In Vitro Study of Licorice on IL-1ß-Induced Chondrocytes and In Silico Approach for Osteoarthritis.

Osteoarthritis (OA) is a common degenerative joint disorder that affects joint function, mobility, and pain. The release of proinflammatory cytokines stimulates matrix metalloproteinases (MMPs) and aggrecanase production which further induces articular cartilage degradation. Hypertrophy-like changes in chondrocytes are considered to be an important feature of OA pathogenesis. A Glycyrrhiza new variety, Wongam (WG), was developed by the Korea Rural Development Administration to enhance the cultivation and quality of Glycyrrhizae Radix et Rhizoma (licorice). This study examined the regulatory effect of WG against hypertrophy-like changes such as RUNX2, Collagen X, VEGFA, MMP-13 induction, and Collagen II reduction induced by IL-1ß in SW1353 human chondrocytes. Additionally, in silico methods were performed to identify active compounds in licorice to target chondrocyte hypertrophy-related proteins. WG showed inhibitory effects against IL-1ß-induced chondrocyte hypertrophy by regulating both HDAC4 activation via the PTH1R/PKA/PP2A pathway and the SOX9/ß-catenin signaling pathway. In silico analysis demonstrated that 21 active compounds from licorice have binding potential with 11 targets related to chondrocyte hypertrophy. Further molecular docking analysis and in vivo studies elicited four compounds. Based on HPLC, isoliquiritigenin and its precursors were identified and quantified. Taken together, WG is a potential therapeutic agent for chondrocyte hypertrophy-like changes in OA.

Fatal Systemic Capillary Leak Syndrome after SARS-CoV-2Vaccination in Patient with Multiple Myeloma.

A young man with smoldering multiple myeloma died of hypotensive shock 2.5 days after severe acute respiratory syndrome coronavirus 2 vaccination. Clinical findings suggested systemic capillary leak syndrome (SCLS); the patient had experienced a previous suspected flare episode. History of SCLS may indicate higher risk for SCLS after receiving this vaccine.

Machine-Deep-Ensemble Learning Model for Classifying Cybersickness Caused by Virtual Reality Immersion.

This study aims to classify cybersickness (CS) caused by virtual reality (VR) immersion through a machine-deep-ensemble learning model. The heart rate variability and respiratory signal parameters of 20 subjects were measured, while watching a VR video for ∼5 minutes. After the experiment, the subjects were examined for CS and questioned to determine their CS states. Based on the results, we constructed a machine-deep-ensemble learning model that could identify and classify VR immersion CS among subjects. The ensemble model comprised four stacked machine learning models (support vector machine [SVM], k-nearest neighbor [KNN], random forest, and AdaBoost), which were used to derive prediction data, and then, classified the prediction data using a convolution neural network. This model was a multiclass classification model, allowing us to classify subjects' CS into three states (neutral, non-CS, and CS). The accuracy of SVM, KNN, random forest, and AdaBoost was 94.23 percent, 92.44 percent, 93.20 percent, and 90.33 percent, respectively, and the ensemble model could classify the three states with an accuracy of 96.48 percent. This implied that the ensemble model has a higher classification performance than when each model is used individually. Our results confirm that CS caused by VR immersion can be detected as physiological signal data with high accuracy. Moreover, our proposed model can determine the presence or absence of CS as well as the neutral state. Clinical Trial Registration Number: 20-2021-1.

Production of protein hydrolysate from Protaetia brevitarsis seulensis (Kolbe) larvae by enzyme treatment under high pressure.

To improve the industrial use of health-functional materials based on edible insects, the objective of this study was to establish optimal conditions for improving the quality of Protaetia brevitarsis seulensis larval (PBSL) hydrolysates. PBSL was extracted using four methodologies: atmospheric pressure 50 °C-water extraction, atmospheric pressure 95 °C-water extraction, atmospheric pressure 50 °C-water enzymatic hydrolysis, and enzyme treatment under high pressure (HPE). The quality characteristics of soluble solid content, extraction yield, total protein content, protein yield, protein content with low molecular weight (LMW) (< 1kD), and the amino acid composition of hydrolysates were compared based on the different methods. All of the quality characteristics were found to be higher for HPE extracts than for the other extracts. Under optimized HPE conditions, extraction yield, protein yield, protein content with LMW, amino acid content and the content of essential amino acids increased by 3.4, 4.4 1.4 1.5, and 1.3 times respectively, compared to the other methods.

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals.

This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.

Arousal and Valence Classification Model Based on Long Short-Term Memory and DEAP Data for Mental Healthcare Management.

OBJECTIVES: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. METHODS: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. RESULTS: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. CONCLUSIONS: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring.

OBJECTIVES: Stress management is related to public healthcare and quality of life; an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. METHODS: In this paper, we present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data. Statistical analysis was performed to identify the nine features of stress detection, and we evaluated the performance of the proposed stress classification by comparison with several stress detection models. The proposed model was also evaluated using Deep Belief Networks (DBN). RESULTS: We designed profiles depending on the number of hidden layers, nodes, and hyper-parameters according to the loss function results. The experimental results showed that the proposed model achieved an accuracy and a specificity of 66.23% and 75.32%, respectively. The proposed DBN model performed better than other classification models, such as support vector machine, naive Bayesian classifier, and random forest. CONCLUSIONS: The proposed model in this study was demonstrated to be effective in classifying stress detection, and in particular, it is expected to be applicable for stress prediction in stress monitoring systems.

Dilated cardiomyopathy secondary to hypothyroidism: case report with a review of literatures.

To date, it has been well documented that there is a relationship between alterations in thyroid hormones and cardiac dysfunction. We experienced a case of a 36-year-old man with dilated cardiomyopathy (DCM) accompanied by undiagnosed primary hypothyroidism. In the current case, there was a significant improvement in the cardiac function following heart failure management and thyroid hormone replacement. Our case highlights that clinicians should consider the possibility of hypothyroidism as a cause of DCM.

Estimation of the properties of silver nanoparticle ink during laser sintering via in-situ electrical resistance measurement.

In this work, the in-situ properties of silver nanoparticle ink were estimated during laser sintering process. The silver nanoparticle ink was composed of 34 wt% silver nanoparticles with an average size of approximately 50 nm, and was deposited on a glass substrate via inkjet printing technology. A 532 nm continuous-wave laser was irradiated to the printed ink for 60 s under various laser intensities. During the laser irradiation, the in-situ electrical conductance of the sintered ink was measured to obtain the transient thermal conductivity of the silver nanoparticle ink using the Wiedemann Franz law. The 2-dimensional, transient heat-conduction equation was calculated to obtain the transient temperature of the silver nanoparticle ink. By coupling the calculated temperature with the measured, transient electrical conductance, the transient thermal conductivity of the ink during the laser sintering process was derived in the calculation. The calculated thermal conductivity of the ink sintered at a laser intensity of 467.9 W/cm2 with 598 K is 355.5 W/mK, which is 86.4% of the thermal conductivity of bulk silver, 411.4 W/mK, at that tempearture. The difference resulting from the porosity of the sintered ink has an effect on the thermal conductivity of the sintered ink.

A Case of Myopathy, Encephalopathy, Lactic Acidosis and Stroke-Like Episodes (MELAS) Syndrome with Intracardiac Thrombus [corrected].

Myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) is a multisystem clinical syndrome manifested by mitochondrial myopathy, encephalopathy, lactic acidosis and recurrent stroke-like episodes. A 27-year-old female with MELAS syndrome presented with cerebral infarction. Echocardiography revealed a thrombus attached to the apex of the hypertrophied left ventricle, with decreased systolic function. The embolism of the intracardiac thrombus might have been the cause of stroke. There should be more consideration given to the increased possibility of intracardiac thrombus formation when a MELAS patient with cardiac involvement is encountered.

A mobile tele-radiology imaging system with JPEG2000 for an emergency care.

The aim of this study was to design a tele-radiology imaging system for rapid emergency care via mobile networks and to assess the diagnostic feasibility of the Joint Photographic Experts Group 2000 (JPEG2000) radiological imaging using portable devices. Rapid patient information and image exchange is helpful to make clinical decisions. We assessed the usefulness of the mobile tele-radiology system by measuring both a quantitative method, PNSR calculation, for image qualities, and its transmission time via mobile networks in different mobile networks, respectively; code division multiple access evolution-data optimized, wireless broadband, and high-speed downlink packet access; and the feasibility of the JPEG2000 computed tomography (CT) images by qualitatively assessing with the Alberta stroke program early CT score method with 12 CT image cases (seven normal and five abnormal cases). We found that the quality of the JPEG2000 radiological images was satisfied quantitatively and was judged as acceptable qualitatively at 5:1 and 10:1 compression levels for the mobile tele-radiology imaging system. The JPEG2000-format radiological images achieved a fast transmission while maintaining a diagnosis quality on a portable device via mobile networks. Unfortunately, a PDA device, having a limited screen resolution, posed difficulties in reviewing the JPEG2000 images regardless of the compression levels. An ultra mobile PC was preferable to study the medical image. The mobile tele-radiology imaging systems supporting JPEG2000 image transmission can be applied to actual emergency care services under mobile computing environments.

A portable dental image viewer using a mobile network to provide a tele-dental service.

We tested three imaging devices for suitability in emergency dental situations for telemedicine. The three devices were a special-purpose oral camera, a digital single lens reflex (DSLR) camera, and the built-in camera of a mobile phone. A total of 20 subjects volunteered to take part in the study. We simulated five different conditions which could affect the quality of the image and measured image transmission times across two widely available mobile networks (a CDMA-based 3G network and a Wireless Broadband Network, WiBRO). The DSLR produced the best quality images, although it was a relatively large device and not easy to use. The oral camera failed to give satisfactory images under certain conditions (i.e. without extra lighting, out of focus and head motion). In contrast, the mobile phone based camera was very easy to use and to handle, and it gave good information for initial diagnosis, even at high compression ratios. If the image acquisition conditions are carefully set up, this device might be the best for emergency dental trauma situations.

A mobile telemedicine system for remote consultation in cases of acute stroke.

A mobile telemedicine system, capable of transmitting video and audio simultaneously, was designed for consulting acute stroke patients remotely. It could use a wireless local area network (e.g. inside the hospital) or a mobile phone network (e.g. outside the hospital). When initiating a call, the sending unit chose a suitable encoding profile based on the measured data throughput, in order to allocate appropriate bit rates for video and audio transmission. The system was tested using a portable digital assistant (PDA) type phone and smart phone as receiving units. Video and audio recordings were made from five patients (two normal and three stroke patients) and then transmitted at different rates. Subjectively, both video and audio qualities improved as the data throughput increased. The physical findings, including facial droop, arm drift and abnormal speech, were observed remotely by four specialists according to the Cincinnati Pre-hospital Stroke Scale guideline. A comparison between the face-to-face method and the mobile telemedicine method showed that there were no discrepancies at bit rates of more than 400 kbit/s. We conclude that specialists could generally conduct remote consultations for stroke patients either using a public mobile network or a wireless LAN.

Implementation of a large-scale hospital information infrastructure for multi-unit health-care services.

With the increase in demand for high quality medical services, the need for an innovative hospital information system has become essential. An improved system has been implemented in all hospital units of the Yonsei University Health System. Interoperability between multi-units required appropriate hardware infrastructure and software architecture. This large-scale hospital information system encompassed PACS (Picture Archiving and Communications Systems), EMR (Electronic Medical Records) and ERP (Enterprise Resource Planning). It involved two tertiary hospitals and 50 community hospitals. The monthly data production rate by the integrated hospital information system is about 1.8 TByte and the total quantity of data produced so far is about 60 TByte. Large scale information exchange and sharing will be particularly useful for telemedicine applications.

PDA-phone-based instant transmission of radiological images over a CDMA network by combining the PACS screen with a Bluetooth-interfaced local wireless link.

Remote teleconsultation by specialists is important for timely, correct, and specialized emergency surgical and medical decision making. In this paper, we designed a new personal digital assistant (PDA)-phone-based emergency teleradiology system by combining cellular communication with Bluetooth-interfaced local wireless links. The mobility and portability resulting from the use of PDAs and wireless communication can provide a more effective means of emergency teleconsultation without requiring the user to be limited to a fixed location. Moreover, it enables synchronized radiological image sharing between the attending physician in the emergency room and the remote specialist on picture archiving and communication system terminals without distorted image acquisition. To enable rapid and fine-quality radiological image transmission over a cellular network in a secure manner, progressive compression and security mechanisms have been incorporated. The proposed system is tested over a code division Multiple Access 1x-Evolution Data-Only network to evaluate the performance and to demonstrate the feasibility of this system in a real-world setting.

Instant wireless transmission of radiological images using a personal digital assistant phone for emergency teleconsultation.

The instant transmission of radiological images may be important for making rapid clinical decisions about emergency patients. We have examined an instant image transfer system based on a personal digital assistant (PDA) phone with a built-in camera. Images displayed on a picture archiving and communication systems (PACS) monitor can be captured by the camera in the PDA phone directly. Images can then be transmitted from an emergency centre to a remote physician via a wireless high-bandwidth network (CDMA 1 x EVDO). We reviewed the radiological lesions in 10 normal and 10 abnormal cases produced by modalities such as computerized tomography (CT), magnetic resonance (MR) and digital angiography. The images were of 24-bit depth and 1,144 x 880, 1,120 x 840, 1,024 x 768, 800 x 600, 640 x 480 and 320 x 240 pixels. Three neurosurgeons found that for satisfactory remote consultation a minimum size of 640 x 480 pixels was required for CT and MR images and 1,024 x 768 pixels for angiography images. Although higher resolution produced higher clinical satisfaction, it also required more transmission time. At the limited bandwidth employed, higher resolutions could not be justified.

Evaluation of compressed video-images for emergency telemedicine work with trauma patients.

Video-encoders in telemedicine systems need to adjust their coding methods for operation on heterogeneous networks on which bandwidth fluctuates. We evaluated MPEG-4 compressed video-pictures of three trauma patients. We compared the original video-frames with compressed video-frames in terms of the peak signal-to-noise ratio (PSNR). In a qualitative evaluation, three emergency specialists scored the quality of the video-images blind, on a five-point scale (1= bad to 5 = excellent). The PSNR increased as the bit rate increased from 0.2 to 6 Mbit/s. When the bit rate was fixed, in other words at a given network bandwidth, a higher PSNR was obtained at the expense of spatial resolution and frame rate. The video quality was highly affected by the amount of camera shake. Emergency telemedicine systems require a high bit rate, high spatial resolution and a high frame rate to achieve optimum video quality. However, if the bandwidth is limited (i.e. the bit rate is fixed), temporal resolution becomes more important than spatial resolution.

Evaluation of two mobile telemedicine systems in the emergency room.

Two different prototype mobile telemedicine systems were constructed for use in the emergency room. They could transmit physiological signals as well as video pictures and sound. One device, the mobile emergency bed (MEB), was powered by battery and had a wireless connection to the local-area network (LAN). For the other, the mobile emergency server (MES), a patient monitor, video-camera and microphone were connected by a radio-frequency link to a server. A functional evaluation and a clinical evaluation (by 12 emergency doctors in six emergency centres) were performed on both prototypes. The bandwidth and the video quality of the MEB were better than those of the MES, because of the digital transmission of the wireless LAN. The MES was better for directing patient treatment and teleconsultation; the MEB was better for static patients in the emergency centre. In general, the MES was more suitable for practical emergency telemedicine work.