Pandemic Grief Reaction and Intolerance of Uncertainty on the Cognitive-Behavioral Model of COVID-Related Hypochondriasis Among Firefighters.

OBJECTIVE: This study aimed to explore the feasibility of cognitive-behavioral model hypochondriasis regarding coronavirus disease-2019 (COVID-19) among firefighters. In addition, we examined the possible role of their grief reaction and intolerance of uncertainty in the model of COVID-related hypochondriasis. METHODS: An anonymous online survey was done on October 27-28, 2022, among firefighters who witnessed people's death. Demographic characteristics were collected, and their psychological states were assessed using rating scales such as the Obsession with COVID-19 Scale (OCS), Coronavirus Reassurance-Seeking Behaviors Scale (CRBS), Fear of COVID-19 Scale (FCV-19S), Pandemic Grief Scale (PGS), and Intolerance of Uncertainty Scale-12 (IUS-12). RESULTS: Their OCS score was expected by the CRBS (ß=0.30, p<0.001), FCV-19S (ß=0.10, p<0.001), PGS (ß=0.29, p<0.001), and IUS12 (ß=0.04, p=0.024) (F=134.5, p<0.001). The COVID-related cognitive-behavioral model of hypochondriasis was feasible among firefighters who witnessed people's death. Their pandemic grief reaction and intolerance of uncertainty directly influenced their preoccupation with coronavirus, and viral anxiety and coronavirus reassurance-seeking behavior mediated the relationship. CONCLUSION: Firefighters' viral anxiety and coronavirus reassurance-seeking behavior mediated the influence of pandemic grief reaction or intolerance of uncertainty on the preoccupation with coronavirus.

The association between insulin resistance and depressive symptoms - A national representative cross-sectional study.

OBJECTIVE: Increasing evidence suggests a positive association between insulin resistance (IR) and depression. However, whether sex-or body mass index-specific differences exist remains controversial, and only few studies have analyzed specific symptom domains. Thus, the present study aimed to analyze the association between IR and depressive symptom domains and to clarify the effects of sex and body mass index. METHODS: The study sample comprised 4007 participants, aged 19-79, from the Korea National Health and Nutrition Examination Study 2020. Participants completed health interviews and examinations, providing data on circulating insulin and glucose levels, the Patient Health Questionnaire-9 (PHQ-9), and related covariates. IR was calculated using the homeostasis model assessment of insulin resistance. Associations between IR and PHQ-9 were analyzed using negative binomial regression with adjustments for the complex survey design. RESULTS: The association between log-transformed IR and PHQ-9 total scores was statistically significant (incidence rate ratio [IRR] = 1.17, 95% confidence interval [CI] = 1.07-1.29, p = 0.001). Only body mass index specific differences were statistically significant, as the association was only significant in those without obesity (IRR = 1.21, 95% CI = 1.06-1.38, p = 0.005). IR was associated with cognitive/affective (IRR = 1.23, 95% CI = 1.08-1.41, p = 0.002) and somatic (IRR = 1.14, 95% CI = 1.04-1.25, p = 0.005) depressive symptom domains. Sensitivity analyses revealed similar results. CONCLUSIONS: IR was positively associated with cognitive/affective and somatic depressive symptoms in non-obese individuals.

Feasibility and acceptability testing of CommSense: A novel communication technology to enhance health equity in clinician-patient interactions.

Background: Quality patient-clinician communication is paramount to achieving safe and compassionate healthcare, but evaluating communication performance during real clinical encounters is challenging. Technology offers novel opportunities to provide clinicians with actionable feedback to enhance their communication skills. Methods: This pilot study evaluated the acceptability and feasibility of CommSense, a novel natural language processing (NLP) application designed to record and extract key metrics of communication performance and provide real-time feedback to clinicians. Metrics of communication performance were established from a review of the literature and technical feasibility verified. CommSense was deployed on a wearable (smartwatch), and participants were recruited from an academic medical center to test the technology. Participants completed a survey about their experience; results were exported to SPSS (v.28.0) for descriptive analysis. Results: Forty (n = 40) healthcare participants (nursing students, medical students, nurses, and physicians) pilot tested CommSense. Over 90% of participants "strongly agreed" or "agreed" that CommSense could improve compassionate communication (n = 38, 95%) and help healthcare organizations deliver high-quality care (n = 39, 97.5%). Most participants (n = 37, 92.5%) "strongly agreed" or "agreed" they would be willing to use CommSense in the future; 100% (n = 40) "strongly agreed" or "agreed" they were interested in seeing information analyzed by CommSense about their communication performance. Metrics of most interest were medical jargon, interruptions, and speech dominance. Conclusion: Participants perceived significant benefits of CommSense to track and improve communication skills. Future work will deploy CommSense in the clinical setting with a more diverse group of participants, validate data fidelity, and explore optimal ways to share data analyzed by CommSense with end-users.

Comparison of Pre-Trained YOLO Models on Steel Surface Defects Detector Based on Transfer Learning with GPU-Based Embedded Devices.

Steel is one of the most basic ingredients, which plays an important role in the machinery industry. However, the steel surface defects heavily affect its quality. The demand for surface defect detectors draws much attention from researchers all over the world. However, there are still some drawbacks, e.g., the dataset is limited accessible or small-scale public, and related works focus on developing models but do not deeply take into account real-time applications. In this paper, we investigate the feasibility of applying stage-of-the-art deep learning methods based on YOLO models as real-time steel surface defect detectors. Particularly, we compare the performance of YOLOv5, YOLOX, and YOLOv7 while training them with a small-scale open-source NEU-DET dataset on GPU RTX 2080. From the experiment results, YOLOX-s achieves the best accuracy of 89.6% mAP on the NEU-DET dataset. Then, we deploy the weights of trained YOLO models on Nvidia devices to evaluate their real-time performance. Our experiments devices consist of Nvidia Jetson Nano and Jetson Xavier AGX. We also apply some real-time optimization techniques (i.e., exporting to TensorRT, lowering the precision to FP16 or INT8 and reducing the input image size to 320 × 320) to reduce detection speed (fps), thus also reducing the mAP accuracy.

Tailoring Compressive Strength and Absorption Energy of Lightweight Multi-Phase AlCuSiFeX (X = Cr, Mn, Zn, Sn) High-Entropy Alloys Processed via Powder Metallurgy.

The development of lightweight HEAs with high strength and low cost is an urgent requirement. In this study, equimolar AlCuSiFeX (X = Cr, Mn, Zn, Sn) lightweight HEAs were fabricated by advanced powder metallurgy. The mechanical alloying was performed for 45 h, and the powder compacts were densified at 650 °C. The final results revealed that AlCuSiFeSn lightweight HEA was composed of a single face-centered cubic (FCC) and Cu81Sn22, whereas AlCuSiFeZn showed a dual FCC and body-centered cubic (BCC) structures. Similarly, AlCuSiFeMn alloy contained a BCC + FCC phase with a µ-phase, whereas a σ-phase was present in AlCuSiFeCr in addition to FCC + BCC phases. We also calculated various thermodynamic parameters to predict the solid-solution phase stability of each of the above lightweight HEAs. It was found that lightweight HEAs with additive elements Sn and Zn tend to predominant FCC phases, whereas those with Cr and Mn result in major BCC with hard µ and σ phases, which further improve their mechanical strength. A maximum fracture strain of 23% was obtained for AlCuSiFeSn followed by 19% for AlCuSiFeZn HEA. The compressive fracture mechanisms of these lightweight HEAs are also discussed and reported here.

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

Clinical Characteristics Associated with Suicidal Attempt and Non-Suicidal Self-Injury in Korean Adolescents.

OBJECTIVE: This study evaluated the association between mood and anxiety symptoms and suicidal attempt (SA) and/or non-suicidal self-injury (NSSI) in adolescents seeking mental health services. We also tested predictors of SA and NSSI. METHODS: We retrospectively reviewed the medical records of 220 adolescents who completed psychological assessment in clinical sample. Participants did the Adolescent General Behavior Inventory (A-GBI) and Children's Depression Inventory (CDI). SA and NSSI were assessed retrospectively by interview. The caregiver of participants completed the Beck Depression Inventory (BDI) for themselves. RESULTS: 17% of total participants had a history of SA, and 24% experienced NSSI. Both SA and NSSI were more common in girls. The score of depressive subscale on A-GBI was higher in adolescents with SA than those without. The participants with NSSI showed higher scores on CDI and depressive subscale on A-GBI than those without. SA was associated with maternal BDI and history of NSSI; female sex, depressive subscale on A-GBI, and history of SA with NSSI. CONCLUSION: Our study found that NSSI and SA are strongly associated in adolescents. Female sex and depressive symptoms of the adolescents were also significantly associated with NSSI in Korean adolescent. Findings are consistent with patterns in other countries.

Toothbrush microbiomes feature a meeting ground for human oral and environmental microbiota.

BACKGROUND: While indoor microbiomes impact our health and well-being, much remains unknown about taxonomic and functional transitions that occur in human-derived microbial communities once they are transferred away from human hosts. Toothbrushes are a model to investigate the potential response of oral-derived microbiota to conditions of the built environment. Here, we characterize metagenomes of toothbrushes from 34 subjects to define the toothbrush microbiome and resistome and possible influential factors. RESULTS: Toothbrush microbiomes often comprised a dominant subset of human oral taxa and less abundant or site-specific environmental strains. Although toothbrushes contained lower taxonomic diversity than oral-associated counterparts (determined by comparison with the Human Microbiome Project), they had relatively broader antimicrobial resistance gene (ARG) profiles. Toothbrush resistomes were enriched with a variety of ARGs, notably those conferring multidrug efflux and putative resistance to triclosan, which were primarily attributable to versatile environmental taxa. Toothbrush microbial communities and resistomes correlated with a variety of factors linked to personal health, dental hygiene, and bathroom features. CONCLUSIONS: Selective pressures in the built environment may shape the dynamic mixture of human (primarily oral-associated) and environmental microbiota that encounter each other on toothbrushes. Harboring a microbial diversity and resistome distinct from human-associated counterparts suggests toothbrushes could potentially serve as a reservoir that may enable the transfer of ARGs. Video abstract.

A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost.

Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13-14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.

Face Recognition at a Distance for a Stand-Alone Access Control System.

Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

Exploratory Factor Analysis of the Adolescent Version of the General Behavior Inventory in Korean Youth.

OBJECTIVES: We examined the factor structure of the Adolescent version of the General Behavior Inventory (A-GBI) for Koreans. METHODS: We retrospectively reviewed the medical records of 220 adolescents (age, 12-18 years) who completed the A-GBI through the Department of Psychiatry at Asan Medical Center, Seoul, Korea, from October 2011 to December 2018. Caregivers of the study participants completed the Parent version of the GBI (P-GBI) 10-item Mania Scale. The adolescents were evaluated based on the A-GBI, Children's Depression Inventory (CDI), and Revised-Children's Manifest Anxiety Scale (RCMAS). Subsequently, an exploratory factor analysis (EFA) using the maximum likelihood method with direct oblimin rotation and correlation analyses with other scales were performed. RESULTS: The EFA identified a two-factor structure as having the best fit: factor I included depressive symptoms and factor II included hypomanic/biphasic symptoms. Factor I was very strongly correlated with the A-GBI depressive subscale (r=0.990, p<0.001) and strongly correlated with CDI (r=0.764, p<0.001) and RCMAS (r=0.666, p<0.001). Factor II was also very strongly correlated with the A-GBI hypomanic/biphasic subscale (r=0.877, p<0.001) and weakly correlated with CDI (r=0.274, p<0.001) and RCMAS (r=0.332, p<0.001). CONCLUSION: The above findings support a two-dimensional model of mood symptoms in Korean youth.

Ulmus davidiana var. japonica Nakai upregulates eosinophils and suppresses Th1 and Th17 cells in the small intestine.

The bark of Ulmus davidiana var. japonica Nakai (Ulmaceae) has been used in traditional Korean medicine for chronic inflammation in the gastrointestinal tract. Here we investigated the frequency and cytokine profile of the major immune cells in the small intestinal lamina propria (SI LP), spleen, and mesenteric lymph nodes (MLNs) of mice treated orally with Ulmus davidiana var. japonica Nakai bark water extract (UDE) to address the immunomodulatory role of this herb in intestinal homeostasis. B6 mice were given 5g/kg UDE once daily for 14 days. They were then sacrificed, and cells were isolated from the spleen, MLNs, and SI LP. The proportion of B versus T lymphocytes, CD4(+) versus CD8(+) T lymphocytes, Th1 and Th17 cells, and Foxp3(+) regulatory T cells in the spleen, MLNs, and SI LP were analyzed. The frequency of antigen-presenting cells (APCs), including dendritic cells, macrophages, and eosinophils in the SI LP and the expression of costimulatory molecules on APCs were also evaluated. The numbers and frequencies of Th1 and Th17 cells in the SI LP were significantly reduced in the UDE-treated mice compared with PBS controls. In addition, the proportion of IL-4-producing eosinophils in the SI LP was significantly elevated in the UDE-treated mice compared with controls. Taken together, these data indicate that UDE up-regulates the number and frequency of SI LP eosinophils, which can down-regulate the Th1 and Th17 responses via IL-4 secretion and contribute to intestinal homeostasis.

Musicians' medicine: musculoskeletal problems in string players.

There is increasing attention to medical problems of musicians. Many studies find a high prevalence of work-related musculoskeletal disorders in musicians, ranging from 73.4% to 87.7%, and string players have the highest prevalence of musculoskeletal problems. This paper examines the various positions and movements of the upper extremities in string players: 1) basic postures for holding instruments, 2) movements of left upper extremity: fingering, forearm posture, high position and vibrato, 3) movements of right upper extremity: bowing, bow angles, pizzicato and other bowing techniques. These isotonic and isometric movements can lead to musculoskeletal problems in musicians. We reviewed orthopedic disorders that are specific to string players: overuse syndrome, muscle-tendon syndrome, focal dystonia, hypermobility syndrome, and compressive neuropathy. Symptoms, interrelationships with musical performances, diagnosis and treatment of these problems were then discussed.

Visible cathodoluminescence of quantum dot films by direct irradiation of electron beam and its materialization as a field emission device.

The field emission (FE) device based on quantum dot (QD) films as a cathodoluminescent (CL) material has not emerged yet due to the relatively low quantum efficiency and weak photostability of nanocrystals (NCs). Here we improve film stability and luminescence yields by preparing neat films of well-packed core-multishell QDs using spray coating method and then using low-temperature atomic layer deposition (ALD) to infill the pores of these films with metal oxides to produce inorganic nanocomposites. The ALD coatings to protect oxidation and degradation by electrons prevent internal atomic and molecular diffusion and decrease surface trap densities of QD films. Furthermore, the CL of the core-multishell QD films is 2.4 times higher than before ALD infilling. We fabricate the FE device by combining cathode structure with carbon nanotube (CNT) emitters and anode plates with QD thin film and successfully can get brilliant images of the light-emitting FE device. Our research opens a way for developing new quantum optoelectronics with high-performance.

Characterization of CCR9 expression and thymus-expressed chemokine responsiveness of the murine thymus, spleen and mesenteric lymph node.

CC chemokine receptor 9 (CCR9) is a receptor expressed at high levels in immature thymocytes, small intestine trafficking T cells and IgA-producing plasma cells. CCR9 mediates chemotaxis in response to thymus-expressed chemokine (TECK) selectively expressed in the thymus and small intestine. CCR9 expression in different subpopulations of thymus, spleen and mesenteric lymph node (MLN) cells was analyzed by flow cytometry and TECK responsiveness of those lymphoid cells was assessed by a Transwell migration assay. CCR9 surface expression level did not completely correlate with cellular chemotaxis to its cognate ligand TECK. The active chemotaxis to TECK was observed in CD4 single positive thymocytes and CD4(-)B220(hi) splenocyte and MLN cells, which poorly expressed CCR9 on their surface. TECK responsiveness of CCR9-abundant subpopulations in the thymus and MLN was unremarkable except for CD4(+)B220(hi) subset of the MLN, and was evident in the CCR3(+) subsets of the thymus and spleen. Exposure to TECK did not affect CCR9 expression in the thymus, spleen and MLN, except for the CD4(+)CD8(+) thymocyte. CCR9 was exuberantly expressed in the cytoplasm of lymphoid cells. CCR9 may act in concert with CCR3 for in terms of TECK responsiveness. Its cytoplasmic location may allow precise regulation of leukocyte responsiveness to TECK.

Cho-kyung-jong-ok-tang, a traditional Korean herbal formula induces type 2 shift in murine natural killer cell cytokine production.

ETHNOPHARMACOLOGICAL RELEVANCE: Cho-kyung-jong-ok-tang (CKJOT) is a traditional Korean herbal formula specifically used for female infertility including unexplained recurrent pregnancy loss (RPL). AIM OF THE STUDY: The present study aims to evaluate the effects of CKJOT on mouse natural killer (NK) cells to address the possible immunological basis of protective effects of this herbal medicine on unexplained RPL. MATERIALS AND METHODS: NK cells isolated from spleens of 6-week-old C57BL/6 mice were differentiated into NK0, NK1, and NK2 cells in the presence of various concentrations of CKJOT-extract. Apoptotic cell number, level of intracellular cytokines, and expression of cytokine-related transcription factors were measured. RESULTS: CKJOT had little effect in improving viability of NK0, NK1, and NK2 cells. However, CKJOT addition during NK cell differentiation suppressed the production of interferon-gamma (IFN-γ), and enhanced that of interleukin-5, in the NK1 and NK2 subsets, respectively. T-bet, a transcription factor associated with IFN-γ expression was down-regulated; while Th2 linked transcription factors (STAT6 and GATA3) were up-regulated especially with 100 µg/mL treatment of CKJOT. CONCLUSION: The type 2 shift in NK cell-secreted cytokines induced by CKJOT in mouse NK cells may explain the protective effect associated with its traditional use in unexplained RPL.

Effects of bee venom on the maturation of murine dendritic cells stimulated by LPS.

AIM OF STUDY: This study was performed to elicit the effectiveness of bee venom (BV), a traditional immunosuppressive Korean acupuncture agent, on the maturation of dendrtic cells (DCs). MATERIALS AND METHODS: Immature dendritic cells (iDCs) were generated from mouse bone marrow cells with GM-CSF. After 10 days of initial differentiation, DCs were activated with lipopolysaccharides (LPS) for another 48h in the presence or absence of BV. Surface molecule analysis, intracytoplasmic staining of cytokines, FITC-conjugated antigen uptake, and transwell migration assays were conducted with iDCs and activated DCs. RESULTS: Up-regulation of costimulatory molecules, typical of mature DCs (mDCs) was inhibited by addition of BV. Pro-inflammatory cytokines were also found to be reduced with BV treatment in LPS-stimulated DC. A decrease in antigen uptake upon the maturation of DC was reversed in low dose BV treated mDC. In addition, BV treated mDC demonstrated reduced directional migration in response to CCL21, a lymphoid chemokine which directs mDC. CONCLUSIONS: BV may have a therapeutic effect an on abnormally activated immune status, such as autoimmune rheumatoid arthritis, through an immune-modulatory effect on DC.