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BackgroundKawasaki disease (KD), an acute febrile illness and systemic vasculitis, is the leading cause of acquired heart disease in children in industrialized countries. KD leads to the development of coronary artery aneurysms (CAA) in affected children, which may persist for months and even years after the acute phase of the disease. There is an unmet need to characterize the immune and pathological mechanisms of the long-term complications of KD.MethodsWe examined cardiovascular complications in the Lactobacillus casei cell wall extract (LCWE) mouse model of KD-like vasculitis over 4 months. The long-term immune, pathological, and functional changes occurring in cardiovascular lesions were characterized by histological examination, flow cytometric analysis, immunofluorescent staining of cardiovascular tissues, and transthoracic echocardiogram.ResultsCAA and abdominal aorta dilations were detected up to 16 weeks following LCWE injection and initiation of acute vasculitis. We observed alterations in the composition of circulating immune cell profiles, such as increased monocyte frequencies in the acute phase of the disease and higher counts of neutrophils. We determined a positive correlation between circulating neutrophil and inflammatory monocyte counts and the severity of cardiovascular lesions early after LCWE injection. LCWE-induced KD-like vasculitis was associated with myocarditis and myocardial dysfunction, characterized by diminished ejection fraction and left ventricular remodeling, which worsened over time. We observed extensive fibrosis within the inflamed cardiac tissue early in the disease and myocardial fibrosis in later stages.ConclusionOur findings indicate that increased circulating neutrophil counts in the acute phase are a reliable predictor of cardiovascular inflammation severity in LCWE-injected mice. Furthermore, long-term cardiac complications stemming from inflammatory cell infiltrations in the aortic root and coronary arteries, myocardial dysfunction, and myocardial fibrosis persist over long periods and are still detected up to 16 weeks after LCWE injection.

Background Artificial intelligence (AI) technology can enable more efficient decision-making in healthcare settings. There is a growing interest in improving the speed and accuracy of AI systems in providing responses for given tasks in healthcare settings. Objective This study aimed to assess the reliability of ChatGPT in determining emergency department (ED) triage accuracy using the Korean Triage and Acuity Scale (KTAS). Methods Two hundred and two virtual patient cases were built. The gold standard triage classification for each case was established by an experienced ED physician. Three other human raters (ED paramedics) were involved and rated the virtual cases individually. The virtual cases were also rated by two different versions of the chat generative pre-trained transformer (ChatGPT, 3.5 and 4.0). Inter-rater reliability was examined using Fleiss’ kappa and intra-class correlation coefficient (ICC). Results The kappa values for the agreement between the four human raters and ChatGPTs were .523 (version 4.0) and .320 (version 3.5). Of the five levels, the performance was poor when rating patients at levels 1 and 5, as well as case scenarios with additional text descriptions. There were differences in the accuracy of the different versions of GPTs. The ICC between version 3.5 and the gold standard was .520, and that between version 4.0 and the gold standard was .802. Conclusions A substantial level of inter-rater reliability was revealed when GPTs were used as KTAS raters. The current study showed the potential of using GPT in emergency healthcare settings. Considering the shortage of experienced manpower, this AI method may help improve triaging accuracy.

There is a growing interest in digital healthcare to manage disease and improve medication compliance. In Korea, the health apps market continues to grow, however, little research has been done on their content and quality. A thorough assessment beyond the current star rating would ensure the safety and effectiveness of health apps. A systematic search was conducted in the Google Play and Apple App Store. The Korean version of the Mobile App Rating Scale (MARS) was used to access and systematically evaluate the contents of available medication management apps. Descriptions and technical information about the app were collected based on AppStore and the software company’s website. A total of 2,135 apps were identified and 39 apps were evaluated by four independent reviewers. Of the apps included, 17.9% were paid ones, at a cost between $2 and $4. The oldest was developed 11 years ago and 5 apps (12.8%) were released within the last year. The study results identified the features of apps used to manage medications, ranging from reminders to connecting to IoT devices. In addition, relationships were identified between the input variables studied, including health professionals, updates, and quality scores using MARS. The findings of this review highlight the importance of quality control with adequate features and continuous maintenance in health apps. In addition, to achieve effectiveness with good medication adherence, along with the inclusion of health professionals, close collaboration with all stakeholders in addition to the proper integration of newly updated technology is essential.

Objectives De-identifying protected health information (PHI) in medical documents is important, and a prerequisite to de-identification is the identification of PHI entity names in clinical documents. This study aimed to compare the performance of three pre-training models that have recently attracted significant attention and to determine which model is more suitable for PHI recognition. Methods We compared the PHI recognition performance of deep learning models using the i2b2 2014 dataset. We used the three pre-training models—namely, bidirectional encoder representations from transformers (BERT), robustly optimized BERT pre-training approach (RoBERTa), and XLNet (model built based on Transformer-XL)—to detect PHI. After the dataset was tokenized, it was processed using an inside-outside-beginning tagging scheme and WordPiece-tokenized to place it into these models. Further, the PHI recognition performance was investigated using BERT, RoBERTa, and XLNet. Results Comparing the PHI recognition performance of the three models, it was confirmed that XLNet had a superior F1-score of 96.29%. In addition, when checking PHI entity performance evaluation, RoBERTa and XLNet showed a 30% improvement in performance compared to BERT. Conclusions Among the pre-training models used in this study, XLNet exhibited superior performance because word embedding was well constructed using the two-stream self-attention method. In addition, compared to BERT, RoBERTa and XLNet showed superior performance, indicating that they were more effective in grasping the context.

Objectives This study was conducted to develop a generalizable annotation tool for bilingual complex clinical text annotation, which led to the design and development of a clinical text annotation tool, ANNO. Methods We designed ANNO to enable human annotators to support the annotation of information in clinical documents efficiently and accurately. First, annotations for different classes (word or phrase types) can be tagged according to the type of word using the dictionary function. In addition, it is possible to evaluate and reconcile differences by comparing annotation results between human annotators. Moreover, if the regular expression set for each class is updated during annotation, it is automatically reflected in the new document. The regular expression set created by human annotators is designed such that a word tagged once is automatically labeled in new documents. Results Because ANNO is a Docker-based web application, users can use it freely without being subjected to dependency issues. Human annotators can share their annotation markups as regular expression sets with a dictionary structure, and they can cross-check their annotated corpora with each other. The dictionary-based regular expression sharing function, cross-check function for each annotator, and standardized input (Microsoft Excel) and output (extensible markup language [XML]) formats are the main features of ANNO. Conclusions With the growing need for massively annotated clinical data to support the development of machine learning models, we expect ANNO to be helpful to many researchers.

IntroductionKawasaki disease (KD) is an acute systemic vasculitis that predominantly afflicts children. KD development is known to be associated with an aberrant immune response and abnormal platelet activation, however its etiology is still largely unknown. Myosin light chain 9 (Myl9) is known to regulate cellular contractility of both non-muscle and smooth muscle cells, and can be released from platelets, whereas any relations of Myl9 expression to KD vasculitis have not been examined.MethodsPlasma Myl9 concentrations in KD patients and children with febrile illness were measured and associated with KD clinical course and prognosis. Myl9 release from platelets in KD patients was also evaluated in vitro. Myl9 expression was determined in coronary arteries from Lactobacillus casei cell wall extract (LCWE)-injected mice that develop experimental KD vasculitis, as well as in cardiac tissues obtained at autopsy from KD patients.Results and discussionPlasma Myl9 levels were significantly higher in KD patients during the acute phase compared with healthy controls or patients with other febrile illnesses, declined following IVIG therapy in IVIG-responders but not in non-responders. In vitro, platelets from KD patients released Myl9 independently of thrombin stimulation. In the LCWE-injected mice, Myl9 was detected in cardiac tissue at an early stage before inflammatory cell infiltration was observed. In tissues obtained at autopsy from KD patients, the highest Myl9 expression was observed in thrombi during the acute phase and in the intima and adventitia of coronary arteries during the chronic phase. Thus, our studies show that Myl9 expression is significantly increased during KD vasculitis and that Myl9 levels may be a useful biomarker to estimate inflammation and IVIG responsiveness to KD.

Background and Objectives: Currently, only patients with osteonecrosis of the femoral head (ONFH), who had bone defects involving 30–33.3% of the remaining femoral head, are indicated in hip resurfacing arthroplasty (HRA). In an experimental cadaver model of ONFH involving up to 50% of the remaining femoral head, the initial stability of the femoral head implant (FHI) at the interface between the implant and the remaining femoral head was measured. Materials and Methods: The ten specimens and the remaining ten served as the experimental group and the control group, respectively. We examined the degree of the displacement of the FHI, the bonding strength between the FHI and the retained bone and that at the interface between the FHI and bone cement. Results: Changes in the degree of displacement at the final phase from the initial phase were calculated as 0.089 ± 0.036 mm in the experimental group and 0.083 ± 0.056 mm in the control group. However, this difference reached no statistical significance (p = 0.7789). Overall, there was an increase in the degree of displacement due to the loading stress, with increased loading cycles in both groups. In cycles of up to 6000 times, there was a steep increase. After cycles of 8000 times, however, there was a gradual increase. Moreover, in cycles of up to 8000 times, there was an increase in the difference in the degree of displacement due to the loading stress between the two groups. After cycles of 8000 times, however, such difference remained almost unchanged. Conclusions: In conclusion, orthopedic surgeons could consider performing the HRA in patients with ONFH where the bone defects involved up to 50% of the remaining femoral head, without involving the femoral head–neck junction in the anterior and superior area of the femoral head. However, more evidence-based studies are warranted to justify our results.

Abstract Background Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. Methods We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results For the C3R model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy. Conclusions This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained.

This paper deals with one of the origins and trigger mechanisms responsible for the observed performance enhancements in the hybrid scenario experiments conducted in Korea Superconducting Tokamak Advanced Research (KSTAR). The major contribution to the performance improvement comes from a broader and higher pedestal formation. The increase of fast ion pressure due to a plasma density decrease also contributes substantially to the global beta. Although the reduced core plasma volume resulting from the pedestal expansion has a negative effect on the core thermal energy, a considerable confinement improvement observed in the inner core region limits the degradation. The one significant characteristic of high-performance discharges is the presence of Coherent Edge-localized Mode (CEM) activity. CEM is triggered during the pedestal recovery phase between typical ELM crashes and has been found to be related to the increase of particle and heat transport. It appears to underlie two commonly observed phenomena in high-performance hybrid scenario discharges in KSTAR; pedestal broadening and continuous density decrease. Despite the associated transport increase, CEM activities can induce performance enhancement. With the pedestal broadening, ELM crashes become delayed and weakened, which, in turn, allows for a higher pedestal. Moreover, the density decrease directly increases fast ion pressure by extending the beam-slowing-down time. The linear gyrokinetic analysis reveals that the increase of fast ions could initiate positive feedback loops, leading to the stabilization of Ion Temperature Gradient mode in the inner core region.

Kawasaki disease (KD) is the leading cause of acquired heart disease among children. Murine and human data suggest that the NLRP3–IL-1β pathway is the main driver of KD pathophysiology. NLRP3 can be activated during defective autophagy/mitophagy. We used the Lactobacillus casei cell wall extract (LCWE) murine model of KD vasculitis to examine the role of autophagy/mitophagy on cardiovascular lesion development. LCWE-injected mice had impaired autophagy/mitophagy and increased levels of ROS in cardiovascular lesions, together with increased systemic 8-OHdG release. Enhanced autophagic flux significantly reduced cardiovascular lesions in LCWE-injected mice, whereas autophagy blockade increased inflammation. Vascular smooth muscle cell–specific deletion of Atg16l1 and global Parkin–/– significantly increased disease formation, supporting the importance of autophagy/mitophagy in this model. Ogg1–/– mice had significantly increased lesions with increased NLRP3 activity, whereas treatment with MitoQ reduced vascular tissue inflammation, ROS production, and systemic 8-OHdG release. Treatment with MN58b or Metformin (increasing AMPK and reducing ROS) resulted in decreased cardiovascular lesions. Our results demonstrate that impaired autophagy/mitophagy and ROS-dependent damage exacerbate the development of murine KD vasculitis. This pathway can be efficiently targeted to reduce disease severity. These findings enhance our understanding of KD pathogenesis and identify potentially novel therapeutic avenues for KD treatment.

ObjectivesBreast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used.MethodsWe used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble.ResultsExperimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data.ConclusionsWe compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.

Kawasaki disease (KD), an acute febrile childhood illness and systemic vasculitis of unknown etiology, is the leading cause of acquired heart disease among children. Experimental data from murine models of KD vasculitis and transcriptomics data generated from whole blood of KD patients indicate the involvement of the NLRP3 inflammasome and interleukin-1 (IL-1) signaling in KD pathogenesis. MicroRNA-223 (miR-223) is a negative regulator of NLRP3 activity and IL-1β production, and its expression has been reported to be upregulated during acute human KD; however, the specific role of miR-223 during KD vasculitis remains unknown. Here, using the Lactobacillus casei cell wall extract (LCWE) murine model of KD vasculitis, we demonstrate increased miR-223 expression in LCWE-induced cardiovascular lesions. Compared with control WT mice, LCWE-injected miR-223-deficient mice (miR223−/y) developed more severe coronary arteritis and aortitis, as well as more pronounced abdominal aorta aneurysms and dilations. The enhanced cardiovascular lesions and KD vasculitis observed in LCWE-injected miR223−/y mice correlated with increased NLRP3 inflammasome activity and elevated IL-1β production, indicating that miR-223 limits cardiovascular lesion development by downmodulating NLRP3 inflammasome activity. Collectively, our data reveal a previously unappreciated role of miR-223 in regulating innate immune responses and in limiting KD vasculitis and its cardiovascular lesions by constraining the NLRP3 inflammasome and the IL-1β pathway. These data also suggest that miR-223 expression may be used as a marker for KD vasculitis pathogenesis and provide a novel therapeutic target.

Stress resistance is an important trait expected of lactic acid bacteria used in food manufacturing. Among the various sources of stress, high temperature is a key factor that interrupts bacterial growth. In this regards, constant efforts are made for the development of heat-resistant strains, but few studies were done accompanying genomic analysis to identify the causal factors of the resistance mechanisms. Furthermore, it is also thought that tolerance to multiple stresses are equally important. Herein, we isolated one Enterococcus faecium strain named BIOPOP-3 and completed a full-length genome sequence. Using this strain, a two-step adaptive laboratory evolution (ALE) method was applied to obtain a heat-resistant strain, BIOPOP-3 ALE. After sequencing the whole genome, we compared the two full-length sequences and identified one non-synonymous variant and four indel variants that could potentially confer heat resistance, which were technically validated by resequencing. We experimentally verified that the evolved strain was significantly enhanced in not only heat resistance but also acid and bile resistance. We demonstrated that the developed heat-resistant strain can be applied in animal feed manufacturing processes. The multi-stress-resistant BIOPOP-3 ALE strain developed in this study and the two-step ALE method are expected to be widely applied in industrial and academic fields. In addition, we expect that the identified variants which occurred specifically in heat-resistant strain will enhance molecular biological understanding and be broadly applied to the biological engineering field.

ObjectivesSchizophrenia is one of the most prevalent mental illnesses contributing to national burden worldwide. It is well known that mental health nursing education, including clinical placement, is still insufficient to reach the optimal level of competency in nursing students. This study suggests a new form of mental health virtual reality (VR) simulation that is user-friendly and engaging to improve education about schizophrenia, thereby improving its treatment.MethodA mixed-methods study was conducted with a total of 60 nursing students, using 360-degree videos of five different scenarios reflecting clinical symptoms of schizophrenia patients and related treatment tasks delivered via head-mounted displays (HMDs). We used a 17-item quantitative questionnaire and a 7-item open-ended qualitative questionnaire to evaluate the ease of use and usefulness of the VR simulation program and to identify areas where further improvement is required.ResultsThe VR simulation program was perceived as useful and exciting. Participants stressed that the high realism of the simulation increased their engagement in and motivation to learn about mental health nursing. Some participants made suggestions, such as further refining the picture and sound quality in order to achieve satisfactory educational outcomes.ConclusionVR simulation using 360-degree videos and HMDs could serve as an effective alternative form of clinical training in mental health nursing. Education could be enhanced by its benefits of being engaging and exciting, as reported by this study's participants.

There is ongoing research on using electroencephalography (EEG) to predict depression. In particular, the deep learning method in which brain waves are used as inputs of a convolutional neural network (CNN) is being widely researched and has shown remarkable performance. We built a regression model to predict the severity score (Beck Depression Inventory [BDI]) of depressed patients as an extension of the deep-asymmetry method, which has shown promising performance in depression classification. Predicting the severity of depression is very important because the treatment and coping methods are different for each severity level. We imaged brain waves using the deep-asymmetry method, used them to train a two-dimensional CNN-based deep learning model, and achieved satisfactory performance. The EEG image-based CNN approach will make an important contribution to creating a highly interpretable model for predicting depression in the future.

For solar thermal harvesting, an experimental study was performed on the thermal absorption performance of water-based carbon nanotubes (CNTs), Cu, and Al2O3 nanofluids using a halogen lamp-based thermal radiation system. The effect of nanoparticle concentrations (0.01 wt.%, 0.1 wt.%, and 1 wt.%) on the nanofluid dispersion, stability, and thermal absorption characteristics was investigated, and a comparative analysis was performed for each type of nanofluid. All types of nanofluids increased the absorbance and electrical conductivity with increasing nanoparticle concentration, which contributed to improving the thermal absorption performance of nanofluids. The results showed that the thermal absorption performance was high in the order of carbon-based nanofluids (CNTs), metal-based nanofluids (Cu), and oxide-based nanofluids (Al2O3). In CNTs nanofluids, the thermal absorption performance expressed the time reduction rate, which was 12.8%, 16.3%, and 16.4% at 0.01 wt.%, 0.1 wt.%, and 1 wt.% test cases, respectively. Therefore, the 0.1 wt.%-CNTs nanofluid is more economical and appropriate. However, in Al2O3 nanofluids, the time reduction rate of the 1 wt.% nanofluid was significantly higher than that of the 0.01 wt.% and 0.1 wt.% nanofluids. In Cu nanofluids, unlike CNTs and Al2O3 nanofluids, the time reduction rate constantly increased as the nanoparticle concentration increased.

ObjectivesCardiovascular predictions are related to patients' quality of life and health. Therefore, a risk prediction model for cardiovascular conditions is needed.MethodsIn this paper, we propose a cardiovascular disease prediction model using the sixth Korea National Health and Nutrition Examination Survey (KNHANES-VI) 2013 dataset to analyze cardiovascular-related health data. First, statistical analysis was performed to find variables related to cardiovascular disease using health data related to cardiovascular disease. Second, a model of cardiovascular risk prediction by learning based on the deep belief network (DBN) was developed.ResultsThe proposed statistical DBN-based prediction model showed accuracy and an ROC curve of 83.9% and 0.790, respectively. Thus, the proposed statistical DBN performed better than other prediction algorithms.ConclusionsThe DBN proposed in this study appears to be effective in predicting cardiovascular risk and, in particular, is expected to be applicable to the prediction of cardiovascular disease in Koreans.

For the secondary use of clinical documents, it is necessary to de-identify protected health information (PHI) in documents. However, the difficulty lies in the fact that there are few publicly annotated PHI documents. To solve this problem, in this study, we propose a filtered bidirectional encoder representation from transformers (BERT)-based method that predicts a masked word and validates the word again through a similarity filter to construct augmented sentences. The proposed method effectively performs data augmentation. The results show that the augmentation method based on filtered BERT improved the performance of the model. This suggests that our method can effectively improve the performance of the model in the limited data environment.

ObjectivesThis study aimed to develop an effective and efficient obesity treatment and management service platform for obese children/teenagers.MethodsThe integrated smart platform was planned and established through cooperation with service providers such as hospitals and public health centers, obese children/teenagers who constitute the service's user base, and IT development and policy institutions and companies focusing on child-teen obesity management and treatment.ResultsBased on guidelines on intervention strategies to manage child-teen obesity, we developed two patient/parent mobile applications, one web-monitoring service for medical staff, one mobile application for food-craving endurance, and one mobile application for medical examinations.ConclusionsThe establishment of the integrated service platform was successfully completed; however, this study was restrictively to the hospital where the pilot program took place. The effectiveness of the proposed platform will be verified in the future in tests involving other organizations.

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG’s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG’s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.

Over the years, solar collecting systems have gained interest in renewable energy. This study investigated improving the efficiency of the working fluid in thermal solar systems by using nanofluids with three concentrations of alumina, 0.1, 0.3, and 0.5 wt%. The UV-vis absorbance, electronic conductivity, and thermal transfer properties of the nanofluids were analyzed, and the thermal changes with exposure to solar radiation in an experimental collector system were measured by pyranometer. The electronic conductivity, thermal conductivity, and UV-vis absorbance increased with the alumina concentration. Moreover, the temperatures of the nanofluids increased more under solar irradiation than that of distilled water. This implies that the alumina nanofluids absorb solar energy more efficiently than water. The findings of this study suggest that the use of both alumina nanofluids and nanoparticles will improve the efficiency of thermal solar power systems.

ObjectivesThe importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans.MethodsA model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model.ResultsThe rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction.ConclusionsThe accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models.

Decubitus ulcer is a disease that causes the necrosis of skin tissue through the disorder of blood circulation on a certain part of the body where bones protrude. This study is aimed at obtaining an optimal design of a set composed of a rubber air-cell seat cushion and back-supporter, showing even distribution of the interface pressure and minimizing its peak value on the buttocks through optimization using the simulation. The finite element (FE) human model was a human body model developed by the MADYMO (mathematical dynamic model; TASS, Netherlands) and consisted of 44 FE models such as skeletal structures, flesh, muscles, and internal organs. For the optimization of a seat cushion and a back-supporter, a basic model for seat cushion and back-supporter were designed using 50th percentile European male model. To design an optimal seat cushion and back-supporter system, the optimization was performed considering the design factors such as the air-cell height of buttocks and thighs, air-cell height of back-supporter, and angle of back-supporter. The results of the sensitivity analysis confirmed the two design factors (height of buttock air-cell and height of thigh air-cell). The optimization result was 55 mm for the buttock air-cell's height, 60 mm for the thigh air-cell's height, 90 mm for the back-supporter air-cell's height, and 100° for the back-supporter's angle. Based on this study, there is preliminary evidence to show that the design of seat cushion and back-supporter is necessary to reflect biomechanical characteristics, such as body shape (difference of body shape between thigh and buttocks). A main advantage of air-cell type for seat cushion and back-supporter is the good effect of interface pressure distribution.

ObjectivesAnaphora recognition is a process to identify exactly which noun has been used previously and relates to a pronoun that is included in a specific sentence later. Therefore, anaphora recognition is an essential element of a dialogue agent system. In the current study, all the merits of rule-based, machine learning-based, semantic-based anaphora recognition systems were combined to design and realize a new hybrid-type anaphora recognition system with an optimum capacity.MethodsAnaphora recognition rules were encoded on the basis of the internal traits of referred expressions and adjacent contexts to realize a rule-based system and to serve as a baseline. A semantic database, related to predicate instances of sentences including referred expressions, was constructed to identify semantic co-relationships between the referent candidates (to which semantic tags were attached) and the semantic information of predicates. This approach would upgrade the anaphora recognition system by reducing the number of referent candidates. Additionally, to realize a machine learning-based system, an anaphora recognition model was developed on the basis of training data, which indicated referred expressions and referents. The three methods were further combined to develop a new single hybrid-based anaphora recognition system.ResultsThe precision rate of the rule-based systems was 54.9%. However, the precision rate of the hybrid-based system was 63.7%, proving it to be the most efficient method.ConclusionsThe hybrid-based method, developed by the combination of rule-based and machine learning-based methods, represents a new system with enhanced functional capabilities as compared to other pre-existing individual methods.

PurposeTo assess the efficacy of pregabalin by showing differences in the neuronal activities of fibromyalgia (FM) patients before and after longitudinal treatment using functional magnetic resonance imaging (fMRI).Materials and methodsIn total, 21 female patients with FM and 11 age- and gender-matched healthy controls participated. FM patients underwent fMRI at baseline and following pharmacological therapy with pregabalin to diminish their pain. Pressure-pain stimuli were delivered on the subject's thumbnail bed during fMRI scans. Brain activation regions in fMRI were evaluated for longitudinal changes using a paired t-test. Changes in clinical features were also assessed with the Fibromyalgia Impact Questionnaire (FIQ), Brief Fatigue Inventory (BFI), Beck Depression Inventory (BDI), Widespread Pain Index (WPI), Symptom Severity Scale Score (SSS), and State-Trait Anxiety Inventory (STAI).ResultsClinical scores were reduced significantly following therapy with five of the six clinical tests (FIQ, BFI, BDI, WPI, SSS; p < 0.05). Brain activation post-treatment was significantly lower than that pre-treatment in 13 regions of the brain (p < 0.001).ConclusionsOur findings confirm that pregabalin influences aspects of the whole pain matrix, using fMRI, inducing longitudinal changes in neuronal activity during the pain state, and that it reduces pain and other core symptoms of FM. This method could be applied to other longitudinal clinical trials of pharmacological treatments for FM.

Background: Kawasaki disease (KD), an acute febrile illness and systemic vasculitis, is the leading cause of acquired heart disease in children in industrialized countries. KD leads to the development of coronary artery aneurysms (CAA) in affected children, which may persist for months and even years after the acute phase of the disease. There is an unmet need to characterize the immune and pathological mechanisms of the long-term complications of KD. Methods: We examined cardiovascular complications in the Lactobacillus casei cell wall extract (LCWE) mouse model of KD-like vasculitis over 4 months. The long-term immune, pathological, and functional changes occurring in cardiovascular lesions were characterized by histological examination, flow cytometric analysis, immunofluorescent staining of cardiovascular tissues, and transthoracic echocardiogram. Results: CAA and abdominal aorta dilations were detected up to 16 weeks following LCWE injection and initiation of acute vasculitis. We observed alterations in the composition of circulating immune cell profiles, such as increased monocyte frequencies in the acute phase of the disease and higher counts of neutrophils. We determined a positive correlation between circulating neutrophil and inflammatory monocyte counts and the severity of cardiovascular lesions early after LCWE injection. LCWE-induced KD-like vasculitis was associated with myocarditis and myocardial dysfunction, characterized by diminished ejection fraction and left ventricular remodeling, which worsened over time. We observed extensive fibrosis within the inflamed cardiac tissue early in the disease and myocardial fibrosis in later stages. Conclusion: Our findings indicate that increased circulating neutrophil counts in the acute phase are a reliable predictor of cardiovascular inflammation severity in LCWE-injected mice. Furthermore, long-term cardiac complications stemming from inflammatory cell infiltrations in the aortic root and coronary arteries, myocardial dysfunction, and myocardial fibrosis persist over long periods and are still detected up to 16 weeks after LCWE injection. [ABSTRACT FROM AUTHOR]