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Uranium (U) is one of the typical naturally occurring radioactive elements enriched in groundwater through geological mechanisms, thereby bringing about adverse effects on human health. For this reason, some countries and the World Health Organization (WHO) regulate U with drinking water standards and monitor its status in groundwater. In Korea, there have been continuous investigations to monitor and manage U in groundwater, but they have targeted only public groundwater wells. However, the features of private wells differ from public ones, particularly in regard to the well’s depth and diameter, affecting the U distribution in private wells. This study was initiated to investigate U concentrations in private groundwater wells for potable use, and the significant factors controlling them were also elucidated through statistical methods. The results obtained from the analyses of 7036 groundwater samples from private wells showed that the highest, average, and median values of U concentrations were 1450, 0.4, and 4.0 μg/L, respectively, and 2.1% of the wells had U concentrations exceeding the Korean and WHO standard (30 μg/L). In addition, the U concentrations were highest in areas of the Jurassic granite, followed by Quaternary alluvium and Precambrian metamorphic rocks. A more detailed investigation of the relationship between U concentration and geology revealed that the Jurassic porphyritic granite, mainly composed of Daebo granite, showed the highest U contents, which indicated that U might originate from uraninite (UO2) and coffinite (USiO4). Consequently, significant caution should be exercised when using the groundwater in these geological areas for potable use. The results of this study might be applied to establish relevant management plans to protect human health from the detrimental effect of U in groundwater.

Soil health deteriorates through the contamination and remediation processes, resulting in the limitation of the reuse and recycling of the remediated soils. Therefore, soil health should be recovered for the intended purposes of reuse and recycling. This study aimed to evaluate the applicability and effectiveness of several amendments to revitalize total petroleum hydrocarbon contaminated soils remediated by the landfarming process. Ten inorganic, organic, and biological amendments were investigated for their dosage and duration, and nine physicochemical, four fertility, and seven microbial (soil enzyme activity) factors were compared before and after the treatment of amendments. Finally, the extent of recovery was quantitatively estimated, and the significance of results was confirmed with statistical methods, such as simple regression and correlation analyses assisted by principal component analysis. The landfarming process is considered a somewhat environmentally friendly remediation technology to minimize the adverse effect on soil quality, but four soil properties—such as water holding capacity (WHC), exchangeable potassium (Ex. K), nitrate-nitrogen (NO3-N), available phosphorus (Av. P), and urease—were confirmed to deteriorate through the landfarming process. The WHC was better improved by organic agents, such as peat moss, biochar, and compost. Zeolite was evaluated as the most effective material for improving Ex. K content. The vermicompost showed the highest efficacy in recovering the NO3-N content of the remediated soil. Chlorella, vermicompost, and compost were investigated for their ability to enhance urease activity effectively. Although each additive showed different effectiveness according to different soil properties, their effect on overall soil properties should be considered for cost-effectiveness and practical implementation. Their overall effect was evaluated using statistical methods, and the results showed that compost, chlorella, and vermicompost were the most relevant amendments for rehabilitating the overall health of the remediated soil for the reuse and/or recycling of agricultural purposes. This study highlighted how to practically improve the health of remediated soils for the reuse and recycling of agricultural purposes.

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

ABSTRACTAlthough Argonaute (AGO) proteins have been the focus of microRNA (miRNA) studies, we observed AGO-free mature miRNAs directly interacting with RNA-binding proteins, implying the sophisticated nature of fine-tuning gene regulation by miRNAs. To investigate microRNA-binding proteins (miRBPs) globally, we analyzed PAR-CLIP data sets to identify RBP quaking (QKI) as a novel miRBP for let-7b. Potential existence of AGO-free miRNAs were further verified by measuring miRNA levels in genetically engineered AGO-depleted human and mouse cells. We have shown that QKI regulates miRNA-mediated gene silencing at multiple steps, and collectively serves as an auxiliary factor empowering AGO2/let-7b-mediated gene silencing. Depletion of QKI decreases interaction of AGO2 with let-7b and target mRNA, consequently controlling target mRNA decay. This finding indicates that QKI is a complementary factor in miRNA-mediated mRNA decay. QKI, however, also suppresses the dissociation of let-7b from AGO2, and slows the assembly of AGO2/miRNA/target mRNA complexes at the single-molecule level. We also revealed that QKI overexpression suppresses cMYC expression at post-transcriptional level, and decreases proliferation and migration of HeLa cells, demonstrating that QKI is a tumour suppressor gene by in part augmenting let-7b activity. Our data show that QKI is a new type of RBP implicated in the versatile regulation of miRNA-mediated gene silencing.

ObjectivesIn veterinary medicine, attempts to apply artificial intelligence (AI) to ultrasonography have rarely been reported, and few studies have investigated the value of AI in ultrasonographic diagnosis. This study aimed to develop a deep learning-based model for classifying the status of canine chronic kidney disease (CKD) using renal ultrasonographic images and assess its diagnostic performance in comparison with that of veterinary imaging specialists, thereby verifying its clinical utility.Materials and methodsIn this study, 883 ultrasonograms were obtained from 198 dogs, including those diagnosed with CKD according to the International Renal Interest Society (IRIS) guidelines and healthy dogs. After preprocessing and labeling each image with its corresponding IRIS stage, the renal regions were extracted and classified based on the IRIS stage using the convolutional neural network-based object detection algorithm You Only Look Once. The training scenarios consisted of multi-class classification, categorization of images into IRIS stages, and four binary classifications based on specific IRIS stages. To prevent model overfitting, we balanced the dataset, implemented early stopping, used lightweight models, and applied dropout techniques. Model performance was assessed using accuracy, recall, precision, F1 score, and receiver operating characteristic curve and compared with the diagnostic accuracy of four specialists. Inter- and intra-observer variabilities among specialists were also evaluated.ResultsThe developed model exhibited a low accuracy of 0.46 in multi-class classification. However, a significant performance improvement was observed in binary classifications, with the model designed to distinguish stage 3 or higher showing the highest accuracy of 0.85. In this classification, recall, precision, and F1 score values were all 0.85, and the area under the curve was 0.89. Compared with radiologists, whose accuracy ranged from 0.48 to 0.62 in this experimental scenario, the AI model exhibited superiority. Intra-observer reliability among radiologists was substantial, whereas inter-observer variability showed a moderate level of agreement.ConclusionsThis study developed a deep-learning framework capable of reliably classifying CKD IRIS stages 3 and 4 in dogs using ultrasonograms. The developed framework demonstrated higher accuracy than veterinary imaging specialists and provided more objective and consistent interpretations. Therefore, deep-learning-based ultrasound diagnostics are potentially valuable tools for diagnosing CKD in dogs.

Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep and its malfunction, the gold-standard is polysomnography, which utilizes an extensive range of variables for sleep stage classification. However, undergoing full polysomnography, which requires many sensors that are directly connected to the heaviness of the setup and the discomfort of sleep, brings a significant burden. In this study, sleep stage classification was performed using the single dimension of nasal pressure, dramatically decreasing the complexity of the process. In turn, such improvements could increase the much needed clinical applicability. Specifically, we propose a deep learning structure consisting of multi-kernel convolutional neural networks and bidirectional long short-term memory for sleep stage classification. Sleep stages of 25 healthy subjects were classified into 3-class (wake, rapid eye movement (REM), and non-REM) and 4-class (wake, REM, light, and deep sleep) based on nasal pressure. Following a leave-one-subject-out cross-validation, in the 3-class the accuracy was 0.704, the F1-score was 0.490, and the kappa value was 0.283 for the overall metrics. In the 4-class, the accuracy was 0.604, the F1-score was 0.349, and the kappa value was 0.217 for the overall metrics. This was higher than the four comparative models, including the class-wise F1-score. This result demonstrates the possibility of a sleep stage classification model only using easily applicable and highly practical nasal pressure recordings. This is also likely to be used with interventions that could help treat sleep-related diseases.

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.

BackgroundChronic rhinosinusitis (CRS) is an inflammatory disease affecting the sinuses or nose. Persistent inflammatory responses can lead to tissue remodeling, which is a pathological characteristics of CRS. Activation of fibroblasts in the nasal mucosal stroma, differentiation and collagen deposition, and subepithelial fibrosis have been associated with CRS.ObjectivesWe aimed to assess the inhibitory effects of doxycycline and deoxycholic acid-polyethyleneimine conjugate (DA3-Doxy) on myofibroblast differentiation and extracellular matrix (ECM) production in nasal fibroblasts stimulated with TGF-β1.MethodsTo enhance efficacy, we prepared DA3-Doxy using a conjugate of low-molecular-weight polyethyleneimine (PEI) (MW 1800) and deoxycholic acid (DA) and Doxy. The synthesis of the DA3-Doxy polymer was confirmed using nuclear magnetic resonance, and the critical micelle concentration required for cationic micelle formation through self-assembly was determined. Subsequently, the Doxy loading efficiency of DA3 was assessed. The cytotoxicity of Doxy, DA3, PEI, and DA-Doxy in nasal fibroblasts was evaluated using the WST-1 assay. The anti-tissue remodeling and anti-inflammatory effects of DA3-Doxy and DA3 were examined using real-time polymerase chain reaction (Real-time PCR), immunocytochemistry, western blot, and Sircol assay.ResultsBoth DA3 and DA3-Doxy exhibited cytotoxicity at 10 μg/ml in nasal fibroblasts. Doxy partially inhibited α-smooth muscle actin, collagen types I and III, and fibronectin. However, DA3-Doxy significantly inhibited α-SMA, collagen types I and III, and fibronectin at 5 μg/ml. DA3-Doxy also modulated TGF-β1-induced changes in the expression of MMP 1, 2, and 9. Nonetheless, TGF-β1-induced expression of MMP3 was further increased by DA3-Doxy. The expression of TIMP 1 and 2 was partially reduced with 5 μg/ml DA3-Doxy.ConclusionsAlthough initially developed for the delivery of genetic materials or drugs, DA3 exhibits inhibitory effects on myofibroblast differentiation and ECM production. Therefore, it holds therapeutic potential for CRS, and a synergistic effect can be expected when loaded with CRS treatment drugs.

배경 In sepsis, the systemic response systemic inflammatory response of the host to pathogens, host factors with poor prognosis include old age, frailty, and sarcopenia. Biomarkers to evaluate the diagnosis and severity of sepsis may be critical for adequate clinical decision and help improve the prognosis of sepsis. Among biomarkers, this study was conducted to evaluate the association between the myokine, cytokines produced and secreted by muscle cells, and the diagnosis and prognosis of sepsis, and also to evaluate whether myokine is associated with muscle mass. 방법 A prospective cohort study was conducted among adult sepsis patients who visited the emergency room of Korea University Ansan Hospital from January 2016 to January 2020. The myokines measured in this study were brain-derived neutrotrophic factor oncostatin M. Muscle mass index was measured by a board-certified radiologist on axial images of L3 vertebra level in patients underwent abdomen CT scan at the time of admission for sepsis. 결과 During the study population, 627 sepsis patients were included in the study. Of these, myokines were measured in 168 patients, and 66 patients (39.2%) were diagnosed as septic shock. The median SOFA score was 8 in sepsis group and 11 in septic shock group. The initial lactate levels were 2.5 and 5.6 mmol/L in sepsis and septic shock, respectively. In-hospital mortality rate was 39.3%, and the 7-, 14-, and 28-day mortality rates were 13.7%, 21.4%, and 26.8%, respectively. Among myokines, the median value of BDNF were 11.2ng/mL and 10ng/mL in sepsis and septic shock, respectively. The median value of oncostatin M were 102pg/mL and 111.6pg/mL in sepsis and septic shock, respectively. Total abdomi- nal muscle area index (TAMAI) were 34.6㎠/㎡ and 34.2㎠/㎡ in sepsis and septic shock, respectively. BDNF was statistically significantly different from sepsis (P < 0.001) and septic shock (P <0.001) com- pared with the control group, respectively. Oncostatin M in sepsis (P <0.001) and septic shock (P <0.001) were significantly different from the control group, respectively. Higher oncostatin M were asso- ciated with increased 14-day mortality (adjusted hazard ratio [aHR]: 1.01, 95% confidence interval [CI]: 1.00-1.02, P = 0.028) and 28-day mortality (aHR: 1.01, 95% CI 1.00-1.02, P = 0.010). BDNF at the di- agnosis of sepsis was statistically higher in survivors (12.15 ng/mL) than in deaths (8.19 ng/mL), where as oncostatin M showed no difference between the two groups. TAMAI showed higher value in the sur- vivors (35.23㎠/㎡) than in the deaths (30.6㎠/㎡). TAMAI did not show any association with myokines. 결론 In patients with sepsis and septic shock, BDNF was lower than that of healthy controls and oncostatin M was higher than that of healthy controls. Oncostatin M was associated with higher mortality in sepsis patients, and BDNF was significantly higher in sepsis survivors. Muscle mass did not show a significant association with myokine levels. [ABSTRACT FROM AUTHOR]

A new kind of sequence–to–sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG–based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery–based brain–computer interface (BCI) have been limited to classifying EEG signals within one type of imagery. Therefore, it is important to develop a general model to learn various types of neural representations. In this study, we designed an experimental paradigm based on motor imagery, visual imagery, and speech imagery tasks to interpret the neural representations during mental imagery in different modalities. We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi–head attention over the spatial, spectral, and temporal domains. The proposed network shows promising performance with 0.62, 0.70, and 0.72 mental imagery accuracy with the private EEG dataset, BCI competition IV 2a dataset, and Arizona State University dataset, respectively, as compared to the conventional deep learning models. Hence, we believe that it will contribute significantly to overcoming the limited number of classes and low classification performances in the BCI system.

Since sepsis was first defined, sepsis management has remained challenging. To improve mortality rates for sepsis and septic shock, an accurate diagnosis and prompt administration of appropriate antibiotics are essential. The goals of antimicrobial stewardship are to achieve optimal clinical outcomes and to ensure cost-effectiveness and minimal unintended consequences, such as toxic effects and development of resistant pathogens. A combination of inadequate diagnostic criteria for sepsis and time pressure to provide broad-spectrum antimicrobial therapy remains an obstacle for antimicrobial stewardship. Efforts such as selection of appropriate empirical antibiotics and de-escalation or determination of whether or not to stop antibiotics may help to improve a patient's clinical prognosis as well as the successful implementation of antimicrobial stewardship. [ABSTRACT FROM AUTHOR]

Although ionizing radiation (IR) is widely used for therapeutic and research purposes, studies on low-dose ionizing radiation (LDIR) are limited compared with those on other IR approaches, such as high-dose gamma irradiation and ultraviolet irradiation. High-dose IR affects DNA damage response and nucleotide–protein crosslinking, among other processes; however, the molecular consequences of LDIR have been poorly investigated. Here, we developed a method to profile RNA species crosslinked to an RNA-binding protein, namely, human antigen R (HuR), using LDIR and high-throughput RNA sequencing. The RNA fragments isolated via LDIR-crosslinking and immunoprecipitation sequencing were crosslinked to HuR and protected from RNase-mediated digestion. Upon crosslinking HuR to target mRNAs such as PAX6, ZFP91, NR2F6, and CAND2, the transcripts degraded rapidly in human cell lines. Additionally, PAX6 and NR2F6 downregulation mediated the beneficial effects of LDIR on cell viability. Thus, our approach provides a method for investigating post-transcriptional gene regulation using LDIR.

Abstract Background Epigenetic regulations frequently appear in Glioblastoma (GBM) and are highly associated with metabolic alterations. Especially, Histone deacetylases (HDACs) correlates with the regulation of tumorigenesis and cell metabolism in GBM progression, and HDAC inhibitors report to have therapeutic efficacy in GBM and other neurological diseases; however, GBM prevention and therapy by HDAC inhibition lacks a mechanism in the focus of metabolic reprogramming. Methods HDAC2 highly express in GBM and is analyzed in TCGA/GEPIA databases. Therefore, HDAC2 knockdown affects GBM cell death. Analysis of RNA sequencing and qRT-PCR reveals that miR-3189 increases and GLUT3 decreases by HDAC2 knockdown. GBM tumorigenesis also examines by using in vivo orthotopic xenograft tumor models. The metabolism change in HDAC2 knockdown GBM cells measures by glucose uptake, lactate production, and OCR/ECAR analysis, indicating that HDAC2 knockdown induces GBM cell death by inhibiting GLUT3. Results Notably, GLUT3 was suppressed by increasing miR-3189, demonstrating that miR-3189-mediated GLUT3 inhibition shows an anti-tumorigenic effect and cell death by regulating glucose metabolism in HDAC2 knockdown GBM. Conclusions Our findings will demonstrate the central role of HDAC2 in GBM tumorigenesis through the reprogramming of glucose metabolism by controlling miR-3189-inhibited GLUT3 expression, providing a potential new therapeutic strategy for GBM treatment.

Highly sophisticated control based on a brain-computer interface (BCI) requires decoding kinematic information from brain signals. The forearm is a region of the upper limb that is often used in everyday life, but intuitive movements within the same limb have rarely been investigated in previous BCI studies. In this study, we focused on various forearm movement decoding from electroencephalography (EEG) signals using a small number of samples. Ten healthy participants took part in an experiment and performed motor execution (ME) and motor imagery (MI) of the intuitive movement tasks (Dataset I). We propose a convolutional neural network using a channel-wise variational autoencoder (CVNet) based on inter-task transfer learning. We approached that training the reconstructed ME-EEG signals together will also achieve more sufficient classification performance with only a small amount of MI-EEG signals. The proposed CVNet was validated on our own Dataset I and a public dataset, BNCI Horizon 2020 (Dataset II). The classification accuracies of various movements are confirmed to be 0.83 (±0.04) and 0.69 (±0.04) for Dataset I and II, respectively. The results show that the proposed method exhibits performance increases of approximately 0.09~0.27 and 0.08~0.24 compared with the conventional models for Dataset I and II, respectively. The outcomes suggest that the training model for decoding imagined movements can be performed using data from ME and a small number of data samples from MI. Hence, it is presented the feasibility of BCI learning strategies that can sufficiently learn deep learning with a few amount of calibration dataset and time only, with stable performance.

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

Non-invasive brain-computer interface (BCI) has been developed for recognizing and classifying human mental states with high performances. Specifically, classifying pilots' mental states accurately is a critical issue because their cognitive states, which are induced by mental fatigue, workload, and distraction, may be fundamental in catastrophic accidents. In this study, we present an electroencephalogram (EEG) classification of four mental states (fatigue, workload, distraction, and the normal state) from EEG signals in both offline and pseudo-online analyses. To the best of our knowledge, this study is the first attempt to classify pilots' mental states using only EEG signals during continuous decoding. We recorded EEG signals from seven pilots under various simulated flight conditions. We proposed a multiple feature block-based convolutional neural network (MFB-CNN) with temporal-spatio EEG filters to recognize the pilot's current mental states. We validated the proposed method for two analyses across all subjects. In the offline analysis, we confirmed the classification accuracy of 0.75 (±0.04). Also, in the pseudo-online analysis, we obtained the detection accuracy of 0.72 (±0.20), 0.72 (±0.27), and 0.61 (±0.18) for fatigue, workload, and distraction, respectively. Hence, we demonstrate the feasibility of classifying various types of mental states for implementation in real-world environments.

Recent advances in brain-computer interface (BCI) techniques have led to increasingly refined interactions between users and external devices. Accurately decoding kinematic information from brain signals is one of the main challenges encountered in the control of human-like robots. In particular, although the forearm of an upper extremity is frequently used in daily life for high-level tasks, only few studies addressed decoding of the forearm movement. In this study, we focus on the classification of forearm movements according to elaborated rotation angles using electroencephalogram (EEG) signals. To this end, we propose a hierarchical flow convolutional neural network (HF-CNN) model for robust classification. We evaluate the proposed model not only with our experimental dataset but also with a public dataset (BNCI Horizon 2020). The grand-average classification accuracies of three rotation angles yield 0.73 (±0.04) for the motor execution (ME) task and 0.65 (±0.09) for the motor imagery (MI) task across ten subjects in our experimental dataset. Further, in the public dataset, the grand-averaged classification accuracies were 0.52 (±0.03) for ME and 0.51 (±0.04) for MI tasks across fifteen subjects. Our experimental results demonstrate the possibility of decoding complex kinematics information using EEG signals. This study will contribute to the development of a brain-controlled robotic arm system capable of performing high-level tasks.

A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Motor imagery (MI) paradigm is widely used in non-invasive BCI to control external devices by decoding user intentions. The traditional MI-BCI problem is to obtain enough EEG data samples for adopting deep learning techniques, as electroencephalography (EEG) data have intricate and non-stationary properties that can cause a discrepancy between different sessions of data. Because of the discrepancy, the recorded EEG data with different sessions cannot be treated as the same. In this study, we recorded a large intuitive EEG dataset that contained nine types of movements of a single-arm across 12 subjects. We proposed a SessionNet that learns generality with EEG data recorded over multiple sessions using feature similarity to improve classification performance. Additionally, the SessionNet adopts the principle of a hierarchical convolutional neural network that shows robust classification performance regardless of the number of classes. The SessionNet outperforms conventional methods on 3-class, 5-class, and two types of 7-class and 9-class of a single-arm task. Hence, our approach could demonstrate the possibility of using feature similarity based on a novel ensemble learning method to train generality from multiple session data for better MI classification performance.

We report the impact of intrinsic defects in epitaxial BiFeO3 films on charge conduction and resistive switching of Pt/BiFeO3/SrRuO3 capacitors, although the BiFeO3 films show very similar ferroelectric domain types probed by piezoresponse force microscopy. Capacitors with p-type Bi-deficient and n-type Bi-rich BiFeO3 films exhibit switchable diode and conventional bipolar resistive switching behaviors, respectively. Both the capacitors show good retention properties with a high ON/OFF ratio of >100 in Bi-deficient films and that of >1000 in Bi-rich films. The present investigation advances considerably understanding of interface control through defect engineering of BiFeO3 thin films for non-volatile memory application. [ABSTRACT FROM AUTHOR]

Five engineering constants of transversely isotropic nanohoneycomb material are obtained, using one tensile test, two bending tests, and two indentation tests. The advantage is that all these tests can be carried out using only simple beam shape specimens. In all but the tensile test, a desired material constant is to be extracted from mixed materials constants. A disadvantage is that the deviation of the desired material constants are increased by the deviation of other related material constants. This is the first attempt to measure mechanical properties of microscopic specimens of brittle transversely isotropic material. It is intended to lay the foundation for easy and simple measurement of complex material constants of microscopic specimens. [ABSTRACT FROM AUTHOR]

배경 Sepsis is still one of the infectious diseases represented by high mortality rates. Rapid and accurate diagnosis of sepsis is necessary for early administration of antibiotics to improve the prognosis of sepsis. This study was designed to improve the rapid diagnosis of sepsis among the large number of patients admitted to the emergency room. 방법 We developed “K-Speed: Korea University-Korea Institute of Science and Technology SePsis Early prEdiction and Diagnosis algorithm”, a high performance clinical decision algorithm for early diagnosis of sepsis by the application of deep learning technique. The study population comprised of patients with Korean Triage and Acuity Scale (KTAS) score of 3 or higher, who visited emergency room (ER) between January 2016 and January 2020, and were classified as sepsis, septic shock, and non-sepsis according to the final diagnosis. K-Speed analyzed a combination of clinical data including demographics, vital signs, and laboratory findings to generate a sepsis and septic shock risk score. 결과 During study period, a total of 1208 patients were enrolled in this study. From 4-fold cross validation, the Area Under the Receiver Operating Characteristic (AUROC) of K-Speed was 0.83 to distinguish between sepsis and non-sepsis patients among severely ill patients in ER and was higher than procalcitonin (0.73). The sensitivity, specificity, accuracy, precision, and F1 score were 0.77, 0.79, 0.78, 0.80, and 0.78, respectively. The AUROC of K-Speed was 0.78 for distinguishing between septic shock and others. The sensitivity, specificity, accuracy, precision, and F1 score were 0.97, 0.19, 0.81, 0.83, and 0.89, respectively. 결론 K-Speed, a high performance clinical decision algorithm via deep learning based on demographics, vital signs, and laboratory findings showed relatively high AUROC to predict the onset of sepsis. K-Speed may be useful for early diagnosis of sepsis and septic shock in ER. [ABSTRACT FROM AUTHOR]

It is important to operate devices with control panels and touch screens assisted by haptic feedback in mobile environments such as driving automobiles and electric power wheelchairs. A lot of consideration is needed to give accurate haptic feedback, especially, presenting clear touch feedback to the elderly and people with reduced sensation is a very critical issue from healthcare and safety perspectives. In this study, we aimed to identify the perceptual characteristics for the frequency and direction of haptic vibration on the touch screen with vehicle-driving vibration and to propose an efficient haptic system based on these characteristics. As a result, we demonstrated that the detection threshold shift decreased at frequencies above 210 Hz due to the contact pressure during active touch, but the detection threshold shift increased at below 210 Hz. We found that the detection thresholds were 0.30–0.45 gpeak with similar sensitivity in the 80–270 Hz range. The haptic system implemented by reflecting the experimental results achieved characteristics suitable for use scenarios in automobiles. Ultimately, it could provide practical guidelines for the development of touch screens to give accurate touch feedback in the real-world environment.

Transforming growth factor-β1 (TGF-β1) is highly expressed in the tumor microenvironment and known to play a multifunctional role in cancer progression. In addition, TGF-β1 promotes metastasis by inducing epithelial–mesenchymal transition (EMT) in a variety of tumors. Thus, inhibition of TGF-β1 is considered an important strategy in the treatment of cancer. In most tumors, TGF-β1 signal transduction exhibits modified or non-functional characteristics, and TGF-β1 inhibitors have various inhibitory effects on cancer cells. Currently, many studies are being conducted to develop TGF-β1 inhibitors from non-toxic natural compounds. We aimed to develop a new TGF-β1 inhibitor to suppress EMT in cancer cells. As a result, improved chalcone-like chain CTI-82 was identified, and its effect was confirmed in vitro. We showed that CTI-82 blocked TGF-β1-induced EMT by inhibiting the cell migration and metastasis of A549 lung cancer cells. In addition, CTI-82 reduced the TGF-β1-induced phosphorylation of SMAD2/3 and inhibited the expression of various EMT markers. Our results suggest that CTI-82 inhibits tumor growth, migration, and metastasis.

Tumor necrosis factor-α (TNF-α) plays a significant role in inflammation and cancer-related apoptosis. We identified a TNF-α-mediated epigenetic mechanism of apoptotic cell death regulation in estrogen receptor-α (ERα)-positive human breast cancer cells. To assess the apoptotic effect of TNF-α, annexin V/ propidium iodide (PI) double staining, cell viability assays, and Western blotting were performed. To elucidate this mechanism, histone deacetylase (HDAC) activity assay and immunoprecipitation (IP) were conducted; the mechanism was subsequently confirmed through chromatin IP (ChIP) assays. Finally, we assessed HDAC3–ERα-mediated apoptotic cell death after TNF-α treatment in ERα-positive human breast cancer (MCF-7) cells via the transcriptional activation of p53 target genes using luciferase assay and quantitative reverse transcription PCR. The TNF-α-induced selective apoptosis in MCF-7 cells was negatively regulated by the HDAC3–ERα complex in a caspase-7-dependent manner. HDAC3 possessed a p53-binding element, thus suppressing the transcriptional activity of its target genes. In contrast, MCF-7 cell treatment with TNF-α led to dissociation of the HDAC3–ERα complex and substitution of the occupancy on the promoter by the p53–p300 complex, thus accelerating p53 target gene expression. In this process, p53 stabilization was accompanied by its acetylation. This study showed that p53-mediated apoptosis in ERα-positive human breast cancer cells was negatively regulated by HDAC3–ERα in a caspase-7-dependent manner. Therefore, these proteins have potential application in therapeutic strategies.

Breast cancer is one of the leading causes of morbidity and mortality among women. Epidermal growth factor receptor (EGFR) and proto-oncogene tyrosine-protein kinase Src (c-Src) are critical components of the signaling pathways that are associated with breast cancer. However, the regulatory mechanism of histone deacetylase 3 (HDAC3) in these pathways remains unclear. Using the Net Phos 3.1 program for the analysis of kinase consensus motifs, we found two c-Src-mediated putative phosphorylation sites, tyrosine (Tyr, Y)-328 and Y331 on HDAC3, and generated a phospho-specific HDAC3 antibody against these sites. c-Src-mediated phosphorylation was observed in the cells expressing wild-type HDAC3 (HDAC3WT), but not in cells overexpressing phosphorylation-defective HDAC3 (HDAC3Y328/331A). Phosphorylated HDAC3 showed relatively higher deacetylase activity, and PP2, which is a c-Src inhibitor, blocked HDAC3 phosphorylation and reduced its enzymatic activity. EGF treatment resulted in HDAC3 phosphorylation in both MDA-MB-231 and EGFR-overexpressing MCF7 (MCF7-EGFR) cells, but not in MCF7 cells. Total internal reflection fluorescence analysis showed that HDAC3 was recruited to the plasma membrane following EGF stimulation. HDAC3 inhibition with either c-Src knockdown or PP2 treatment significantly ameliorated the invasiveness of breast cancer cells. Altogether, our findings reveal an EGF signaling cascade involving EGFR, c-Src, and HDAC3 in breast cancer cells.

Abstract Background Scrub typhus is a mite-borne infectious rickettsial disease that can occur in rural and urban areas, with an especially high prevalence in older populations. This disease causes systemic vasculitis that can invade the central nervous system. Considering these characteristics, here we examined whether scrub typhus was associated with the occurrence of dementia, using large population-based cohort data. Method This population-based cohort study enrolled patients aged 60–89 years using data from the Health Insurance Review and Assessment database of South Korea between 2009 and 2018. We defined scrub typhus and dementia using International Classification of Diseases, Tenth Edition diagnostic codes. The control group was stratified according to age and sex at a ratio of 1:5 to the case group in the study population. The index date was set after 90 days beyond the date of the scrub typhus diagnosis, while the observation period was from the time of the index appointment to December 31, 2020. The primary outcome was newly diagnosed dementia. The secondary outcome was dementia classification, such as Alzheimer’s disease, vascular dementia, and other. All analyses were conducted by matching age, gender, and comorbidity. Results During the observation period, 10,460 of 71,047 (14.7%) people who had a history of scrub typhus versus 42,965 of 355,235 (12.1%) people in the control group, that is, with no history of scrub typhus, were diagnosed with dementia (adjusted hazard ratio, 1.12; 95% confidence interval, 1.10–1.15, p

Abstract Background Despite high vaccination coverage, measles outbreaks have been reported in measles elimination countries, especially among healthcare workers in their 20 and 30 s. This study was designed to identify measles-susceptible individuals and to evaluate whether primary or secondary vaccine failure occurred during measles outbreak response immunization (ORI) activities. Methods The study population was divided into three groups as follows: natural immunity group (Group 1), vaccine-induced immunity group (Group 2), and vaccine failure group (Group 3). We evaluated the immunogenicity of measles among healthcare workers using three methods—enzyme-linked immunoassays, plaque reduction neutralization tests, and avidity assays. The results were assessed at baseline, 4 weeks after, and 6 months after the completion of measles-mumps-rubella (MMR) vaccination. Results In total, 120 subjects were enrolled, with 40 subjects in each group. The median age of Group 3 was 29 years, which was significantly lower than that of the other groups. The baseline negative measles virus (MeV) IgG in Group 3 increased to a median value of 165 AU/mL at 4 weeks after ORI and was lower than that in Groups 1 and 2. The median neutralizing antibody titer was highest in Group 1, and this was significantly different from that in Group 2 or Group 3 at 4 weeks (944 vs. 405 vs. 482 mIU/mL, P = 0.001) and 6 months (826 vs. 401 vs. 470, P = 0.011) after ORI. The rates of high MeV avidity IgG were highest in Group 2, and these were significantly different from those in Groups 1 or 3 at 4 weeks (77.5 vs. 90% vs. 88.6%, P = 0.03) and 6 months (81 vs. 94.8 vs. 82.1%, P = 0.01) after ORI. Conclusions Considering the MeV-neutralizing antibodies and IgG avidity after MMR vaccination in measles-susceptible group, vaccine failure is inferred as secondary vaccine failure, and further data regarding the maintenance of immunogenicity are needed based on long-term data. The MeV-neutralizing antibody levels were highest in the natural immunity group, and the primary vaccine-induced immunity group showed the highest rates of high MeV IgG avidity.

ABSTRACT: Objective: To evaluate the impact of enhanced environmental cleaning (two times per day) compared with conventional cleaning on the acquisition of carbapenem-resistant Acinetobacter baumannii (CRAB) in a highly endemic intensive care unit (ICU). Methods: The intervention consisted of alternating environmental cleaning and conventional cleaning every 3 months during the study period from July 2018 to June 2019. CRAB acquisition was assessed using incidence and prevalence rates, and colonization pressure. Results: The incidence rates of CRAB with and without enhanced cleaning were 16.3 and 13.9 cases per 1000 population, respectively; and the prevalence rates were 66.9 and 61.7 cases per 1000 population, respectively. Colonization pressure was 53.3% with enhanced cleaning and 51.3% without enhanced cleaning. No association was found between enhanced environmental cleaning and the incidence (P=0.156), prevalence (P=0.888) and colonization pressure (P=0.825) of CRAB acquisition. The ventilator utilization ratio increased the colonization pressure of CRAB acquisition (P=0.010, β coefficient=0.37). The proportion of new nurses with

T cells are instrumental to protective immune responses against SARS-CoV-2, the pathogen responsible for the COVID-19 pandemic. Here the authors show that, in convalescent COVID-19 patients, memory T cell responses are detectable up to 317 days post-symptom onset, in which the presence of stem cell-like memory T cells further hints long-lasting immunity.

Abstract Background The therapeutic response of cervical tuberculous lymphadenitis (CTBL) may be delayed or paradoxical, with the frequent development of residual lymph nodes (LNs) during and after antituberculous treatment. We investigated the incidence of residual LNs and the clinical, radiological, microbiological, and pathologic responses of patients with CTBL after 6 months of antituberculous therapy. Methods The medical records of HIV-negative adult patients with CTBL diagnosed between July 2009 and December 2017 were analyzed. After 6 months of first-line antituberculous treatment, computed tomography (CT) scans were conducted to evaluate for residual LNs. Fine-needle aspiration biopsy (FNAB) was carried out if a patient presented with residual LNs > 10 mm in diameter with central necrosis, peripheral rim enhancement, or perinodal inflammation on CT scan. Results Residual LNs were detected in 35 of 157 patients who underwent follow-up CT scans and were more commonly observed in younger patients who completed the treatment (mean years ± standard deviation [SD]: 33 ± 13 vs. 44 ± 16, p

Few studies have focused on clinical courses or viral loads in young asymptomatic or mild patients with COVID-19 infection. We sought to better understand the clinical course and association between viral load and prevalence of pneumonia in young COVID-19 patients with asymptomatic or mild disease severity. In this retrospective study, 106 COVID-19 young patients with asymptomatic or mild disease severity were analyzed for clinical characteristics, clinical course, prevalence of radiologically proven pneumonia and viral load. The cut-off value of viral load for presence of pneumonia was also investigated. The mean age was 28.0±9.3 years. Eleven patients (10.4%) experienced viral remission within one week of diagnosis, but one (0.9%) transferred to the hospital due to aggravation of pneumonia. Patients with pneumonia had significantly higher viral load than those without, and the cut-off value of the Ct value for presence of pneumonia were 31.38. The patients with pneumonia had significantly slower recovery times than those without. Diarrhea was significantly more common in patients with pneumonia than patients without pneumonia. In conclusion, most young asymptomatic and mildly symptomatic patients showed stable clinical course. There were significant differences in viral load and recovery times between patients with and without pneumonia.

Brain cancer: Enzyme target for potential therapy A drug that targets a key enzyme in aggressive brain cancer tumors could help tackle resistance to existing treatments. Glioblastoma is the most aggressive form of brain cancer and remains difficult to treat because the cancer cells can survive chemotherapy and radiotherapy. Certain cells within glioblastoma tumors have ‘stemness’ – unique stem cell-like metabolic characteristics that allow them to rapidly repair DNA damage and trigger relapse. Hyonchol Jang at the National Cancer Center in Goyang, South Korea and co-workers discovered that an enzyme called farnesyl diphosphate synthase (FDPS) helps maintain stemness in glioblastoma. The team then treated patient-derived glioblastoma cells with existing drugs known to inhibit FDPS. One such drug, which is already used to treat osteoporosis, inhibited the formation of secondary glioblastoma and may prove valuble in the treatment of brain cancer.

Yersinia pseudotuberculosis is a causative agent of foodborne zoonosis that usually causes self-limiting pseudoappendicitis. Y. pseudotuberculosis infection also causes systemic spread or extraintestinal manifestations in patients with predisposing conditions. Here, we present a case of acute hepatitis with Y. pseudotuberculosis bacteremia in a 30-year-old man. He was previously healthy without significant medical history other than obesity and current smoking. At the time of admission, he presented with high fever accompanied by chills, jaundice, abdominal pain, and watery diarrhea. Laboratory studies revealed leukocytosis and elevated liver function parameters. A stool culture showed no causative pathogens. Empiric antibiotic therapy with ceftriaxone and metronidazole was administered. Y. pseudotuberculosis was later isolated from the initial blood culture performed on the day of admission using MALDI-TOF mass spectrometry. Antibiotic treatment was continued based on the susceptibility testing results from MALDI-TOF MS and VITEk®2, as well as clinical and laboratory improvements. The patient was discharged on the tenth day of admission and remained healthy with no recurrence during the 12-month follow-up. Here, we review the literature on the systemic infection caused by Y. pseudotuberculosis, including extraintestinal manifestations. This case highlights that Y. pseudotuberculosis may be considered a differential causative organism in patients with acute colitis and hepatitis.

The formation of secondary phases of NiTe2 and NbTe2 in p-type Bi0.5Sb1.5Te3 thermoelectric alloys was investigated through in situ phase separation by using the melt spinning process. Adding stoichiometric Ni, Nb, and Te in a solid-state synthesis process of Bi0.5Sb1.5Te3, followed by rapid solidification by melt spinning, successfully segregated NiTe2 and NbTe2 in the Bi0.5Sb1.5Te3 matrix. Since heterointerfaces of Bi0.5Sb1.5Te3 with NiTe2 and NbTe2 form potential barriers of 0.26 and 0.08 eV, respectively, a low energy carrier filtering effect can be expected; higher Seebeck coefficients and power factors were achieved for Bi0.5Sb1.5Te3(NiTe2)0.01 (250 μV/K and 3.15 mW/mK2), compared to those of Bi0.5Sb1.5Te3 (240 μV/K and 2.69 mW/mK2). However, there was no power factor increase for NbTe2 segregated samples. The decrease in thermal conductivity was seen due to the possible additional phonon scattering by the phase segregations. Consequently, zT at room temperature was enhanced to 0.98 and 0.94 for Bi0.5Sb1.5Te3(NiTe2)0.01 and Bi0.5Sb1.5Te3(NbTe2)0.01, respectively, compared to 0.79 for Bi0.5Sb1.5Te3. The carrier filtering effect induced by NiTe2 segregations with an interface potential barrier of 0.26 eV effectively increased the Seebeck coefficient and power factor, thus improving the zT of p-type Bi0.5Sb1.5Te3, while the interface potential barrier of 0.08 eV of NbTe2 segregation appeared to be too small to induce an effective carrier filtering effect.

After publication of the original article [1], we were notified that an author’s name has been incorrectly spelled. Soon You Kwon’s correct full name is Soon Young Kwon.

Temozolomide is the current first-line treatment for glioblastoma patients but, because many patients are resistant to it, there is an urgent need to develop antitumor agents to treat temozolomide-resistant glioblastoma. Gossypol, a natural polyphenolic compound, has been studied as a monotherapy or combination therapy for the treatment of glioblastoma. The combination of gossypol and temozolomide has been shown to inhibit glioblastoma, but it is not clear yet whether gossypol alone can suppress temozolomide-resistant glioblastoma. We find that gossypol suppresses the growth of temozolomide-resistant glioblastoma cells in both tumor sphere and adherent culture conditions, with tumor spheres showing the greatest sensitivity. Molecular docking and binding energy calculations show that gossypol has a similar affinity to the Bcl2 (B-cell lymphoma 2) family of proteins and several dehydrogenases. Gossypol reduces mitochondrial membrane potential and cellular ATP levels before cell death, which suggests that gossypol inhibits several dehydrogenases in the cell’s metabolic pathway. Treatment with a Bcl2 inhibitor does not fully explain the effect of gossypol on glioblastoma. Overall, this study demonstrates that gossypol can suppress temozolomide-resistant glioblastoma and will be helpful for the refinement of gossypol treatments by elucidating some of the molecular mechanisms of gossypol in glioblastoma.