Prevalence and outcomes of chronic comorbid conditions in patients with sepsis in Korea: a nationwide cohort study from 2011 to 2016.

BACKGROUND: Chronic comorbid conditions are common in patients with sepsis and may affect the outcomes. This study aimed to evaluate the prevalence and outcomes of common comorbidities in patients with sepsis. METHODS: We conducted a nationwide retrospective cohort study. Using data from the National Health Insurance Service of Korea. Adult patients (age ≥ 18 years) who were hospitalized in tertiary or general hospitals with a diagnosis of sepsis between 2011 and 2016 were analyzed. After screening of all International Classification of Diseases 10th revision codes for comorbidities, we identified hypertension, diabetes mellitus (DM), liver cirrhosis (LC), chronic kidney disease (CKD), and malignancy as prevalent comorbidities. RESULTS: Overall, 373,539 patients diagnosed with sepsis were hospitalized in Korea between 2011 and 2016. Among them, 46.7% had hypertension, 23.6% had DM, 7.4% had LC, 13.7% had CKD, and 30.7% had malignancy. In-hospital mortality rates for patients with hypertension, DM, LC, CKD, and malignancy were 25.5%, 25.2%, 34.5%, 28.0%, and 33.3%, respectively, showing a decreasing trend over time (P < 0.001). After adjusting for baseline characteristics, male sex, older age, use of mechanical ventilation, and continuous renal replacement therapy, LC, CKD, and malignancy were significantly associated with in-hospital mortality. CONCLUSIONS: Hypertension is the most prevalent comorbidity in patients with sepsis, and it is associated with an increased survival rate. Additionally, liver cirrhosis, chronic kidney disease, and malignancy result in higher mortality rates than hypertension and DM, and are significant risk factors for in-hospital mortality in patients with sepsis.

Hexane Fraction of Adenophora triphylla var. japonica Root Extract Inhibits Angiogenesis and Endothelial Cell-Induced Erlotinib Resistance in Lung Cancer Cells.

The aim of this study was to investigate the anti-angiogenic effects of the hexane fraction of Adenophora triphylla var. japonica root extract (HAT) and its influence on the development of erlotinib resistance in human lung cancer cells. HAT significantly reduced the migration, invasion, and tube formation of human umbilical vein endothelial cells (HUVECs). The phosphorylation levels of vascular endothelial growth factor receptor 2 (VEGFR2) and its downstream molecules were decreased via HAT, indicating its anti-angiogenic potential in endothelial cells (ECs). A docking analysis demonstrated that ß-sitosterol and lupeol, representative components of HAT, exhibit a high affinity for binding to VEGFR2. In addition, conditioned media from HAT-pretreated H1299 human lung cancer cells attenuated cancer-cell-induced chemotaxis of HUVECs, which was attributed to the decreased expression of angiogenic and chemotactic factors in H1299 cells. Interestingly, co-culture of erlotinib-sensitive PC9 human lung cancer cells with HUVECs induced erlotinib resistance in PC9 cells. However, co-culture with HAT-pretreated HUVECs partially restored the sensitivity of PC9 cells to erlotinib. HAT inhibited the development of erlotinib resistance by attenuating hepatocyte growth factor (HGF) production by ECs. Taken together, our results demonstrate that HAT exerts its anticancer effects by regulating the crosstalk between ECs and lung cancer cells.

Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study.

BACKGROUND : Deep learning models have previously been established to predict the histopathology and invasion depth of gastric lesions using endoscopic images. This study aimed to establish and validate a deep learning-based clinical decision support system (CDSS) for the automated detection and classification (diagnosis and invasion depth prediction) of gastric neoplasms in real-time endoscopy. METHODS : The same 5017 endoscopic images that were employed to establish previous models were used for the training data. The primary outcomes were: (i) the lesion detection rate for the detection model, and (ii) the lesion classification accuracy for the classification model. For performance validation of the lesion detection model, 2524 real-time procedures were tested in a randomized pilot study. Consecutive patients were allocated either to CDSS-assisted or conventional screening endoscopy. The lesion detection rate was compared between the groups. For performance validation of the lesion classification model, a prospective multicenter external test was conducted using 3976 novel images from five institutions. RESULTS : The lesion detection rate was 95.6 % (internal test). On performance validation, CDSS-assisted endoscopy showed a higher lesion detection rate than conventional screening endoscopy, although statistically not significant (2.0 % vs. 1.3 %; P = 0.21) (randomized study). The lesion classification rate was 89.7 % in the four-class classification (advanced gastric cancer, early gastric cancer, dysplasia, and non-neoplastic) and 89.2 % in the invasion depth prediction (mucosa confined or submucosa invaded; internal test). On performance validation, the CDSS reached 81.5 % accuracy in the four-class classification and 86.4 % accuracy in the binary classification (prospective multicenter external test). CONCLUSIONS : The CDSS demonstrated its potential for real-life clinical application and high performance in terms of lesion detection and classification of detected lesions in the stomach.

Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study.

BACKGROUND: Our research group previously established a deep-learning-based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis. OBJECTIVE: This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM. METHODS: A total of 11,868 endoscopic images were used for training and internal testing. The primary outcomes were lesion classification accuracy (6 classes: advanced gastric cancer, early gastric cancer, dysplasia, atrophy, IM, and normal) and atrophy and IM lesion segmentation rates for the segmentation model. The following tests were carried out to validate the performance of lesion classification accuracy: (1) external testing using 1282 images from another institution and (2) evaluation of the classification accuracy of atrophy and IM in real-world procedures in a prospective manner. To estimate the clinical utility, 2 experienced endoscopists were invited to perform a blind test with the same data set. A CDSS was constructed by combining the established 6-class lesion classification model and the preneoplastic lesion segmentation model with the previously established lesion detection model. RESULTS: The overall lesion classification accuracy (95% CI) was 90.3% (89%-91.6%) in the internal test. For the performance validation, the CDSS achieved 85.3% (83.4%-97.2%) overall accuracy. The per-class external test accuracies for atrophy and IM were 95.3% (92.6%-98%) and 89.3% (85.4%-93.2%), respectively. CDSS-assisted endoscopy showed an accuracy of 92.1% (88.8%-95.4%) for atrophy and 95.5% (92%-99%) for IM in the real-world application of 522 consecutive screening endoscopies. There was no significant difference in the overall accuracy between the invited endoscopists and established CDSS in the prospective real-clinic evaluation (P=.23). The CDSS demonstrated a segmentation rate of 93.4% (95% CI 92.4%-94.4%) for atrophy or IM lesion segmentation in the internal testing. CONCLUSIONS: The CDSS achieved high performance in terms of computer-aided diagnosis of all stages of gastric carcinogenesis and demonstrated real-world application potential.

A Study on Wheel Member Condition Recognition Using 1D-CNN.

The condition of a railway vehicle's wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle's wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D-CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate.

A Study on Wheel Member Condition Recognition Using Machine Learning (Support Vector Machine).

The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%.

An Ethanol Extract of Perilla frutescens Leaves Suppresses Adrenergic Agonist-Induced Metastatic Ability of Cancer Cells by Inhibiting Src-Mediated EMT.

Previous studies have indicated that the adrenergic receptor signaling pathway plays a fundamental role in chronic stress-induced cancer metastasis. In this study, we investigated whether an ethanol extract of Perilla frutescens leaves (EPF) traditionally used to treat stress-related symptoms by moving Qi could regulate the adrenergic agonist-induced metastatic ability of cancer cells. Our results show that adrenergic agonists including norepinephrine (NE), epinephrine (E), and isoproterenol (ISO) increased migration and invasion of MDA-MB-231 human breast cancer cells and Hep3B human hepatocellular carcinoma cells. However, such increases were completely abrogated by EPF treatment. E/NE induced downregulation of E-cadherin and upregulation of N-cadherin, Snail, and Slug. Such effects were clearly reversed by pretreatment with EPF, suggesting that the antimetastatic activity of EPF could be related to epithelial-mesenchymal transition (EMT) regulation. EPF suppressed E/NE-stimulated Src phosphorylation. Inhibition of Src kinase activity with dasatinib completely suppressed the E/NE-induced EMT process. Transfecting MDA-MB-231 cells with constitutively activated Src (SrcY527F) diminished the antimigration effect of EPF. Taken together, our results demonstrate that EPF can suppress the adrenergic agonist-promoted metastatic ability of cancer cells by inhibiting Src-mediated EMT. This study provides basic evidence supporting the probable use of EPF to prevent metastasis in cancer patients, especially those under chronic stress.

Clozapine Blood Concentration Predicts Corrected QT-Interval Prolongation in Patients With Psychoses.

BACKGROUND: Corrected QT-interval (QTc) prolongation (QTP) is a rare but fatal adverse effect of antipsychotics. Clozapine is the only antipsychotic recommended for treatment of resistant schizophrenia; however, clozapine has been reported to cause QTP. We sought factors predictive of QTP in patients who had antipsychotic polypharmacy involving clozapine. We explored whether the clozapine blood concentration might predict QTP. METHODS: We included 133 patients with schizophrenia spectrum disorder who had antipsychotic polypharmacy involving clozapine. We used the χ2 and nonparametric tests to compare clozapine therapeutic drug monitoring (TDM) values and QTc-prolonged person (QTPP) status. Multivariate regression and mediator models were used to identify risk factors for QTPP status and QTP. RESULTS: In total, 111 patients were prescribed clozapine. The QTPP rates were 31.3% (20) for men and 23.2% (16) for women. Compared with the non-QTPP group, the QTPP group exhibited significantly higher daily dose of all antipsychotics including clozapine, a higher clozapine dose, and elevated clozapine and norclozapine TDM values. Furthermore, such patients were prescribed a greater number of antipsychotics. Multivariate logistic regression revealed that only the clozapine TDM value could be predictive factor for QTPP status (P = 0.018). A clozapine TDM value above the therapeutic range (>600 mg/dL) was associated with a high risk of QTPP status (adjusted odds ratio, 6.5; 95% confidence interval, 1.7-25.2; P = 0.006). The mediator model revealed that the clozapine TDM values completely mediated the association between the clozapine dose and the QTc interval. CONCLUSIONS: The clozapine blood concentration reliably predicts QTP in patients with clozapine use.

Mechanism mediated by a noncoding RNA, nc886, in the cytotoxicity of a DNA-reactive compound.

DNA-reactive compounds are harnessed for cancer chemotherapy. Their genotoxic effects are considered to be the main mechanism for the cytotoxicity to date. Because this mechanism preferentially affects actively proliferating cells, it is postulated that the cytotoxicity is specific to cancer cells. Nonetheless, they do harm normal quiescent cells, suggesting that there are other cytotoxic mechanisms to be uncovered. By employing doxorubicin as a representative DNA-reactive compound, we have discovered a cytotoxic mechanism that involves a cellular noncoding RNA (ncRNA) nc886 and protein kinase R (PKR) that is a proapoptotic protein. nc886 is transcribed by RNA polymerase III (Pol III), binds to PKR, and prevents it from aberrant activation in most normal cells. We have shown here that doxorubicin evicts Pol III from DNA and, thereby, shuts down nc886 transcription. Consequently, the instantaneous depletion of nc886 provokes PKR and leads to apoptosis. In a short-pulse treatment of doxorubicin, these events are the main cause of cytotoxicity preceding the DNA damage response in a 3D culture system as well as the monolayer cultures. By identifying nc886 as a molecular signal for PKR to sense doxorubicin, we have provided an explanation for the conundrum why DNA-damaging drugs can be cytotoxic to quiescent cells that have the competent nc886/PKR pathway.

A Study on Object Detection Performance of YOLOv4 for Autonomous Driving of Tram.

Recently, autonomous driving technology has been in the spotlight. However, autonomous driving is still in its infancy in the railway industry. In the case of railways, there are fewer control elements than autonomous driving of cars due to the characteristics of running on railways, but there is a disadvantage in that evasive maneuvers cannot be made in the event of a dangerous situation. In addition, when braking, it cannot be decelerated quickly for the weight of the body and the safety of the passengers. In the case of a tram, one of the railway systems, research has already been conducted on how to generate a profile that plans braking and acceleration as a base technology for autonomous driving, and to find the location coordinates of surrounding objects through object recognition. In pilot research about the tram's automated driving, YOLOv3 was used for object detection to find object coordinates. YOLOv3 is an artificial intelligence model that finds coordinates, sizes, and classes of objects in an image. YOLOv3 is the third upgrade of YOLO, which is one of the most famous object detection technologies based on CNN. YOLO's object detection performance is characterized by ordinary accuracy and fast speed. For this paper, we conducted a study to find out whether the object detection performance required for autonomous trams can be sufficiently implemented with the already developed object detection model. For this experiment, we used the YOLOv4 which is the fourth upgrade of YOLO.

Determination of Traffic Lane in Tunnel and Positioning of Autonomous Vehicles Using Chromaticity of LED Lights.

Currently, the location recognition and positioning system are the essential parts of unmanned vehicles. Among them, location estimation under GPS-denied environments is currently being studied using IMU, Wi-Fi, and VLC, but there are problems such as cumulative errors, hardware complexity, and precision positioning. To address this problem with the current positioning system, the present study proposed a lane positioning technique by analyzing the chromaticity coordinates, judging from the color temperature of LED lights in tunnels. The tunnel environment was built using LEDs with three color temperatures, and to solve nonlinear problems such as lane positioning from chromaticity analysis, a single input single output fuzzy algorithm was developed to estimate the position of an object on lanes using chromaticity values of signals measured by RGB sensors. The RGB value measured by the sensor removes the disturbance through the pre-processing filter, accepts only the tunnel LED information, and estimates where it is located on the x-distance indicating the lane position through a fuzzy algorithm. Finally, the performance of the fuzzy algorithm was evaluated through experiments, and the accuracy was shown with an average error of less than 4.86%.

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures.

The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN's object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations-the first air delivery service by drones in Korea.

The Root Extract of Peucedanum praeruptorum Dunn Exerts Anticancer Effects in Human Non-Small-Cell Lung Cancer Cells with Different EGFR Mutation Statuses by Suppressing MET Activity.

The aim of this study was to investigate the anticancer effects of the root extract of Peucedanum praeruptorum Dunn (EPP) in human non-small-cell lung cancer (NSCLC) cells and explore the mechanisms of action. We used four types of human lung cancer cell lines, including H1299 (epidermal growth factor receptor (EGFR) wild-type), PC9 (EGFR Glu746-Ala750 deletion mutation in exon 19; EGFR tyrosine kinase inhibitor (TKI)-sensitive), H1975 (EGFR L858R/T790M double-mutant; EGFR TKI-resistant), and PC9/ER (erlotinib-resistant) cells. EPP suppressed cell growth and the colony formation of NSCLC cells in a concentration-dependent manner. EPP stimulated chromatin condensation, increased the percentage of sub-G1 phase cells, and enhanced the proportion of annexin V-positive cells, demonstrating that EPP triggered apoptosis in NSCLC cells regardless of the EGFR mutation and EGFR TKI resistance status. The phosphorylation level of the signal transducer and activator of transcription 3 (STAT3) and AKT was decreased by EPP. The expression of STAT3 target genes was also downregulated by EPP. EPP reversed hepatocyte growth factor (HGF)-induced MET phosphorylation and gefitinib resistance. Taken together, our results demonstrate that EPP exerted anticancer effects not only in EGFR TKI-sensitive NSCLC cells, but also in EGFR TKI-resistant NSCLC cells, by suppressing MET activity.

ß2-Adrenergic Receptor Signaling Pathway Stimulates the Migration and Invasion of Cancer Cells via Src Activation.

Chronic stress has been reported to stimulate the release of catecholamines, including norepinephrine (NE) and epinephrine (E), which promote cancer progression by activating the adrenergic receptor (AR). Although previous studies showed that ß2-AR mediated chronic stress-induced tumor growth and metastasis, the underlying mechanism has not been fully explored. In this study, we aimed to investigate the molecular mechanism by which ß2-AR exerts a pro-metastatic function in hepatocarcinoma (HCC) cells and breast cancer (BC) cells. Our results showed that Hep3B human HCC cells and MDA-MB-231 human BC cells exhibited the highest ADRB2 expression among diverse HCC and BC cell lines. NE, E, and isoprenaline (ISO), adrenergic agonists commonly increased the migration and invasion of Hep3B cells and MDA-MB-231 cells. The phosphorylation level of Src was significantly increased by E/NE. Dasatinib, a Src kinase inhibitor, blocked E/NE-induced migration and invasion, indicating that AR agonists enhanced the mobility of cancer cells by activating Src. ADRB2 knockdown attenuated E/NE-induced Src phosphorylation, as well as the metastatic ability of cancer cells, suggesting the essential role of ß2-AR. Taken together, our results demonstrate that chronic stress-released catecholamines promoted the migration and invasion of HCC cells and BC cells via ß2-AR-mediated Src activation.

The Safety of Operating on Breasts With a History of Prior Reduction Mammoplasty: Dynamic Magnetic Resonance Imaging Analysis of Angiogenesis.

BACKGROUND: The vascularity of the nipple-areolar complex (NAC) is altered after reduction mammoplasty, increasing the risk of complications after repeat reduction or nipple-sparing mastectomy. OBJECTIVES: The aim of this study was to evaluate angiogenesis of the NAC via serial analysis of magnetic resonance images. METHODS: Magnetic resonance images of breasts after reduction mammoplasty were analyzed for 35 patients (39 breasts) from 3-dimensional reconstructions of maximum-intensity projection images. All veins terminating at the NAC were classified as internal mammary, anterior intercostal, or lateral thoracic in origin. The vein with the largest diameter was considered the dominant vein. Images were classified based on the time since reduction: <6 months, 6 to 12 months, 12 to 24 months, >2 years. RESULTS: The average number of veins increased over time: 1.17 (<6 months), 1.56 (6-12 months), 1.64 (12-24 months), 1.73 (>2 years). Within 6 months, the pedicle was the only vein. Veins from other sources began to appear at 6 to 12 months. In most patients, at least 2 veins were available after 1 year. After 1 year, the internal mammary vein was the most common dominant vein regardless of the pedicle used. CONCLUSIONS: Repeat reduction mammoplasty or nipple-sparing mastectomy should be performed ≥1 year following the initial procedure. After 1 year, the superior or superomedial pedicle may represent the safest option when the previous pedicle is unknown.

Activation of temperature-sensitive TRPV1-like receptors in ARC POMC neurons reduces food intake.

Proopiomelanocortin (POMC) neurons in the arcuate nucleus of the hypothalamus (ARC) respond to numerous hormonal and neural signals, resulting in changes in food intake. Here, we demonstrate that ARC POMC neurons express capsaicin-sensitive transient receptor potential vanilloid 1 receptor (TRPV1)-like receptors. To show expression of TRPV1-like receptors in ARC POMC neurons, we use single-cell reverse transcription-polymerase chain reaction (RT-PCR), immunohistochemistry, electrophysiology, TRPV1 knock-out (KO), and TRPV1-Cre knock-in mice. A small elevation of temperature in the physiological range is enough to depolarize ARC POMC neurons. This depolarization is blocked by the TRPV1 receptor antagonist and by Trpv1 gene knockdown. Capsaicin-induced activation reduces food intake that is abolished by a melanocortin receptor antagonist. To selectively stimulate TRPV1-like receptor-expressing ARC POMC neurons in the ARC, we generate an adeno-associated virus serotype 5 (AAV5) carrying a Cre-dependent channelrhodopsin-2 (ChR2)-enhanced yellow fluorescent protein (eYFP) expression cassette under the control of the two neuronal POMC enhancers (nPEs). Optogenetic stimulation of TRPV1-like receptor-expressing POMC neurons decreases food intake. Hypothalamic temperature is rapidly elevated and reaches to approximately 39 °C during treadmill running. This elevation is associated with a reduction in food intake. Knockdown of the Trpv1 gene exclusively in ARC POMC neurons blocks the feeding inhibition produced by increased hypothalamic temperature. Taken together, our findings identify a melanocortinergic circuit that links acute elevations in hypothalamic temperature with acute reductions in food intake.

CTCF cooperates with CtIP to drive homologous recombination repair of double-strand breaks.

The pleiotropic CCCTC-binding factor (CTCF) plays a role in homologous recombination (HR) repair of DNA double-strand breaks (DSBs). However, the precise mechanistic role of CTCF in HR remains largely unclear. Here, we show that CTCF engages in DNA end resection, which is the initial, crucial step in HR, through its interactions with MRE11 and CtIP. Depletion of CTCF profoundly impairs HR and attenuates CtIP recruitment at DSBs. CTCF physically interacts with MRE11 and CtIP and promotes CtIP recruitment to sites of DNA damage. Subsequently, CTCF facilitates DNA end resection to allow HR, in conjunction with MRE11-CtIP. Notably, the zinc finger domain of CTCF binds to both MRE11 and CtIP and enables proficient CtIP recruitment, DNA end resection and HR. The N-terminus of CTCF is able to bind to only MRE11 and its C-terminus is incapable of binding to MRE11 and CtIP, thereby resulting in compromised CtIP recruitment, DSB resection and HR. Overall, this suggests an important function of CTCF in DNA end resection through the recruitment of CtIP at DSBs. Collectively, our findings identify a critical role of CTCF at the first control point in selecting the HR repair pathway.

Numerical Analysis of 2-D Positioned, Indoor, Fuzzy-Logic, Autonomous Navigation System Based on Chromaticity and Frequency-Component Analysis of LED Light.

Topics concerning autonomous navigation, especially those related to positioning systems, have recently attracted increased research attention. The commonly available global positioning system (GPS) is unable to determine the positions of vehicles in GPS-shaded regions. To address this concern, this paper presents a fuzzy-logic system capable of determining the position of a moving robot in a GPS-shaded indoor environment by analyzing the chromaticity and frequency-component ratio of LED lights installed under the ceiling. The proposed system's performance was analyzed by performing a MATLAB simulation of an indoor environment with obstacles. During the simulation, the mobile robot utilized a fuzzy autonomous navigation system with behavioral rules to approach targets successfully in a variety of indoor environments without colliding with obstacles. The robot utilized the x and y coordinates of the fuzzy positioning system. The results obtained in this study confirm the suitability of the proposed method for use in applications involving autonomous navigation of vehicles in areas with poor GPS-signal reception, such as in tunnels.

A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags.

The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in "past information" through LSTM by regarding the driver motion as time-series data.

Comparative Proteomic Analysis of the Mesenchymal Stem Cells Secretome from Adipose, Bone Marrow, Placenta and Wharton's Jelly.

Mesenchymal stem cells (MSCs) have the potential to be a viable therapy against various diseases due to their paracrine effects, such as secretion of immunomodulatory, trophic and protective factors. These cells are known to be distributed within various organs and tissues. Although they possess the same characteristics, MSCs from different sources are believed to have different secretion potentials and patterns, which may influence their therapeutic effects in disease environments. We characterized the protein secretome of adipose (AD), bone marrow (BM), placenta (PL), and Wharton's jelly (WJ)-derived human MSCs by using conditioned media and analyzing the secretome by mass spectrometry and follow-up bioinformatics. Each MSC secretome profile had distinct characteristics depending on the source. However, the functional analyses of the secretome from different sources showed that they share similar characteristics, such as cell migration and negative regulation of programmed cell death, even though differences in the composition of the secretome exist. This study shows that the secretome of fetal-derived MSCs, such as PL and WJ, had a more diverse composition than that of AD and BM-derived MSCs, and it was assumed that their therapeutic potential was greater because of these properties.