprehensive risk assessments encompassing the necessary factors are crucial to prevent accidents in robot-related work environments. Conclusion The proposed risk assessment model can offer a robust foundation for guiding the future development of diverse risk assessment models for collaborative robot environments and facilitate the safe coexistence of humans and robots in the Industry 4.0 era.

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.