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.

Viral load and the duration of viral shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are important determinants of the transmission of coronavirus disease 2019.In this study, we examined the effects of viral doses on the lung and spleen of K18-hACE2 transgenic mice by temporal histological and transcriptional analyses. Approximately, 1×105 plaque-forming units (PFU) of SARS-CoV-2 induced strong host responses in the lungs from 2 days post inoculation (dpi) which did not recover until the mice died, whereas responses to the virus were obvious at 5 days, recovering to the basal state by 14 dpi at 1×102 PFU. Further, flow cytometry showed that number of CD8+ T cells continuously increased in 1×102 PFU-virusinfected lungs from 2 dpi, but not in 1×105 PFU-virus-infected lungs. In spleens, responses to the virus were prominent from 2 dpi, and number of B cells was significantly decreased at 1×105PFU; however, 1×102 PFU of virus induced very weak responses from 2 dpi which recovered by 10 dpi. Although the defense responses returned to normal and the mice survived, lung histology showed evidence of fibrosis, suggesting sequelae of SARS-CoV-2 infection. Our findings indicate that specific effectors of the immune response in the lung and spleen were either increased or depleted in response to doses of SARS-CoV-2. This study demonstrated that the response of local and systemic immune effectors to a viral infection varies with viral dose, which either exacerbates the severity of the infection or accelerates its elimination.

Background: Developmental trajectories of clinical skills in training physicians vary among tasks and show interindividual differences. This study examined the predictors of medical internship performance and residency entrance and found subtypes of performance trajectory in training physicians. Methods: This retrospective cohort study involved 888 training physicians who completed a medical internship between 2015 and 2019. After the internship, 627 physicians applied for residency training between 2016 and 2020. Finally, 160 of them completed their first-year residency in internal medicine, surgery, pediatrics, and psychiatry departments between 2016 and 2020. Pearson’s correlation coefficients of internship performance and first year-residency performance (n = 160) were calculated. Latent profile analysis identified performance trajectory subtypes according to medical school grade point average (GPA), internship performance, English proficiency, and residency selection procedures. Multivariate logistic regression models of residency acceptance (n = 627) and performance in the top 30%/lower 10% in the first year of residency were also constructed. Results: Medical internship performance showed a significant positive correlation with the medical school GPA (r = 0.194) and the written score for the medical licensing examination (r = 0.125). Higher scores in the interview (adjusted odds ratio [aOR], 2.57) and written examination (aOR, 1.45) of residency selection procedures and higher medical internship performance (aOR, 1.19) were associated with a higher chance of residency acceptance. The latent profile analyses identified three training physician subgroups: average performance, consistently high performance (top 30%), and adaptation to changes (lowest 10%). Higher scores in the interview for residency selection (aOR, 1.35) and lower scores for medical internship performance (aOR, 0.79) were associated with a higher chance of performing in the top 30% or lowest 10% in the first year of residency, respectively. Conclusion Performance in the interview and medical internship predicted being among the top 30% and lowest 10% of performers in the first year of residency training, respectively.Individualized educational programs to enhance the prospect of trainees becoming highfunctioning physicians are needed.

Background: Enlarged perivascular space (ePVS) is recently reported to be associated with cerebral small vessel disease (SVD) and Alzheimer’s disease (AD). The topographical location of ePVS may relate to the underlying pathology; basal ganglia (BG)-ePVS has been associated with cerebral vascular diseases and centrum semi-ovale (CSO)-ePVS associated with cerebral amyloid angiopathy (CAA). However, the effects of ePVS on various neurological conditions remain still controversial. To investigate the clinical relevance of ePVS in neurodegenerative diseases, we tested relationships between ePVS and cognition, markers of SVD, vascular risk factors, or amyloid pathology. Methods: We retrospectively reviewed 292 patients (133 AD dementia, 106 mild cognitive impairment, 39 other neurodegenerative diseases, 14 subjective cognitive decline) who underwent both amyloid positron emission tomography and brain magnetic resonance imaging. Vascular risk factors and cognitive tests results were collected. The ePVS in the BG and CSO, SVD markers and the volume of white matter hyperintensities were measured. Results: There were no significant differences in the severity and distribution of ePVS among clinical syndromes. Both BG- and CSO-ePVS were not related to cognitive function. Patients with lacunes were more likely to have high-degree BG-ePVS. High degree CSO-ePVS had an odds ratio (OR) for amyloid positive of 2.351, while BG-ePVS was a negative predictor for amyloid pathology (OR, 0.336). Conclusions Our findings support that ePVS has different underlying pathologies according to the cerebral topography. BG-ePVS would be attributed to hypertensive angiopathy considering the relation with SVD markers, whereas and CSO-ePVS would be attributed to CAA considering the association with amyloid pathology.

Background: As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. Results: In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. Conclusions This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.

Background: In diffuse large B-cell lymphoma (DLBCL), bone marrow involvement (BMI) has an important clinical implication as a component of staging and International Prognostic Index. This study aimed to determine whether molecular analysis of immunoglobulin heavy chain (IgH) genes and positron emission tomography-computed tomography (PET/CT) could overcome the limitation of defining morphologic BMI by trephination biopsy and could increase the diagnostic accuracy or prognostic prediction. Methods: A total of 94 de novo patients with DLBCL underwent PET/CT, polymerase chain reaction (PCR) test for detection of IgH gene rearrangement, and unilateral bone marrow (BM) trephination at diagnosis. Results: A total of 9 patients (9.6%) were confirmed to present morphologic BMI (mBMI) based on trephination biopsy. On the other hand, 21 patients (22.3%) were confirmed to have IgH clonality (IgH BMI), while 16 (17.0%) were classified with BMI based on the assessment of PET/CT (PET BMI). Each IgH rearrangement PCR and PET/CT showed the high negative predictive value of detecting the BMI. However, the combined assessment of IgH rearrangement and PET/CT could increase the diagnostic accuracy and specificity with 87.2% and 97.0%, respectively. The survival outcome of patients with double positive PET BMI and IgH BMI was significantly worse than that with either single positive PET BMI or IgH BMI, and even less than patients with neither PET BMI nor IgH BMI (3-year PFS: 50.0% vs. 75.4% vs. 97.9%, P = 0.007, 3-year OS: 50.0% vs. 75.6% vs. 80.1%, P = 0.035, respectively). Conclusion This study suggests that the combined evaluation of PET/CT and IgH rearrangement could give additional information for predicting therapeutic outcomes in patients with negative morphologic BMI as an important part of the prognosis.