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Abstract Predicting major adverse cardiovascular events (MACE) is crucial due to its high readmission rate and severe sequelae. Current risk scoring model of MACE are based on a few features of a patient status at a single time point. We developed a self-attention-based model to predict MACE within 3 years from time series data utilizing numerous features in electronic medical records (EMRs). In addition, we demonstrated transfer learning for hospitals with insufficient data through code mapping and feature selection by the calculated importance using Xgboost. We established operational definitions and categories for diagnoses, medications, and laboratory tests to streamline scattered codes, enhancing clinical interpretability across hospitals. This resulted in reduced feature size and improved data quality for transfer learning. The pre-trained model demonstrated an increase in AUROC after transfer learning, from 0.564 to 0.821. Furthermore, to validate the effectiveness of the predicted scores, we analyzed the data using traditional survival analysis, which confirmed an elevated hazard ratio for a group with high scores.

Abstract Loop diuretics are prevailing drugs to manage fluid overload in heart failure. However, adjusting to loop diuretic doses is strenuous due to the lack of a diuretic guideline. Accordingly, we developed a novel clinician decision support system for adjusting loop diuretics dosage with a Long Short-Term Memory (LSTM) algorithm using time-series EMRs. Weight measurements were used as the target to estimate fluid loss during diuretic therapy. We designed the TSFD-LSTM, a bi-directional LSTM model with an attention mechanism, to forecast weight change 48 h after heart failure patients were injected with loop diuretics. The model utilized 65 variables, including disease conditions, concurrent medications, laboratory results, vital signs, and physical measurements from EMRs. The framework processed four sequences simultaneously as inputs. An ablation study on attention mechanisms and a comparison with the transformer model as a baseline were conducted. The TSFD-LSTM outperformed the other models, achieving 85% predictive accuracy with MAE and MSE values of 0.56 and 1.45, respectively. Thus, the TSFD-LSTM model can aid in personalized loop diuretic treatment and prevent adverse drug events, contributing to improved healthcare efficacy for heart failure patients.

Hypoxia-inducible factor (HIF)-1α is a crucial transcription factor associated with cancer metabolism and is regarded as a potent anticancer therapeutic strategy within the hypoxic microenvironment of cancer. In this study, stilbenoid derivatives were designed, synthesized, and assessed for their capacity to inhibit HIF-1α-associated cancer metabolism and evaluated for inhibition of cancer cell viability and HIF activation. Through the structure-activity relationship studies, compound 28e was identified as the most potent derivative. Specifically, under the hypoxic condition, 28e reduced the accumulation of HIF-1α protein and the expression of its target genes related to glucose metabolism without affecting the expression of HIF-1α mRNA. Furthermore, 28e inhibited glucose uptake, glycolytic metabolism, and mitochondrial respiration, decreasing cellular ATP production under hypoxic conditions. In addition, 28e displayed significant anti-tumor effects and effectively suppressed the accumulation of HIF-1α protein in tumor tissue in vivo xenograft model. These findings suggest that our stilbenoid derivatives exert their anticancer effects by targeting HIF-1α-centered cancer metabolism under hypoxic conditions.

Abstract High-rate production of multicarbon chemicals via the electrochemical CO2 reduction can be achieved by efficient CO2 mass transport. A key challenge for C−C coupling in high-current-density CO2 reduction is how to promote *CO formation and dimerization. Here, we report molecularly enhanced CO2-to-*CO conversion and *CO dimerization for high-rate ethylene production. Nanoconfinement of ascorbic acid by graphene quantum dots enables immobilization and redox reversibility of ascorbic acid in heterogeneous electrocatalysts. Cu nanowire with ascorbic acid nanoconfined by graphene quantum dots (cAA-CuNW) demonstrates high-rate ethylene production with a Faradaic efficiency of 60.7% and a partial current density of 539 mA/cm2, a 2.9-fold improvement over that of pristine CuNW. Furthermore, under low CO2 ratio of 33%, cAA-CuNW still exhibits efficient ethylene production with a Faradaic efficiency of 41.8%. We find that cAA-CuNW increases *CO coverage and optimizes the *CO binding mode ensemble between atop and bridge for efficient C−C coupling. A mechanistic study reveals that ascorbic acid can facilitate *CO formation and dimerization by favorable electron and proton transfer with strong hydrogen bonding.

Abstract As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

Abstract Background The role of visiting health services has been proven to be effective in promoting the health of older populations. Hence, developing a web system for nurses may help improve the quality of visiting health services for community-dwelling frail older adults. This study was conducted to develop a web application that reflects the needs of visiting nurses. Methods Visiting nurses of public health centers and community centers in South Korea participated in the design and evaluation process. Six nurses took part in the focus group interviews, and 21 visiting nurses and community center managers participated in the satisfaction evaluation. Focus group interviews were conducted to identify the needs of visiting nurses with respect to system function. Based on the findings, a web application that can support the effective delivery of home visiting services in the community was developed. An artificial intelligence (AI) algorithm was also developed to recommend health and welfare services according to each patient’s health status. After development, a structured survey was conducted to evaluate user satisfaction with system features using Kano’s model. Results The new system can be used with mobile devices to increase the mobility of visiting nurses. The system includes 13 features that support the management of patient data and enhance the efficiency of visiting services (e.g., map, navigation, scheduler, protocol archives, professional advice, and online case conferencing). The user satisfaction survey revealed that nurses showed high satisfaction with the system. Among all features, the nurses were most satisfied with the care plan, which included AI-based recommendations for community referral. Conclusions The system developed from the study has attractive features for visiting nurses and supports their essential tasks. The system can help with effective case management for older adults requiring in-home care and reduce nurses' workload. It can also improve communication and networking between healthcare and long-term care institutions.

Research on capturing human mobility patterns for efficient and sustainable urban planning has been widely conducted. However, studies that unveil spatial context beyond macro-level mobility patterns are relatively scarce. This study aims to analyze the spatiotemporal human meta-mobility patterns with rich context using POI data in Seoul from comprehensive perspectives. As a result, the floating population of Seoul exhibits regular and irregular cyclical mobility patterns on weekdays and weekends, respectively, stemming from the periodicity of the dominant POIs. Additionally, graph construction based on mobility similarity and their regional clustering show clusters vary by POIs but are generally divided into peripheral and central regions of Seoul. This indicates that socioeconomic factors cannot be ignored when understanding human mobility patterns. This helps to provide scientific evidence to support policy recommendations towards greenways and sustainable urban mobility systems, such as quantitative disparity of greenways, qualitative issues of greenways in the central areas, and inequality in cultural consumption. Addressing key considerations through targeted policies could significantly improve the overall quality of life for urban residents. We expect this study to lay the groundwork for future research that aims to understand realistic human mobility patterns with a rich context.

BackgroundPredicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. ObjectiveThe aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. MethodsWe trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. ResultsWe confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. ConclusionsWe have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Acute myeloid leukemia (AML) is the most prevalent type of hematopoietic malignancy. Despite recent therapeutic advancements, the high relapse rate associated with extramedullary involvement remains a challenging issue. Moreover, therapeutic targets that regulate the extramedullary infiltration of AML cells are still not fully elucidated. The Aryl Hydrocarbon Receptor (AHR) is known to influence the progression and migration of solid tumors; however, its role in AML is largely unknown. This study explored the roles of AHR in the invasion and migration of AML cells. We found that suppressed expression of AHR target genes correlated with an elevated relapse rate in AML. Treatment with an AHR agonist on patient-derived AML cells significantly decreased genes associated with leukocyte trans-endothelial migration, cell adhesion, and regulation of the actin cytoskeleton. These results were further confirmed in THP-1 and U937 AML cell lines using AHR agonists (TCDD and FICZ) and inhibitors (SR1 and CH-223191). Treatment with AHR agonists significantly reduced Matrigel invasion, while inhibitors enhanced it, regardless of the Matrigel’s stiffness. AHR agonists significantly reduced the migration rate and chemokinesis of both cell lines, but AHR inhibitors enhanced them. Finally, we found that the activity of AHR and the expression of NMIIA are negatively correlated. These findings suggest that AHR activity regulates the invasiveness and motility of AML cells, making AHR a potential therapeutic target for preventing extramedullary infiltration in AML.

The biological activities and related mechanisms of curcumin, a major polyphenolic compound in turmeric, the rhizome of Curcuma longa, have been extensively investigated. Due to its poor solubility in water, the analysis of curcumin’s biological activities is limited in most aqueous experimental systems. In the present study, the effects of polyvinyl alcohol (PVA), a dietary-compatible vehicle, on the solubility, stability, cellular uptake, and bioactivities of curcumin were investigated. Curcumin solubility was improved significantly by PVA; the color intensity of curcumin aqueous solution in the presence of PVA increased concentration-dependently with its peak shift to a shorter wavelength. Improved suspension stability and photostability of curcumin in an aqueous solution were also observed in the presence of PVA, even at 62.5 μg/mL. The scavenging activities of curcumin against DPPH, ABTS, AAPH radicals, and nitric oxide were enhanced significantly in the presence of PVA. PVA at 250 μg/mL also significantly enhanced the cytotoxic activity of curcumin against both HCT 116 colon cancer and INT 407 (HeLa-derived) embryonic intestinal cells by reducing the IC50 from 16 to 11 μM and 25 to 15 μM, respectively. PVA improved the cellular uptake of curcumin in a concentration-dependent manner in INT 407 cells; it increased the cellular levels more effectively at lower curcumin treatment concentrations. The present results indicate that PVA improves the solubility and stability of curcumin, and changes in these chemical behaviors of curcumin in aqueous systems by PVA could enhance the bioavailability and pharmacological efficacy of curcumin.

Mealworms (Tenebrio molitor) have been used as an alternative source of proteins and lipids. Triacylglycerols (TAGs) are major sources of energy and have been used to provide essential fatty acids. They are also the main components of mealworm oil, and their composition and content are extensively linked to its physical and chemical properties. However, because of the complexity of TAG molecules, their identification and quantitation are challenging. This study employed electrospray ionization tandem mass spectrometry (ESI-MS/MS) with multiple neutral loss scans (NLS) to analyze the TAG composition and content in mealworm oil. Identifying and quantifying TAGs using ESI-MS/MS in combination with multiple NLS was an efficient way to improve accuracy and timeliness. For the accurate quantification of TAGs, isotopic deconvolution and correlation factors were applied. A total of 57 TAGs were identified and quantified: C52:2 (16:0/18:1/18:1) (1549.4 nmol/g, 18.20%), C52:3 (16:0/18:1/18:2) (1488.1 nmol/g, 17.48%), C54:4 (18:1/18:1/18:2) (870.1 nmol/g, 10.23%), C54:6 (18:1/18:2/18:2) (659.8 nmol/g, 7.76%) and C52:4 (16:0/18:2/18:2) (600.5 nmol/g, 7.06%), which were the most abundant TAGs present in the mealworm oil. The fundamental properties of mealworm oil, including its degree of oxidation, nutritional effect and physical properties, were elucidated.

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26–3.52, p

A rigid body motion, which can be decomposed into rotation and translation, is essential for engineers and scientists who deal with moving systems in a space. While translation is as simple as vector addition, rotation is hard to understand because rotations are non-Euclidean, and there are many ways to represent them. Additionally, each representation comes with complex operations, and the conversions between different representations are not unique. Therefore, in this tutorial we review rotation representations which are widely used in industry and academia such as rotation matrices, Euler angles, rotation axis-angles, unit complex numbers, and unit quaternions. In particular, for better understanding we begin with rotations in a two dimensional space and extend them to a three dimensional space. In that context, we learn how to represent rotations in a two dimensional space with rotation angles and unit complex numbers, and extend them respectively to Euler angles and unit quaternions for rotations in a three dimensional space. The definitions and properties of mathematical entities used for representing rotations as well as the conversions between various rotation representations are summarized in tables for the reader’s later convenience.

Abstract Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of a patient’s prognosis using the HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network that provides analytical insights using a graph representation of a patient’s journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model was successfully predicted as a baseline model. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular disease event outcomes on supervised link prediction learning.

Human interaction patterns on the Web over online social networks vary with the context of communication items (e.g., politics, economics, disasters, celebrities, and etc.), which leads to form unlimited time-evolving curves of information adoption as diffusion proceeds. Online communications often continue to navigate through heterogeneous social systems consisting of a wide range of online media such as social networking sites, blogs, and mainstream news. This makes it very challenging to uncover the underlying causal mechanisms of such macroscopic diffusion. In this respect, we review both top-down and bottom-up approaches to understand the underlying dynamics of an individual item's popularity growth across multiple meta-populations in a complementary way. For a case study, we use a dataset consisting of time-series adopters for over 60 news topics through different online communication channels on the Web. In order to find disparate patterns of macroscopic information propagation, we first generate and cluster the diffusion curves for each target meta-population and then estimate them with two different and complementary approaches in terms of the strength and directionality of influences across the meta-populations. In terms of the strength of influence, we find that synchronous global diffusion is not possible without very strong intra-influence on each population. In terms of the directionality of influence between populations, such concurrent propagation is likely brought by transitive relations among heterogeneous populations. When it comes to social context, controversial news topics in politics and human culture (e.g., political protests, multiculturalism failure) tend to trigger more synchronous than asynchronous diffusion patterns across different social media on the Web. We expect that this study can help to understand dynamics of macroscopic diffusion across complex systems in diverse application domains.

The diamondback moth is a detrimental insect pest of brassicaceous crops which was among the first crop insects to be reported as DDT resistant. It has since proven to be significantly resistant to nearly every synthetic insecticide used in the field in many crucifer-producing regions. Due to insecticide control failures in some parts of the world, economically viable crucifer production is now all but impossible. As a result, there has been an increasing effort to identify new compounds with strong pesticidal activity. Cantharidin is one such compound that has been shown to be highly effective against a variety of insect pests. However, its chemical synthesis and potential toxicity to non-target organisms have been a major source of concern. Herein, using rational design approaches, a new series of cantharidin-based verbenone derivatives were synthesized and evaluated for their insecticidal activities against the diamondback moth. Among different compounds screened, compounds 6a, 6h, 6i, and 6q emerged as the most potent compounds exhibiting 100% mortality at a concentration of 100 mg/L after four days. These compounds demonstrated a good anti-feeding effect against the diamondback moth on cabbage leaves. Subsequently, a 3D QSAR study was carried out to identify the key structural features of the synthesized compounds and their correlation with insecticidal activity.

The East Sea (also known as the Japan Sea; hereafter, EJS) is a semi-enclosed marginal sea surrounded by the Korean Peninsula, Russia, and the Japanese Islands. The EJS is connected to the Pacific through shallow straits. Thus, the EJS has its own thermohaline circulation and the characteristic biogeochemistry. The deep overturning circulation plays a critical role in carbon cycling including absorption of atmospheric CO2 and its sequestration into the interior of the sea. The turnover time of the deep EJS (>1000 m) is ~ hundred years and probably varies depending on physical climate forcing. Thus, the effect of climate change on oceanic processes may be more easily detected in the EJS. In this paper, we summarize the current understanding of carbon cycling in the EJS. We focus especially on the Ulleung Basin in the southwestern EJS, from which more extensive data are available. Notable features of carbon cycling in the EJS include the following: primary productivity and the export/production ratio are higher than in the adjacent Pacific; the EJS is a net sink of atmospheric CO2 and anthropogenic CO2 content is ~1% of the dissolved inorganic carbon inventory; dissolved inorganic carbon in the sea interior is mostly supplied by organic matter decomposition rather than CaCO3 dissolution and thus, the deep waters are vulnerable to acidification; N:P molar ratio of the deep waters is ~13, lower than the Redfield ratio; concentration of dissolved organic carbon is significantly higher than in the oceans; and sediment resuspension and lateral transport is an important component of sinking particulate organic carbon (POC) flux. Another important feature is the temporal trends observed for the last few decades. For example, pH, calcium carbonate saturation status, and dissolved oxygen concentration in the sea interior have decreased, whereas dissolved inorganic carbon and likely, the inventory of anthropogenic CO2 have increased. These temporal trends have an implication on better understanding of the processes occurring more slowly in the oceans. Brief suggestions for future research that will improve our understanding of carbon cycling and its variability are provided at the end of the paper.

MDH1 and MDH2 enzymes play an important role in the survival of lung cancer. In this study, a novel series of dual MDH1/2 inhibitors for lung cancer was rationally designed and synthesized, and their SAR was carefully investigated. Among the tested compounds, compound 50 containing a piperidine ring displayed an improved growth inhibition of A549 and H460 lung cancer cell lines compared with LW1497. Compound 50 reduced the total ATP content in A549 cells in a dose-dependent manner; it also significantly suppressed the accumulation of hypoxia-inducible factor 1-alpha (HIF-1α) and the expression of HIF-1α target genes such as GLUT1 and pyruvate dehydrogenase kinase 1 (PDK1) in a dose-dependent manner. Furthermore, compound 50 inhibited HIF-1α-regulated CD73 expression under hypoxia in A549 lung cancer cells. Collectively, these results indicate that compound 50 may pave the way for the development of next-generation dual MDH1/2 inhibitors to target lung cancer.

Antioxidant small molecules can prevent or delay the oxidative damage caused by free radicals. Herein, a structure-based hybridization of two natural antioxidants (caffeic acid and melatonin) afforded a novel hybrid series of indole-based amide analogues which was synthesized with potential antioxidant properties. A multiple-step scheme of in vitro radical scavenging assays was carried out to evaluate the antioxidant activity of the synthesized compounds. The results of the DPPH assay demonstrated that the indole-based caffeic acid amides are more active free radical scavenging agents than their benzamide analogues. Compared to Trolox, a water-soluble analogue of vitamin E, compounds 3a, 3f, 3h, 3j, and 3m were found to have excellent DPPH radical scavenging activities with IC50 values of 95.81 ± 1.01, 136.8 ± 1.04, 86.77 ± 1.03, 50.98 ± 1.05, and 67.64 ± 1.02 µM. Three compounds out of five (3f, 3j, and 3m) showed a higher capacity to neutralize the radical cation ABTS•+ more than Trolox with IC50 values of 14.48 ± 0.68, 19.49 ± 0.54, and 14.92 ± 0.30 µM, respectively. Compound 3j presented the highest antioxidant activity with a FRAP value of 4774.37 ± 137.20 μM Trolox eq/mM sample. In a similar way to the FRAP assay, the best antioxidant activity against the peroxyl radicals was demonstrated by compound 3j (10,714.21 ± 817.76 μM Trolox eq/mM sample). Taken together, compound 3j was validated as a lead hybrid molecule that could be optimized to maximize its antioxidant potency for the treatment of oxidative stress-related diseases.

BackgroundEffective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient’s hospitalization period may support the making of judicious decisions regarding bed management. ObjectiveFirst, this study aims to develop a machine learning (ML)–based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services. MethodsWe set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features. ResultsWe experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes. ConclusionsIn this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.

The biological carbon pump in the southwestern East Sea (Japan Sea, EJS hereafter) was investigated based on examination of sinking particulate matter samples intercepted by bottom-tethered sediment traps deployed on a mooring at three depths (500, 1000, and 2000 m) between 2011 and 2017. The total particle flux increased as the sampling depth increased, while particulate organic carbon (POC) flux was greatest at 500 m. The lithogenic material content was high at all depths, and accounted for an average of ∼42% of the particulate matter. The total particle flux at all sampling depths consistently shifted toward much higher values in 2014–2016. During this period, the POC flux at 500 m increased by 32% while net primary production (NPP) increased only slightly. Consequently, the POC flux/NPP ratio increased significantly, indicating greater biological pump efficiency than in earlier years of the study. The flux of lithogenic material derived primarily from sediment resuspension was much greater at 500 m in 2014−2016 compared with previous years, implying its potential role as a ballast mineral in enhancing particle export and transfer to the deep sea interior. The radiocarbon isotope ratio of POC was higher, and the excess Mn content values were lower at 500 m in 2014–2016, suggesting that the resuspended sediment at 500 m likely originated from a shallow region during this period, and differed in provenance from the lithogenic material intercepted at 2000 m. The period of enhanced particle flux coincided with the 2015/16 El Niño and a mesoscale warm (anticyclonic) eddy that persisted for 2 years in the study region. The East Korean Warm Current and the eddy may have facilitated the transport of resuspended particles entrained in the Korea Strait and/or the western shelf and upper slope of the basin to the study site.

ObjectiveTo evaluate the feasibility of robot-assisted laparoscopic myomectomy in multiple myomas over 10.MethodsA retrospective study was conducted for 662 patients who underwent robot-assisted laparoscopic myomectomy and open myomectomy by a single operator in a tertiary university hospital.ResultsA total of 30 women underwent removal of 10 or more uterine myomas by robotics and 13 patients were selected for this study. The average number of myomas removed was 13.7 (range 10–20). The maximum diameter of the myomas was 6.8 cm (range 5.0–10.0 cm). The sum of the diameters of each myoma was 34.7 cm (range 20.0–54.5 cm) and the mass of resected myomas for each case was 229.1 g (range 106.8–437.9 g). In no case was the robotic procedure converted into conventional laparoscopy or laparotomy, and all patients recovered without any major complications. In comparison with 13 cases of open myomectomy during the same period, robotic surgery took longer time than open surgery (360.5 vs. 183.8 minutes; P=0.001) but had shorter postoperative hospital days after surgery (mean 2.5 vs. 3.5 days; P=0.003).ConclusionRobot-assisted laparoscopic myomectomy could be an alternative to laparotomic myomectomy for numerous myomas over 10 in number.

LW1497 suppresses the expression of the hypoxia-inducing factor (HIF)-1α inhibiting malate dehydrogenase. Although hypoxia and HIF-1α are known to be important in cancer, LW1497 has not been therapeutically applied to cancer yet. Thus, we investigated the effect of LW1497 on the epithelial-mesenchymal transition (EMT) of lung cancer cells. A549 and H1299 lung cancer cells were induced to undergo via TGF-β1 treatment, resulting in the downregulation of E-cadherin and upregulation of N-cadherin and Vimentin concurrently with increases in the migration and invasion capacities of the cells. These effects of TGF-β1 were suppressed upon co-treatment of the cells with LW1497. An RNA-seq analysis revealed that LW1497 induced differential expression of genes related to hypoxia, RNA splicing, angiogenesis, cell migration, and metastasis in the A549 lung cancer cell lines. We confirmed the differential expression of Slug, an EMT-related transcription factor. Results from Western blotting and RT-PCR confirmed that LW1497 inhibited the expression of EMT markers and Slug. After orthotopically transplanting A549 cancer cells into mice, LW1497 was administered to examine whether the lung cancer progression was inhibited. We observed that LW1497 reduced the area of cancer. In addition, the results from immunohistochemical analyses showed that LW1497 downregulated EMT markers and Slug. In conclusion, LW1497 suppresses cancer progression through the inhibition of EMT by downregulating Slug.

AC1497 is an effective dual inhibitor of malate dehydrogenase 1 and 2 targeting cancer metabolism. However, its poor aqueous solubility results in low bioavailability, limiting its clinical development. This study was conducted to develop an effective self-nanoemulsifying drug delivery system (SNEDDS) of AC1497 to improve its oral absorption. Based on the solubility of AC1497 in various oils, surfactants, and cosurfactants, Capryol 90, Kolliphor RH40, and Transcutol HP were selected as the components of SNEDDS. After testing various weight ratios of Capryol 90 (20–30%), Kolliphor RH40 (35–70%), and Transcutol HP (10–35%), SNEDDS-F4 containing 20% Capryol 90, 45% Kolliphor RH40, and 35% Transcutol HP was identified as an optimal SNEDDS with a narrow size distribution (17.8 ± 0.36 nm) and high encapsulation efficiency (93.6 ± 2.28%). Drug release from SNEDDS-F4 was rapid, with approximately 80% of AC1497 release in 10 min while the dissolution of the drug powder was minimal (w/w) effectively improves the solubility and oral absorption of AC1497.

The temperature and mechanism of phase transition of poly(N-isopropylacrylamide-co-acrylic acid) [P(NiPAAm-co-AAc)], which is one of the multi-stimuli responsive polymers, were investigated at various pHs using infrared (IR) spectroscopy, two-dimensional (2D) gradient mapping, and two-dimensional correlation spectroscopy (2D-COS). The determined phase transition temperature of P(NiPAAm-co-AAc) at pH 4, 3, and 2 based on 2D gradient mapping and principal component analysis (PCA) showed that it decreases with decreasing pH, because COOH group in AAc changes with variation of pH. The results of 2D-COS analysis indicated that the phase transition mechanism of P(NiPAAm-co-AAc) hydrogel at pH4 is different from that at pH2 due to the effect of COOH group of AAc.

The present study was performed to assess the relationship between hand-held myotonometer MyotonPRO and shear wave elastography (SWE) measurements of lower limb muscle stiffness during resting and active voluntary contraction. Forty healthy young adults, (20 males and 20 females) participated in the study. The stiffness of each subject’s rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and medial gastrocnemius (MG) was measured repeatedly by MyotonPRO and SWE. Moderate to strong correlations between the two methods’ measurements were found for both resting and active voluntary contraction. (r = 0.416–0.669, p < 0.05; r = 0.398–0.594, p < 0.05, respectively). Muscle stiffness at rest was significantly lower compared contraction in all four muscles measured by both methods (p < 0.05). Intra-rater reliabilities were generally lower when measurements were taken during contraction. Additionally, when compared by gender, muscle stiffness measured by MyotonPRO was significantly higher at rest in men compared to women, except for the TA. However, a significant difference was found in TA muscle stiffness by gender when measured with SWE. When muscles were contracted, all muscles showed significantly higher stiffness in men compared to women. There were moderate to good correlations in muscle stiffness between measurements of SWE and MyotonPRO at rest and during active voluntary contraction. Additionally, both instruments showed good intra-rater reliability.

ChatGPT, an artificial intelligence (AI) language model, holds significant promise for improving the quality and efficiency of teaching and learning. However, its potential challenges and disruptions in education systems require further investigation for a deeper understanding and mitigation. Given that ChatGPT is already being utilized and complete prohibition is unlikely, it becomes crucial to consider how educators can harness its potential to enhance teaching and learning experiences, assuming successful implementation. This article explores the potential benefits and disruptions within the current education system, and proposes strategies for integrating ChatGPT into educational settings from a constructivist learning perspective. It focuses on four educational system attributes: context, collaboration, conversation, and construction. The article particularly emphasizes the utilization of ChatGPT as a means, rather than an end, to enhance student learning within a constructivist learning environment. This approach aims to address present concerns and challenges effectively.

We, for the first time, report a temporal evolution of the electroluminescence (EL) intensity in lead sulfide (PbS) colloidal quantum dot (CQD) infrared light-emitting diodes. The EL intensity was varied during infrared light emission, and its origin is attributed to competition between the achievement of charge balance associated with interfacial charging at the PbS/ZnO CQD interface and the electric-field induced luminescence quenching. The effect of multi-carrier emission on the enhanced EL intensity is discussed relating to shifting in the wavelength at the peak EL intensity.

Scholarly publications draw collective attention beyond disciplines, leading to highly skewed citation distributions in sciences. Uncovering the mechanisms of such disparate popularity is very challenging, since a wide spectrum of research fields are not only interacting and influencing one another but also time-evolving. Accordingly, this study aims to understand citation dynamics across STEM fields in terms of latent affinity and novelty decay, which is based upon Bayesian inference and learning of the Affinity Poisson Process model (APP) with bibliography data from the Web of Science database. The approaches shown in the study can shed light on predicting and interpreting popularity dynamics in diverse application domains, by considering the effect of time-varying subgroup interactions on diffusion processes.

This study explores the activities, tools, and resources that instructors of massive open online courses (MOOCs) use to improve the personalization of their MOOCs. Following email interviews with 25 MOOC and open education leaders regarding MOOC personalization, a questionnaire was developed. This questionnaire was then completed by 152 MOOC instructors from around the world. While more than 8 in 10 respondents claimed heavy involvement in designing their MOOCs, only one-third placed extensive effort on meeting unique learner needs during course design, and even fewer respondents were concerned with personalization during course delivery. An array of instructional practices, technology tools, and content resources were leveraged by instructors to personalize MOOC-based learning environments. Aligning with previous research, the chief resources and tools employed in their MOOCs were discussion forums, video lectures, supplemental readings, and practice quizzes. In addition, self-monitoring and peer-based methods of learner feedback were more common than instructor monitoring and feedback. Some respondents mentioned the use of flexible deadlines, proposed alternatives to course assignments, and introduced multimedia elements, mobile applications, and guest speakers among the ways in which they attempted to personalize their massive courses. A majority of the respondents reported modest or high interest in learning new techniques to personalize their next MOOC offering.

LW6 (3-[2-(4-adamantan-1-yl-phenoxy)-acetylamino]-4-hydroxy-benzoic acid methyl ester) is a potent inhibitor of drug efflux by the breast cancer resistance protein (BCRP). However, its poor aqueous solubility leads to low bioavailability, which currently limits in vivo applications. Therefore, the present study aimed to develop ternary solid dispersion (SD) formulations in order to enhance the aqueous solubility and dissolution rate of LW6. Various SDs of LW6 were prepared using a solvent evaporation method with different drug/excipient ratios. The solubility and dissolution profiles of LW6 in different SDs were examined, and F8-SD which is composed of LW6, poloxamer 407, and povidone K30 at a weight ratio of 1:5:8 was selected as the optimal SD. The structural characteristics of F8-SD were also examined using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD), and scanning electron microscopy (SEM). In the acidic to neutral pH range, F8-SD achieved rapid dissolution with a drug release of 76−81% within 20 min, while the dissolution of pure LW6 was negligible. The XRPD patterns indicated that F8-SD probably enhanced the solubility and dissolution of LW6 by changing the drug crystallinity to an amorphous state, in addition to the solubilizing effect of the hydrophilic carriers. Furthermore, F8-SD significantly improved the oral bioavailability of topotecan, which is a BCRP substrate, in rats. The systemic exposure of topotecan was enhanced approximately 10-fold by the concurrent use of F8-SD. In conclusion, the ternary SD formulation of LW6 with povidone K30 and poloxamer 407 appeared to be effective at improving the dissolution and in vivo effects of LW6 as a BCRP inhibitor.

Diverse online social networks are becoming increasingly interconnected by sharing information. Accordingly, emergent macro-level phenomena have been observed, such as the synchronous spread of information across different types of social media. Attempting to analyze the emergent global behavior is impossible from the examination of a single social platform, and dynamic influences between different social networks are not negligible. Furthermore, the underlying structural property of networks is important, as it drives the diffusion process in a stochastic way. In this paper, we propose a macro-level diffusion model with a probabilistic approach by combining both the heterogeneity and structural connectivity of social networks. As real-world phenomena, we explore instances of news diffusion across different social media platforms from a dataset that contains over 386 million web documents covering a one-month period in early 2011. We find that influence between different media types is varied by the context of information. News media are the most influential in the arts and economy categories, while social networking sites (SNS) and blog media are in the politics and culture categories, respectively. Furthermore, controversial topics, such as political protests and multiculturalism failure, tend to spread concurrently across social media, while entertainment topics, such as film releases and celebrities, are more likely driven by interactions within single social platforms. We expect that the proposed model applies to a wider class of diffusion phenomena in diverse fields and that it provides a way of interpreting the dynamics of diffusion in terms of the strength and directionality of influences among populations.

Sodium bicarbonate cotransporters (NBCs) are involved in the pH regulation of salivary glands. However, the roles and regulatory mechanisms among different NBC isotypes have not been rigorously evaluated. We investigated the roles of two different types of NBCs, electroneutral (NBCn1) and electrogenic NBC (NBCe1), with respect to pH regulation and regulatory mechanisms using human submandibular glands (hSMGs) and HSG cells. Intracellular pH (pHi) was measured and the pHi recovery rate from cell acidification induced by an NH4Cl pulse was recorded. Subcellular localization and protein phosphorylation were determined using immunohistochemistry and co-immunoprecipitation techniques. We determined that NBCn1 is expressed on the basolateral side of acinar cells and the apical side of duct cells, while NBCe1 is exclusively expressed on the apical membrane of duct cells. The pHi recovery rate in hSMG acinar cells, which only express NBCn1, was not affected by pre-incubation with 5 μM PP2, an Src tyrosine kinase inhibitor. However, in HSG cells, which express both NBCe1 and NBCn1, the pHi recovery rate was inhibited by PP2. The apparent difference in regulatory mechanisms for NBCn1 and NBCe1 was evaluated by artificial overexpression of NBCn1 or NBCe1 in HSG cells, which revealed that the pHi recovery rate was only inhibited by PP2 in cells overexpressing NBCe1. Furthermore, only NBCe1 was significantly phosphorylated and translocated by NH4Cl, which was inhibited by PP2. Our results suggest that both NBCn1 and NBCe1 play a role in pHi regulation in hSMG acinar cells, and also that Src kinase does not regulate the activity of NBCn1.