A Bayesian convolutional neural network-based generalized linear model.

Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.

Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer.

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. In this study, multivariable datasets were collected from 751 college students who could be easily affected by pandemics based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g. decision-making in uncertain situations) were more important in post-pandemic conditions. Although the results should be validated using other mental health variables or national datasets, this study will serve as a reference for researchers regarding the use of the quantum annealing approach in factor analysis with validation through real-world survey dataset analysis.

Neurologic Effects of SARS-CoV-2 Transmitted among Dogs.

SARS-CoV-2 induces illness and death in humans by causing systemic infections. Evidence suggests that SARS-CoV-2 can induce brain pathology in humans and other hosts. In this study, we used a canine transmission model to examine histopathologic changes in the brains of dogs infected with SARS-CoV-2. We observed substantial brain pathology in SARS-CoV-2-infected dogs, particularly involving blood-brain barrier damage resembling small vessel disease, including changes in tight junction proteins, reduced laminin levels, and decreased pericyte coverage. Furthermore, we detected phosphorylated tau, a marker of neurodegenerative disease, indicating a potential link between SARS-CoV-2-associated small vessel disease and neurodegeneration. Our findings of degenerative changes in the dog brain during SARS-CoV-2 infection emphasize the potential for transmission to other hosts and induction of similar signs and symptoms. The dynamic brain changes in dogs highlight that even asymptomatic individuals infected with SARS-CoV-2 may develop neuropathologic changes in the brain.

Investigation of factors associated with mental health during the early part of the COVID-19 pandemic in South Korea based on machine learning algorithms: A cohort study.

Objective: The coronavirus disease 2019 (COVID-19) pandemic is among the most critical public health problems worldwide in the last three years. We tried to investigate changes in factors between pre- and early stages of the COVID-19 pandemic. Methods: The data of 457,309 participants from the 2019 and 2020 Community Health Survey were examined. Four mental health-related variables were selected for examination as a dependent variable (patient health questionnaire-9, depression, stress, and sleep time). Other variables without the aforementioned four variables were split into three groups based on the coefficient values of lasso and ridge regression models. The importance of each variable was calculated and compared using feature importance values obtained from three machine learning algorithms. Results: Psychiatric and sociodemographic variables were identified, both during the pre- and early pandemic periods. In contrast, during the early pandemic period, average sleep time variables ranked the highest with the dependent variables regarding the experience of depression. The difference in sleep time before and after the pandemic was validated by the results of paired t-tests, which were statistically significant (p-value < 0.05). Conclusions: Changes in the importance of mental health factors in the early pandemic period in South Korea were identified. For each mental health-dependent variable, average sleep time, experience of depression, and experience of accidents or addictions were found to be the most important factors. House type and type of residence were also found in regions with larger populations and a higher number of confirmed cases.

Effect of Cucumis melo L. peel extract supplemented postbiotics on reprograming gut microbiota and sarcopenia in hindlimb-immobilized mice.

Postbiotics made from lactic acid bacteria may ameliorate sarcopenia via the metabolic reprogramming of gut dysbiosis. This study investigated the anti-sarcopenic effect of postbiotics (WDK) produced from polyphenol-rich melon peel extract (Cucumis melo L. var. makuwa, KEE) and whey with Lentilactobacillus kefiri DH5 (DH5) in C2C12 skeletal muscle cells and hindlimb-immobilized mice. WDK significantly ameliorated palmitate-induced atrophy of C2C12 cells, restoring myotube length and diameter. It also upregulated the expression of myogenic genes including Atrogin-1, Igf-1, and MyoD. Hindlimb-immobilized C57BL/6J mice were randomly divided and orally administered 10 mL/kg body weight of saline (CON), Whey, Whey + DH5 (WD), DH5 + KEE, Whey + DH5 + KEE postbiotic (WDK) for three weeks (n = 10/group). Interestingly, WDK significantly improved muscle function in hindlimb-immobilized mice by restoring both the grip strength and the mass of the soleus muscle, which was closely related to the upregulation of the myoD gene. WDK increased microbial diversity and modulated the distribution of intestinal bacteria, particularly those involved in protein synthesis and the production of butyrate. There was a significant correlation between myogenic biomarkers and butyrate producing gut microbiota. Restoration of muscle mass and function following postbiotic WDK is strongly related to the regulation of myogenic genes by in part remodulating gut microbiota. In conclusion, these findings suggest that polyphenol- and whey-based postbiotics WDK may have potential as an effective manner to combat the progression of sarcopenia.

Identification and comparison of pandemic-to-symptom networks of South Korea and the United States.

Background: The Coronavirus (COVID-19) pandemic resulted in a dramatic increase in the prevalence of anxiety and depression globally. Although the impact on the mental health of young adults was especially strong, its underlying mechanisms remain elusive. Materials and methods: Using a network approach, the present study investigated the putative pathways between pandemic-related factors and anxiety and depressive symptoms among young adults in South Korea and the U.S. Network analyses were conducted on cross-country data collected during the COVID-19 lockdown period (n = 1,036). Our model included depression symptoms (PHQ-9), generalized anxiety symptoms (GAD-7), and COVID-19-related factors (e.g., COVID-19-related traumatic stress, pandemic concerns, access to medical/mental health services). Results: The overall structure of pandemic-to-symptom networks of South Korea and the U.S. were found to be similar. In both countries, COVID-related stress and negative future anticipation (an anxiety symptom) were identified as bridging nodes between pandemic-related factors and psychological distress. In addition, worry-related symptoms (e.g., excessive worry, uncontrollable worry) were identified as key contributors in maintaining the overall pandemic-to-symptom network in both countries. Conclusion: The similar network structures and patterns observed in both countries imply that there may exist a stable relationship between the pandemic and internalizing symptoms above and beyond the sociocultural differences. The current findings provide new insights into the common potential pathway between the pandemic and internalizing symptoms in South Korea and in the U.S. and inform policymakers and mental health professionals of potential intervention targets to alleviate internalizing symptoms.

Experience and perspectives of end-of-life care discussion and physician orders for life-sustaining treatment of Korea (POLST-K): a cross-sectional study.

BACKGROUND: This study aimed to identify the healthcare providers' experience and perspectives toward end-of-life care decisions focusing on end-of-life discussion and physician's order of life-sustaining treatment documentation in Korea which are major parts of the Life-Sustaining Treatment Act. METHODS: A cross-sectional survey was conducted using a questionnaire developed by the authors. A total of 474 subjects-94 attending physicians, 87 resident physicians, and 293 nurses-participated in the survey, and the data analysis was performed in terms of frequency, percentage, mean and standard deviation using the SPSS 24.0 program. RESULTS: Study results showed that respondents were aware of terminal illness and physician's order of life-sustaining treatment in Korea well enough except for some details. Physicians reported uncertainty in terminal state diagnosis and disease trajectory as the most challenging. Study participants regarded factors (related to relationships and communications) on the healthcare providers' side as the major impediment to end-of-life discussion. Study respondents suggested that simplification of the process and more staff are required to facilitate end-of-life discussion and documentation. CONCLUSION: Based on the study results, adequate education and training for better end-of-life discussion are required for future practice. Also, a simple and clear procedure for completing a physician's order of life-sustaining treatment in Korea should be prepared and legal and ethical advice would be required. Since the enactment of the Life-Sustaining Treatment Act, several revisions already have been made including disease categories, thus continuous education to update and support clinicians is also called for.

Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification.

Background: Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. Methods: To compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation. Results: The combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study. Conclusion: We determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies.

Validation of depression determinants in caregivers of dementia patients with machine learning algorithms and statistical model.

Introduction: Due to its increasing prevalence, dementia is currently one of the most extensively studied health issues. Although it represents a comparatively less-addressed issue, the caregiving burden for dementia patients is likewise receiving attention. Methods: To identify determinants of depression in dementia caregivers, using Community Health Survey (CHS) data collected by the Korea Disease Control and Prevention Agency (KDCA). By setting "dementia caregiver's status of residence with patient" as a standard variable, we selected corresponding CHS data from 2011 to 2019. After refining the data, we split dementia caregiver and general population groups among the dataset (n = 15,708; common variables = 34). We then applied three machine learning algorithms: Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), and Support Vector Classifier (SVC). Subsequently, we selected XGBoost, as it exhibited superior performance to the other algorithms. On the feature importance of XGBoost, we performed a multivariate hierarchical regression analysis to validate the depression causes experienced in each group. We validated the results of the statistical model analysis by performing Welch's t-test on the main determinants exhibited within each group. Results: By verifying the results from machine learning via statistical model analysis, we found "sex" to highly impact depression in dementia caregivers, whereas "status of economic activities" is significantly associated with depression in the general population. Discussion: The evident difference in causes of depression between the two groups may serve as a basis for policy development to improve the mental health of dementia caregivers.

Genetic Analysis of Torque Teno Canis Virus Identified in Republic of Korea.

Torque teno canis virus (TTCaV) is an approximately 2.8 kb circular single-stranded DNA virus known to cause infections in dogs. However, its incidence in Republic of Korea remains unknown. In this study, 135 dog fecal samples were collected to determine TTCaV infection status in Republic of Korea. Based on polymerase chain reaction (PCR) analysis, 13 of 135 (9.6%) dogs tested positive for TTCaV. Three full-length genome sequences (GenBank IDs: MZ503910, MZ503911, and MZ503912) were obtained from the positive specimens. Phylogenetic tree construction and sequence identity, similarity plot, and recombination analyses were performed using these three full-length genomic sequences. Among the three full-length genomes, MZ503912 was determined to be a recombinant virus based on analysis with the reference TTCaV strains. This novel virus strain might have been generated by recombination between TTCaV strain KX827768 discovered in China and MZ503910 discovered in Republic of Korea. This is the first report to determine the incidence, genetic variation, and recombination of TTCaV in dogs in Republic of Korea. Further studies are needed to elucidate TTCaV pathogenesis in dogs.

Discrepancy of C-Reactive Protein, Procalcitonin and Interleukin-6 at Hospitalization: Infection in Patients with Normal C-Reactive Protein, Procalcitonin and High Interleukin-6 Values.

C-reactive protein (CRP) or procalcitonin (PCT) alone has limitations in the early detection of infection or inflammation due to shortcomings in specificity and varied cut-off values. Recently, interleukin (IL)-6 has been assessed, but it is not known to what extent the three values are homogeneous in reality. This retrospective study was conducted with two large datasets (discrepancy set with results within 24 h of admission [7149 patients] and follow-up set until 2 weeks of hospital stay [5261 tests]) consisting of simultaneous examinations of CRP, PCT, and IL-6 between January 2015 and August 2021. The specific discrepant group (n = 102, 1.4%) with normal CRP (<10 mg/L) and PCT (<0.1 ng/mL) and high IL-6 (≥100 pg/mL) values was extracted from the discrepancy set. Dimensionality reduction and visualization were performed using Python. The three markers were not clearly clustered after t-distributed stochastic neighbor embedding. Pearson's correlation coefficients between two markers were substantially low (0.23−0.55). Among the high normalized IL-6 levels (≥0.5) (n = 349), 17.8% and 38.7% of CRP and PCT levels were very low (≤0.01). 9.2% and 13.4% of normal CRP (n = 1522) had high PCT (≥0.5 ng/mL) and IL-6 (≥100 pg/mL) values, respectively. Infection and bacteremia among 102 patients occurred in 36 (35.3%) and 9 (8.8%) patients, respectively. In patients with bacteremia, IL-6 was the first to increase, followed by PCT and CRP. Our study revealed that CRP, PCT, and IL-6 levels were considerably discrepant, which could be misinterpreted if only CRP tests are performed.

Molecular identification of archaic bones as a native Korean black bear: implications for the ongoing bear restoration program.

The genetic investigation of the archeological or museum samples, including endangered species, provides vital information necessary to plan, implement, and revisit conservation strategies. In South Korea, the Asian black bear went almost extinct in wild by 2002, without leaving any authentic specimens representing the native population. Recently researchers found a set of animal bones in a natural cave in Mt. Taebaek (South Korea), suspected to be of a bear. In the present study, we undertook a molecular investigation and radiocarbon dating to establish the species' identity, phylogenetic position, and approximate age of the recovered specimen. The genetic investigation (CytB, COI, D-loop, SRY, and ZFX-ZFY) identified the sample as a male Asian black bear with close phylogenetic affinity with Northeast Asian bears. Radiocarbon dating estimated the bones to be aged 1800-1942 calAD. These findings indicate that the bone specimens found in the natural cave in Mt. Taebaek were from an individual that naturally inhabited South Korea long before the importing of farm bears (the 1980s) and initiation of wild population restoration (2004). The present study provides the first genetic information record of the native South Korean black bear. Our findings reaffirm the appropriateness of the ongoing bear restoration program in South Korea, with the reintroduction of individuals from North Korea and Russia.

Antiviral activity of canine interferon lambda 3 expressed using a recombinant adenovirus against canine coronavirus, canine parvovirus, and canine distemper virus.

Canine coronavirus (CCoV), canine parvovirus (CPV), and canine distemper virus (CDV) are highly contagious canine pathogens; dogs with these diseases are difficult to treat. In a previous study, we developed a recombinant adenovirus expressing canine interferon lambda 3 (Ad-caIFNλ3) in canine epithelial cells. In this study, we aimed to investigate the antiviral activity of Ad-caIFNλ3 against CCoV, CPV, and CDV in two canine cell lines, A72 and MDCK. Ad-caIFNλ3 transduction suppressed replication of these viruses without cytotoxicity. Our results suggest that Ad-caIFNλ3 may be a therapeutic candidate for canine viral diseases.

Cross-Species Transmission of Rabbit Hepatitis E Virus to Pigs and Evaluation of the Protection of a Virus-like Particle Vaccine against Rabbit Hepatitis E Virus Infection in Pigs.

We investigated the cross-species transmission of rabbit hepatitis E virus (rb HEV) to pigs and evaluated the cross-protection of a swine (sw) HEV-3 virus-like particle (VLP) vaccine against rb HEV infection in pigs. Twelve 4-week-old conventional pigs were divided into negative control (n = 3), positive control (rb HEV-infected, n = 4), and vaccinated (vaccinated and rb HEV-challenged, n = 5) groups. The vaccine was administered at weeks 0 and 2, and viral challenge was conducted at week 4. Serum HEV RNA, anti-HEV antibody, cytokine, and liver enzyme levels were determined. Histopathological lesions were examined in abdominal organs. Viral RNA was detected and increased anti-HEV antibody and alanine aminotransferase (ALT) levels were observed in positive control pigs; liver fibrosis, inflammatory cell infiltration in the lamina propria of the small intestine and shortened small intestine villi were also observed. In vaccinated pigs, anti-HEV antibody and Th1 cytokine level elevations were observed after the second vaccination; viral RNA was not detected, and ALT level elevations were not observed. The results verified the cross-species transmission of rb HEV to pigs and cross-protection of the sw HEV-3 VLP vaccine against rb HEV infection in pigs. This vaccine may be used for cross-protection against HEV infection in other species.

Immunization with Virus-Like Particle Vaccine Protects Rabbits against Hepatitis E-3 Virus Infection.

Here, rabbits were immunized with a virus-like particle (VLP) vaccine prepared by expressing 239 amino acids of the swine hepatitis E virus (HEV)-3 capsid protein using a baculovirus system. Thirty specific-pathogen-free rabbits were divided into five groups (negative and positive control and 10, 50, and 100 µg VLP-vaccinated). Positive control group rabbits showed viremia and fecal viral shedding, whereas rabbits vaccinated with 10 µg VLP showed transient fecal viral shedding, and rabbits vaccinated with 50 and 100 µg VLP did not show viremia or fecal viral shedding. Serum anti-HEV antibody titers increased in a dose-dependent manner. Anti-HEV antibody titers were significantly higher (p < 0.05) in 100 µg VLP-vaccinated rabbits than in the negative control rabbits at week 4. Anti-HEV antibody titers were significantly higher in 50 and 10 µg VLP-vaccinated rabbits than in the negative control rabbits at weeks 8 and 11, respectively. Serum IFN-γ and IL-12 levels were significantly higher (p < 0.01) in rabbits vaccinated with 50 and 100 µg VLP than in the negative control rabbits at weeks 4 and 6. Liver tissues of 50 and 100 µg VLP-vaccinated rabbits displayed significantly less (p < 0.05) fibrosis than those of the positive control rabbits. The prepared VLP vaccine demonstrated dose-dependent immunogenicity sufficient for inducing anti-HEV antibody production, thus protecting rabbits against swine HEV-3.

Prefrontal cortical activation in Internet Gaming Disorder Scale high scorers during actual real-time internet gaming: A preliminary study using fNIRS.

Background: Observation of real-time neural characteristics during gameplay would provide distinct evidence for discriminating the currently controversial diagnosis of internet gaming disorder (IGD), and elucidate neural mechanisms that may be involved in addiction. We aimed to provide preliminary findings on possible neural features of IGD during real-time internet gaming using functional near-infrared spectroscopy (fNIRS). Methods: Prefrontal cortical activations accompanying positive and negative in-game events were investigated. Positive events: (1) participant's champion slays or assists in slaying an opponent without being slain. (2) the opposing team's nexus is destroyed. Negative events: (1) participant's champion is slain without slaying or assisting in slaying any opponent. (2) the team's nexus is destroyed. Collected data were compared between the IGD group and control group, each with 15 participants. Results: The IGD group scored significantly higher than the CTRL group on the craving scale. Following positive events, the IGD group displayed significantly stronger activation in the DLPFC. Following negative events, the IGD group displayed significantly weaker activation in the lateral OFC. Discussion and Conclusions: Individuals scoring high on the IGD scale may crave for more internet gaming after encountering desired events during the game. Such observations are supported by the correlation between the craving scale and DLPFC activation. The IGD group may also show diminished punishment sensitivity to negative in-game experiences rendering them to continue playing the game. The present study provides preliminary evidence that IGD may demonstrate neural characteristics observed in other addictive disorders and suggests the use of fNIRS in behavioral addiction studies.

EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction.

Classifying emotional states is critical for brain-computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.

Identification of Risk Factors for Suicidal Ideation and Attempt Based on Machine Learning Algorithms: A Longitudinal Survey in Korea (2007-2019).

Investigating suicide risk factors is critical for socioeconomic and public health, and many researchers have tried to identify factors associated with suicide. In this study, the risk factors for suicidal ideation were compared, and the contributions of different factors to suicidal ideation and attempt were investigated. To reflect the diverse characteristics of the population, the large-scale and longitudinal dataset used in this study included both socioeconomic and clinical variables collected from the Korean public. Three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were used to detect the risk factors for both suicidal ideation and attempt. The importance of the variables was determined using the model with the best classification performance. In addition, a novel risk-factor score, calculated from the rank and importance scores of each variable, was proposed. Socioeconomic and sociodemographic factors showed a high correlation with risks for both ideation and attempt. Mental health variables ranked higher than other factors in suicidal attempts, posing a relatively higher suicide risk than ideation. These trends were further validated using the conditions from the integrated and yearly dataset. This study provides novel insights into suicidal risk factors for suicidal ideations and attempts.

Effect of Surface Layer Proteins Derived from Paraprobiotic Kefir Lactic Acid Bacteria on Inflammation and High-Fat Diet-Induced Obesity.

The antiobesity action of nonviable probiotic lactic acid bacteria (PLAB) may be attributed to bacterial cellular components recognized by host cells. The anti-inflammation and antiobesity properties of surface layer proteins (SLPs) that are cellular components isolated from kefir PLAB were determined in macrophage RAW 264.7 cells and obese mice. Kefir SLPs significantly decreased secretion of IL-6 and production of NF-kB p65 protein by LPS-stimulated RAW 264.7 cells in a dose-response manner. C57BL/6J mice were fed a high-fat (HF) diet with oral administration of either saline (CON) or kefir SLPs for 6 weeks. SLPs significantly improved body weight gain and adipose tissue weight, plasma triglyceride concentrations, and insulin resistance. Profiling of adipocyte gene expression showed that the antiobesity effect was significantly related to the expression of genes associated with adipogenesis, autophagy, and inflammatory/immune response, and fatty acid oxidation. Taken together, SLPs are a novel bioactive component in kefir PLABs to target obesity and obesity-related disorders.