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We investigated assessments of calorie production and energy efficiencies of phytoplankton in Gwangyang, Jaran, and Geoje-Hansan bays in southern Korea, based on seasonal field measurements data for particulate organic carbon (POC), macromolecular compositions, calorie contents, and primary production. Our findings revealed that Geoje-Hansan Bay consistently exhibits higher POC concentrations compared to Gwangyang and Jaran bays, except during the summer season. The observed seasonal variations in POC concentrations and primary productions indicated distinct factors influencing POC distribution among the bays, which varies with the seasons. Macromolecular compositions displayed notable patterns, with Gwangyang Bay exhibiting pronounced seasonal variations, Jaran and Geoje-Hansan bays showing stable carbohydrate (CHO) dominance, and Gwangyang Bay displaying protein (PRT) dominance influenced by river-borne nutrients. Analysis of calorie content revealed that Geoje-Hansan Bay exhibited the highest calorie content, suggesting a more favorable physiological state of phytoplankton compared to Gwangyang and Jaran bays. Estimations of annual primary production and calorie production demonstrated regional variations, with Geoje-Hansan Bay having the highest values followed by Jaran Bay and Gwangyang Bay. These differences in primary production and calorie production reflect the diverse ecological conditions and nutrient availability specific to each bay. Interestingly, Gwangyang Bay demonstrated the highest energy efficiency, producing more calories per unit of carbon compared to the other two bays, potentially due to the influence of different dominant phytoplankton communities. This study enhances our understanding of ecosystem dynamics and ecological characteristics among the three bays, emphasizing the importance of considering seasonal variations and specific bay characteristics in investigating biogeochemical processes, energy flow, and ecosystem functioning. The findings contribute valuable insights for the sustainable management of coastal ecosystems and aquaculture practices.

BackgroundEarly signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI). ObjectiveThis study aimed to use data collected from fitness trackers to predict MCI status. MethodsIn this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status. ResultsOur machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time. ConclusionsOur results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies.

Abstract Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. Our experiments, which involved seven rare tissues from different cancer types, demonstrate that the LLM-based prediction model achieves significant accuracy with very few or zero samples. Our proposed model, the CancerGPT (with ~ 124M parameters), is comparable to the larger fine-tuned GPT-3 model (with ~ 175B parameters). Our research contributes to tackling drug pair synergy prediction in rare tissues with limited data, and also advancing the use of LLMs for biological and medical inference tasks.

We propose an accurate and noise-robust deep learning model to diagnose bearing faults for practical implementation in industry. To achieve high classification accuracy in a noisy environment, we designed a time-frequency multi-domain fusion block, incorporated bearing-fault physics into the model parameters, and employed attention modules. The proposed model individually extracts essential features from the time-domain vibration signal and the corresponding spectrum in a parallel pipeline. Subsequently, multi-domain feature maps are fused to capture a wider representation of bearing fault signals. The performance was enhanced by incorporating physical knowledge of fault frequencies in the design of the frequency-domain feature extraction network. The employment of an attention mechanism to selectively focus on high-importance fault characteristics on the multi-domain feature maps further improved the accuracy under high noise levels. Experiments on bearing datasets with artificially added noise demonstrated the effectiveness of the proposed model compared to other benchmark models.

ABSTRACTGeostationary satellites are valuable tools for monitoring the entire lifetime of tropical cyclones (TCs). Although the most widely used method for TC intensity estimation is manual, several automatic methods, particularly artificial intelligence (AI)-based algorithms, have been proposed and have achieved significant performance. However, AI-based techniques often require large amounts of input data, making it challenging to adopt newly introduced data such as those from recently launched satellites. This study proposed a transfer-learning-based TC intensity estimation method to combine different source data. The pre-trained model was built using the Swin Transformer (Swin-T) model, utilizing data from the Communication Ocean and Meteorological Satellite Meteorological Imager sensor, which has been in operation for an extensive period (2011–2021) and provides a large dataset. Subsequently, a transfer learning model was developed by fine-tuning the pre-trained model using the GEO-KOMPSAT-2A Advanced Meteorological Imager, which has been operational since 2019. The transfer learning approach was tested in three different ways depending on the fine-tuning ratio, with the optimal performance achieved when all layers were fine-tuned. The pre-trained model employed TC observations from 2011 to 2017 for training and 2018 for testing, whereas the transfer learning model utilized data from 2019 and 2020 for training and 2021 for testing to evaluate the model performance. The best pre-trained and transfer learning models achieved mean absolute error of 6.46 kts and 6.48 kts, respectively. Our proposed model showed a 7–52% improvement compared to the control models without transfer learning. This implies that the transfer learning approach for TC intensity estimation using different satellite observations is significant. Moreover, by employing a deep learning model visualization approach known as Eigen-class activation map, the spatial characteristics of the developed model were validated according to the intensity levels. This analysis revealed features corresponding to the Dvorak technique, demonstrating the interpretability of the Swin-T-based TC intensity estimation algorithm. This study successfully demonstrated the effectiveness of transfer learning in developing a deep learning-based TC intensity estimation model for newly acquired data.

The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aβ) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aβ plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.

Abstract The objective of this study was to compare the maternal and neonatal outcomes following delayed cord clamping (DCC) versus immediate cord clamping (ICC) in twin pregnancies. This was a retrospective cohort study of 705 twin pregnancies who delivered at ≥ 24 weeks of gestation. Maternal and neonatal hemoglobin levels, blood transfusion, and neonatal outcomes were compared between DCC (n = 225) and ICC (n = 480) groups. Mean maternal predelivery and postpartum hemoglobin levels and the rate of postpartum hemoglobin drop ≥ 20% or maternal blood transfusion were comparable between the two groups. The DCC group had a significantly higher mean neonatal hemoglobin level (DCC vs. ICC: 17.4 ± 3.5 vs. 16.6 ± 2.7 g/dl, P = 0.010) but significantly lower rates of neonatal blood transfusion (DCC vs. ICC: 3.3% vs. 8.8%, P

Recently, VR-based training applications have become popular and promising, as they can simulate real-world situations in a safe, repeatable, and cost-effective way. For immersive simulations, various input devices have been designed and proposed to increase the effectiveness of training. In this study, we developed a novel device that generates 3D hand motion data and provides haptic force feedback for VR interactions. The proposed device can track 3D hand positions using a combination of the global position estimation of ultrasonic sensors and the hand pose estimation of inertial sensors in real time. For haptic feedback, shape–memory alloy (SMA) actuators were designed to provide kinesthetic forces and an efficient power control without an overheat problem. Our device improves upon the shortcomings of existing commercial devices in tracking and haptic capabilities such that it can track global 3D positions and estimate hand poses in a VR space without using an external suit or tracker. For better flexibility in handling and feeling physical objects compared to exoskeleton-based devices, we introduced an SMA-based actuator to control haptic forces. Overall, our device was designed and implemented as a lighter and less bulky glove which provides comparable accuracy and performance in generating 3D hand motion data for a VR training application (i.e., the use of a fire extinguisher), as demonstrated in the experimental results.

Phosphorus (P) and TiO2 have been extensively studied as anode materials for lithium-ion batteries (LIBs) due to their high specific capacities. However, P is limited by low electrical conductivity and significant volume changes during charge and discharge cycles, while TiO2 is hindered by low electrical conductivity and slow Li-ion diffusion. To address these issues, we synthesized organic–inorganic hybrid anode materials of P–polypyrrole (PPy) and TiO2–PPy, through in situ polymerization of pyrrole monomer in the presence of the nanoscale inorganic materials. These hybrid anode materials showed higher cycling stability and capacity compared to pure P and TiO2. The enhancements are attributed to the electrical conductivity and flexibility of PPy polymers, which improve the conductivity of the anode materials and effectively buffer volume changes to sustain structural integrity during the charge and discharge processes. Additionally, PPy can undergo polymerization to form multi-component composites for anode materials. In this study, we successfully synthesized a ternary composite anode material, P–TiO2–PPy, achieving a capacity of up to 1763 mAh/g over 1000 cycles.

The protein basic helix-loop-helix family member e40 (BHLHE40) is a transcription factor recently emerged as a key regulator of host immunity to infections, autoimmune diseases and cancer. In this study, we investigated the role of Bhlhe40 in protective T cell responses to the intracellular bacterium Chlamydia in the female reproductive tract (FRT). Mice deficient in Bhlhe40 exhibited severe defects in their ability to control Chlamydia muridarum shedding from the FRT. The heightened bacterial burdens in Bhlhe40-/- mice correlated with a marked increase in IL-10-producing T regulatory type 1 (Tr1) cells and decreased polyfunctional CD4 T cells co-producing IFN-γ, IL-17A and GM-CSF. Genetic ablation of IL-10 or functional blockade of IL-10R increased CD4 T cell polyfunctionality and partially rescued the defects in bacterial control in Bhlhe40-/- mice. Using single-cell RNA sequencing coupled with TCR profiling, we detected a significant enrichment of stem-like T cell signatures in Bhlhe40-deficient CD4 T cells, whereas WT CD4 T cells were further down on the differentiation trajectory with distinct effector functions beyond IFN-γ production by Th1 cells. Altogether, we identified Bhlhe40 as a key molecular driver of CD4 T cell differentiation and polyfunctional responses in the FRT against Chlamydia.

This present study investigated the anti-skin-aging properties of Rosa rugosa. Initially, phenolic compounds were isolated from a hot water extract of Rosa rugosa’s flower buds. Through repeated chromatography (column chromatography, MPLC, and prep HPLC), we identified nine phenolic compounds (1–9), including a previously undescribed depside, rosarugoside D (1). The chemical structure of 1 was elucidated via NMR, HR-MS, UV, and hydrolysis. Next, in order to identify bioactive compounds that are effective against TNF-α-induced NHDF cells, we measured intracellular ROS production in samples treated with each of the isolated compounds (1–9). All isolates reduced the level of ROS at a concentration of 10 μM. Particularly, two depsides—rosarugosides A and D (2 and 1)—significantly inhibited ROS expression in TNF-α-induced NHDFs compared to the other phenolic compounds. Subsequently, the production of MMP-1 and procollagen type Ι α1 by these two depsides was examined. Remarkably, rosarugoside A (2) significantly decreased MMP-1 secretion at all concentrations. In contrast, rosarugoside D (1) regulated the expression of procollagen type Ι α1. These findings collectively suggest that Rosa rugosa extracts and their isolated compounds, rosarugosides A (2) and D (1), hold significant potential for protecting against aging and skin damage. Overall, these findings suggest that Rosa rugosa extracts and their isolated compounds, rosarugosides A (2) and D (1), have the potential to prevent and protect against aging and skin damage, although more specific quantitative analysis is needed.

Background: While there is research on protective factors against smartphone addiction (SA) and phubbing, which impact adolescents’ physical, psychological, interpersonal, and academic well-being, focused studies on these issues among Thai Muslim students in Southern Thailand remain scarce. Objectives: To bridge this gap, this research aimed to explore the influence of five family functioning factors—discipline, communication and problem-solving (CPS), relationship, emotional status, and family support—guided by family systems theory and the McMaster Model, on SA and phubbing. Methods: Data from 825 Thai Muslim adolescent secondary school students (Female N = 459 (55.7%), Mage = 15.11 ± 1.78) across three southern Thai provinces were analyzed utilizing structural equation modeling (SEM). Results: Significant connections were identified between family functioning factors, particularly emotional status and discipline, and SA, and their impact on phubbing behavior. SA was found to mediate the relationship between these two family factors and phubbing. Interestingly, a higher quality of family relationships was found to be associated with an increased risk of SA, while the other two family functions—CPS and family support—demonstrated no significant association with these digital behaviors. Conclusions: Despite its limitations, including a cross-sectional design and reliance on self-reports, the study underscores the importance of specific family functions in preventing and addressing adolescent SA and phubbing.

Interleukin (IL)-32 is produced by T lymphocytes, natural killer cells, monocytes, and epithelial cells. IL-32 induces the production of pro-inflammatory cytokines such as tumor necrosis factor (TNF)-α, IL-1β, IL-6, and IL-8, and IL-32 expression is highly increased in rheumatoid arthritis (RA) patients. Enolase-1 (ENO1) is a glycolytic enzyme and the stimulation of ENO1 induces high levels of pro-inflammatory cytokines in concanavalin A (Con A)-activated peripheral blood mononuclear cells (PBMCs) and macrophages in RA patients. In addition, there are many reports that anti-ENO1 antibody is correlated with the disease progression of RA. It implies that ENO1 could regulate IL-32 production during inflammation related to the pathogenesis of RA. Therefore, we investigated the role of ENO1 in IL-32 production using Con A-activated PBMCs and RA PBMCs. IL-32 expression is increased by ENO1 stimulation using real-time PCR and ELISA. In addition, we confirmed that IL-32 production was decreased in Con A-activated PBMCs and RA PBMCs pre-treated with NF-κB or p38 MAPK pathway inhibitors. Taken together, these results suggest that ENO1 plays an important role in inflammation through the induction of IL-32 production by the activation of the NF-κB and p38 MAPK pathways.

The planning accuracy of charged particle therapy (CPT) is subject to the accuracy of stopping power (SP) estimation. In this study, we propose a method of deriving a pseudo-triple-energy CT (pTECT) that can be achievable in the existing dual-energy CT (DECT) systems for better SP estimation. In order to remove the direct effect of errors in CT values, relative CT values according to three scanning voltage settings were used. CT values of each tissue substitute phantom were measured to show the non-linearity of the values thereby suggesting the absolute difference and ratio of CT values as parameters for SP estimation. Electron density, effective atomic number (EAN), mean excitation energy and SP were calculated based on these parameters. Two of conventional methods were implemented and compared to the proposed pTECT method in terms of residuals, absolute error and root-mean-square-error (RMSE). The proposed method outperformed the comparison methods in every evaluation metrics. Especially, the estimation error for EAN and mean excitation using pTECT were converging to zero. In this proof-of-concept study, we showed the feasibility of using three CT values for accurate SP estimation. Our suggested pTECT method indicates potential clinical utility of spectral CT imaging for CPT planning.

Background Particulate matter (PM) is a major air pollutant that affects human health worldwide. PM can pass through the skin barrier, thus causing skin diseases such as heat rash, allergic reaction, infection, or inflammation. However, only a few studies have been conducted on the cytotoxic effects of PM exposure on large-scale animals. Therefore, herein, we investigated whether and how PM affects rhesus macaque skin fibroblasts. Methods Rhesus macaque skin fibroblasts were treated with various concentrations of PM10 (1, 5, 10, 50, and 100 μg/mL) and incubated for 24, 48, and 72 h. Then, cell viability assay, TUNEL assay, and qRT-PCR were performed on the treated cells. Further, the reactive oxygen species, glutathione, and cathepsin B levels were determined. The MTT assay revealed that PM10 (>50 μg/mL) proportionately reduced the cell proliferation rate. Results PM10 treatment increased TUNEL-positive cell numbers, following the pro-apoptosis-associated genes (CASP3 and BAX) and tumor suppressor gene TP53 were significantly upregulated. PM10 treatment induced reactive oxidative stress. Cathepsin B intensity was increased, whereas GSH intensity was decreased. The mRNA expression levels of antioxidant enzyme-related genes (CAT, GPX1 and GPX3) were significantly upregulated. Furthermore, PM10 reduced the mitochondrial membrane potential. The mRNA expression of mitochondrial complex genes, such as NDUFA1, NDUFA2, NDUFAC2, NDUFS4, and ATP5H were also significantly upregulated. In conclusion, these results showed that PM10 triggers apoptosis and mitochondrial damage, thus inducing ROS accumulation. These findings provide potential information on the cytotoxic effects of PM10 treatment and help to understand the mechanism of air pollution-induced skin diseases.

It is known that sperm and seminal plasma (SP) affect uterine immunity. In cattle, artificial insemination enables breeding by depositing frozen and largely diluted sperm with a negligible amount of SP into the uterus. Thus, the present study focused on the impact of frozen-thawed sperm on bovine uterine immunity. We have previously shown that in the bovine uterus, sperm swim smoothly over the luminal epithelium and some sperm interact with uterine glands to induce a weak inflammatory response mainly via the endometrial Toll-like receptor 2 (TLR2) signaling. However, the process by which sperm is encountered in the uterine glands is not completely clear. The present study intended to evaluate the role of sperm-TLR2 in sperm-uterine mucus penetration for reaching the glandular epithelium to induce the uterine immune response. To activate and block sperm-TLR2, they were treated with TLR2 agonist and antagonist, respectively. TLR2 activation enhanced sperm hyperactivation and improved its capacity to penetrate the artificial viscoelastic fluid and estrous-uterine-mucus. In contrast, TLR2-blocked sperm showed completely opposite effects. It is noteworthy, that the TLR2-activated sperm that penetrated the uterine mucus exhibited increased motile activity with hyperactivation. In the sperm-endometrial ex-vivo model, a greater amount of TLR2-activated sperm entered the uterine glands with an immune response, which was seen as the upregulation of mRNA expression for TNFA, IL1B, IL8, PGES, and TLR2 similar to those in control sperm. On the other hand, a lesser amount of TLR2-blocked sperm entered the uterine glands and weakened the sperm-induced increase only in PGES, suggesting that penetration of a certain number of sperm in the uterine gland is necessary enough to trigger the inflammatory response. Altogether, the present findings indicate that activation of sperm-TLR2 promotes their hyperactivation and mucus penetration with greater motility, allowing them to enter into the uterine glands more. This further suggests that the hyperactivated sperm contributes to triggering the pro-inflammatory cascade partly via TLR2 in the uterus.

Abstract Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fisher’s exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage ( http://www.wang-lab-hkust.com:3838/TMZEP ). Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs.

Recent user recognition technologies have focused on biometric signals to remotely identify users in the access management, medical welfare, and public sectors. The electrocardiogram (ECG) signal is an individual’s unique electrophysiological signal generated within the body and is difficult to forge or change; thus, it can be used to uniquely identify the user. Existing user identification systems based on ECG signals detect R peaks according to morphological features and use a fiducial-based segmentation process for data normalization. However, this process does not detect a distinct R peak due to motion artifacts generated by the movement of the subject, thereby degrading identification accuracy. To address the problem of decreasing peak accuracy of the fiducial-based segmentation data, this study proposes a user identification system based on one-dimensional neural networks using periodic non-fiducial-based segmentation data that do not overlap in the time domain. The proposed system comprises a preprocessing step for data denoising, a non-fiducial-based and non-overlap segmentation step, and a step for classifying users using a one-dimensional shallow neural network. In the experiment, when using self-acquired 10-second ECG signal data that were divided into non-fiducial and non-overlapping segments, the long short-term memory (LSTM), bidirectional LSTM, and one-dimensional convolutional neural network (CNN) models achieved accuracies of 92.01%, 93.13%, and 95.51%, respectively, with the 1D CNN model exhibiting the best accuracy. The proposed system demonstrated a 25.48% improvement in accuracy on non-fiducial segmented data compared to the 1D CNN model, which achieved an accuracy of 70.03% on fiducial segmented data. Moreover, when tested on publicly available ECG datasets, including MIT-BIH, ECG-ID, and PTB-XL, the proposed system exhibited a user identification accuracy of over 94%, confirming its superior performance.

Diabetes mellitus (DM) predisposes individuals to vascular injury, leading to poor outcomes after ischemic stroke and symptomatic hemorrhagic transformation (SHT) after thrombolytic and endovascular treatment (EVT). Metformin (MET), an oral antidiabetic drug, has shown potential neuroprotective effects, but its impact on stroke prognosis in DM patients undergoing EVT remains unclear. In a multicenter study, 231 patients with DM undergoing EVT for acute ischemic stroke were enrolled. Prior MET use was identified, and patients were stratified into MET+ and MET− groups. Demographics, clinical data, and outcomes were compared between groups. Multivariate analysis was used to assess the effect of MET on stroke prognosis. Of the enrolled patients, 59.3% were previously on MET. MET+ patients had lower initial infarct volumes and NIHSS scores compared to MET-taking patients. Multivariate analysis showed that MET+ was associated with a lower risk of stroke progression and SHT (with stroke progression as follows: odd ratio [OR] 0.24, 95% confidence interval [CI] [0.12–0.48], p < 0.001; SHT: OR 0.33, 95% CI [0.14–0.75], p = 0.01) and was also associated with better 3-month functional outcomes (mRS 0–2) after EVT. Prestroke MET use in DM patients undergoing EVT is associated with improved stroke prognosis, including reduced risk of stroke progression and SHT and better functional outcomes. These findings suggest the potential neuroprotective role of MET in this population and highlight its clinical utility as an adjunctive therapy in the management of ischemic stroke. Further research is warranted to elucidate the underlying mechanisms and to optimize MET therapy in this setting.

Over the past two decades, the environmental characteristics of the northern East China Sea (NECS) that make it a crucial spawning ground for commercially significant species have faced substantial impacts due to climate change. Protein (PRT) within phytoplankton, serving as a nitrogen-rich food for organisms of higher trophic levels, is a sensitive indicator to environmental shifts. This study aims to develop a regional PRT algorithm to characterize spatial and temporal variations in the NECS from 2012 to 2022. Employing switching chlorophyll-a and particulate organic nitrogen algorithms, the developed regional PRT algorithm demonstrates enhanced accuracy. Satellite-estimated PRT concentrations, utilizing data from the Visible Infrared Imaging Radiometer Suite (VIIRS), generally align with the 1:1 line when compared to in situ data. Seasonal patterns and spatial distributions of PRT in both the western and eastern parts of the NECS from 2012 to 2022 were discerned, revealing notable differences in the spatial distribution and major controlling factors between these two areas. In conclusion, the regional PRT algorithm significantly improves estimation precision, advancing our understanding of PRT dynamics in the NECS concerning PRT concentration and environmental changes. This research underscores the importance of tailored algorithms in elucidating the intricate relationships between environmental variables and PRT variations in the NECS.

Ice algae thriving within sea ice play a crucial role in transferring energy to higher trophic levels and influencing biogeochemical processes in polar oceans; however, the distribution of organic matter within the ice interior is not well understood. This study aimed to investigate the vertical distribution of organic matter, including chlorophyll a (Chl-a), particulate organic carbon and nitrogen (POC and PON), carbohydrates (CHO), proteins (PRT), lipids (LIP), and food material (FM), within the sea ice. Samples were collected from the bottom, middle, and top sections of the sea ice column near Cambridge Bay during the spring of 2018. Based on the δ13C signature, biochemical composition, and POC contribution of biopolymeric carbon (BPC), the organic substances within the sea ice were predominantly attributed to marine autotrophs. While the highest concentrations of each parameter were observed at the sea ice bottom, notable concentrations were also found in the upper sections. The average sea ice column-integrated Chl-a concentration was 5.05 ± 2.26 mg m−2, with the bottom ice section contributing 59% (S.D. = ± 10%) to the total integration. The column-integrated concentrations of FM, BPC, POC, and PON were 2.05 ± 0.39, 1.10 ± 0.20, 1.47 ± 0.25, and 0.09 ± 0.03 g m−2, respectively. Contributions of the bottom ice section to these column-integrated concentrations varied for each parameter, with values of 20 ± 6, 21 ± 7, 19 ± 5, and 28 ± 7%, respectively. While the bottom ice section exhibited a substantial Chl-a contribution in line with previous studies, significantly higher contributions of the other parameters were observed in the upper sea ice sections. This suggests that the particulate matter within the interior of the sea ice could potentially serve as an additional food source for higher trophic grazers or act as a seeding material for a phytoplankton bloom during the ice melting season. Our findings highlight the importance of comprehensive field measurements encompassing the entire sea ice section to better understand the distribution of organic carbon pools within the sea ice in the Arctic Ocean.

Abstract Despite the emerging scientific interest in polymer‐based stretchable electronics, the trade‐off between the crystallinity and stretchability of intrinsically stretchable polymer semiconductors—charge‐carrier mobility increases as crystallinity increases while stretchability decreases—hinders the development of high‐performance stretchable electronics. Herein, a highly stretchable polymer semiconductor is reported that shows concurrently improved thin film crystallinity and stretchability upon thermal annealing. The polymer thin films annealed at temperatures higher than their crystallization temperatures exhibit substantially improved thin film stretchability (> 200%) and hole mobility (≥ 0.2 cm2 V−1 s−1). The simultaneous enhancement of the crystallinity and stretchability is attributed to the thermally‐assisted structural phase transition that allows the formation of edge‐on crystallites and reinforces interchain noncovalent interactions. These results provide new insights into how the current crystallinity–stretchability limitation can be overcome. Furthermore, the results will facilitate the design of high‐mobility stretchable polymer semiconductors for high‐performance stretchable electronics.

Abstract Polymer semiconductors are promising materials for stretchable, wearable, and implantable devices due to their intrinsic flexibility, facile functionalization, and solution processability at low temperatures. However, the crystalline domain of the conjugated structure for high charge carrier mobility in semiconducting polymers exhibits lower stretchability than that of the semi‐crystalline or amorphous domains. Herein, a set of thermoplastic soft semiconductors is synthesized with different ratios of diketopyrrolopyrrole–carbazole–diketopyrrolopyrrole (DPP‐Cz‐DPP)‐based hard segments and thiophene‐based aliphatic soft segments, having the similar structure of thermoplastic elastomers. The polymers exhibit decreased glassy temperatures with the increased content of the soft segments. The polymers show high crystallinity after copolymerization with a large‐sized DPP‐Cz‐DPP core and non‐conjugated segments due to an aggregation property of the conjugated core, still possessing a semi‐crystalline domain after annealing. The polymer films exhibit stretchability under strains of up to 60%. Organic field‐effect transistors fabricated using stretchable polymers show a mobility range of 0.125–0.005 cm2 V−1 s−1 with different proportions of the soft segment. The stretchability is improved significantly and the mobilities are decreased less when the content of the soft segment is increased. Therefore, this study presents a design principle for the development of a high‐performance stretchable semiconducting polymer.

Although the rate of childhood obesity seems to have plateaued in recent years, the prevalence of obesity among children and adolescents remains high. Childhood obesity is a major public health concern as overweight and obese youth suffer from many co-morbid conditions once considered exclusive to adults. It is now well demonstrated that abdominal obesity as measured by waist circumference (WC) is an independent risk factor for cardiovascular disease and metabolic dysfunction in youth. Despite the strong associations between WC and cardiometabolic risk factors, there is no consensus regarding the optimal WC measurement sites to assess abdominal obesity and obesity-related health risk in children and adolescents. Currently, the WC measurement site that provides the best reflections of visceral fat and the best correlations with cardiometabolic risk factors is unclear. The purpose of this review is to explore whether WC measurement sites influence the relationships between WC, visceral fat, and cardiometabolic risk factors in children and adolescents.

Abstract We investigated the associations between serum lipid profiles and risk of early-onset vasomotor symptoms (VMSs) in premenopausal women. This cohort study comprised 2,540 premenopausal women aged 42–52 years without VMSs at baseline (median follow-up: 4.4 years). VMSs, including hot flashes and night sweats, were assessed using the Menopause-Specific Quality of Life questionnaire (Korean version). Early-onset VMSs were defined as VMSs that occurred premenopause; moderate/severe VMSs were defined as a score of ≥ 3 points (range: 0 to 6, 6 being most bothersome). Cox proportional hazard regression models were used to estimate hazard ratios with 95% confidence intervals (CI) for the development of VMSs across the lipid levels. Higher low-density lipoprotein (LDL) cholesterol levels were positively associated with increased risk of early-onset VMSs. Compared to the

Abstract Background Metabolic syndrome (MetS) is a cluster of conditions associated with glucose intolerance, hypertension, abdominal obesity, dyslipidemia, and insulin resistance that increase the risk of cardiovascular diseases (CVD) and type 2 diabetes (T2D). Since MetS is known as a complex symptom with a high incidence of genetic factors, it is important to identify genetic variants for each clinical characteristic of MetS. Methods We performed targeted next-generation sequencing (NGS) to identify genetic variants related to obesity, blood glucose, triacylglycerol (TG), and high-density lipoprotein (HDL)-cholesterol level, and hypertension in 48 subjects with MetS and in 48 healthy subjects. Results NGS analysis revealed that 26 of 48 subjects (54.2%) with MetS had putative non-synonymous variants related to the clinical features of MetS. Of the subjects with MetS, 8 (16.7%) had variants in 4 genes (COL6A2, FTO, SPARC, and MTHFR) related to central obesity, 17 (35.4%) had variants in 6 genes (APOB, SLC2A2, LPA, ABCG5, ABCG8, and GCKR) related to hyperglycemia, 3 (6.3%) had variants in 4 genes (APOA1, APOC2, APOA4, and LMF1) related to hypertriglyceridemia, 8 (16.7%) had variants in 4 genes (ABCA1, CETP, SCARB1, and LDLR) related to low HDL-cholesterolemia, and 5 (10.4%) had variants in ADD1 related to hypertension. Conclusions Our findings may contribute to broadening the genetic spectrum of risk variants related to the development of MetS.

Abstract The role of anti-Müllerian hormone (AMH) levels in incident vasomotor symptoms (VMS) is largely unknown. This study aimed to investigate the relationship between AMH levels and the development of early-onset VMS among premenopausal women. Our cohort study comprised 2041 premenopausal women aged 42–52 years free of VMS at baseline whose AMH levels were measured. VMS, including hot flushes and night sweats, were assessed using the Korean version of the Menopause-specific Quality of Life questionnaire. Early-onset VMS was defined as the occurrence of VMS prior to menopause. Parametric proportional hazards models were used to estimate adjusted hazard ratios (HRs) and 95% CI. During a median follow-up of 4.4 years, 708 premenopausal women developed early-onset VMS (incidence rate, 8.0 per 100 person-years). Lower AMH levels were statistically significantly associated with an increased risk of early-onset VMS. After adjusting for age and other confounders, multivariable-adjusted HRs (95% CI) for incident VMS comparing AMH quintiles 4–1 to the highest quintile were 1.02 (0.78–1.33), 1.37 (1.06–1.76), 1.36 (1.04–1.76), and 2.38 (1.84–3.08), respectively (P for trend

Abstract Adequate serotonin levels are pivotal to human well-being; thus, serotonin can be used as a biomarker because it regulates a wide range of physical and psychological functions. As an imbalance of serotonin is highly likely to initiate the pathogenesis of various disorders, monitoring serotonin levels in real time is in high demand for the early detection of disease. We fabricated a field-effect transistor (FET) biosensor based on aptamer-immobilized conducting polymer nanohybrids, which showed an instantaneous response toward serotonin in solution. The mechanism of serotonin detection was based on aptamer deformation after aptamer-ligand interaction and the consequential decrease in the charge carrier density of the FET template. Docking simulations with AutoDock/Vina and PyMOL were successfully used to investigate the binding site of serotonin in the loop structure of the aptamer. The fabricated FET template showed high sensitivity toward serotonin in the range of 10 fM to 100 nM, and the limit of detection (LOD) was exceptionally low at 10 fM. Moreover, the selectivity toward serotonin was confirmed by observing no signal after the injection of structural analogs, functional analogs and excess physiological biomolecules. The potential clinical application of this sensor was confirmed because it remained consistent when the buffer solution was exchanged for artificial serum or artificial cerebrospinal fluid (CSF). † S.G.L. and S.E.S. contributed equally to this work.

The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT.In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation.The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map.This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.

Blockchain technologies have gained widespread use in security-sensitive applications due to their robust data protection. However, as blockchains are increasingly integrated into critical data management systems, they have become attractive targets for attackers. Among the various attacks on blockchain systems, distributed denial of service (DDoS) attacks are one of the most significant and potentially devastating. These attacks render the systems incapable of processing transactions, causing the blockchain to come to a halt. To address the challenge of detecting DDoS attacks on blockchains, existing visualization schemes have been developed. However, these schemes often fail to provide early DDoS detection since they typically display only past and current system status. In this paper, we present a novel visualization scheme that not only portrays past and current values but also forecasts future expected system statuses. We achieve these future predictions by utilizing polynomial regression with blockchain data. Additionally, we offer an alternative DDoS detection method employing statistical analysis, specifically the coefficient of determination, to enhance accuracy. Through our experiments, we demonstrate that our proposed scheme excels at predicting future blockchain statuses and anticipating DDoS attacks with minimal error. Our work empowers system managers of blockchain-based applications to identify and mitigate DDoS attacks at an earlier stage.

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from −6 to 6 dB signal-to-noise ratio (SNR).

This study aimed to investigate the association between cerebral small vessel disease (CSVD) burden and infarct growth rate (IGR) in patients with large vessel occlusion (LVO) stroke who underwent endovascular treatment (EVT). A retrospective analysis was conducted on a cohort of 495 patients with anterior circulation stroke who received EVT. CSVD burden was assessed using a CSVD score based on neuroimaging features. IGR was calculated from diffusion-weighted imaging (DWI) lesion volumes divided by the time from stroke onset to imaging. Clinical outcomes included stroke progression and functional outcomes at 3 months. Multivariate analyses were performed to assess the relationship between CSVD burden, IGR, and clinical outcomes. The fast IGR group had a higher proportion of high CSVD scores than the slow IGR group (24.4% vs. 0.8%, p < 0.001). High CSVD burden was significantly associated with a faster IGR (odds ratio [95% confidence interval], 26.26 [6.26–110.14], p < 0.001) after adjusting for confounding factors. High CSVD burden also independently predicted stroke progression and poor functional outcomes. This study highlights a significant relationship between CSVD burden and IGR in LVO stroke patients undergoing EVT. High CSVD burden was associated with faster infarct growth and worse clinical outcomes.

Despite extensive research on the individual effects of breakfast and sleep on health outcomes, there has been limited investigation into their combined effects. We aimed to evaluate the relationship between breakfast-eating behavior and sleep timing on cardiovascular disease (CVD) risk factors. A total of 16,121 participants (6744 men and 9377 women) aged 19 years or older were selected from the Korea National Health and Nutrition Examination Surveys (2016–2018, 2021). We classified participants into four groups: early sleep + regular breakfast eaters (group 1), late sleep + regular breakfast eaters (group 2), early sleep + infrequent breakfast eaters (group 3), and late sleep + infrequent breakfast eaters (group 4). In men, group 4 had a lower prevalence of obesity than group 1 (OR 0.78, 95%CI 0.62–0.97), and groups 2, 3, and 4 had a higher prevalence of metabolic syndrome (MetS) than group 1 (OR 1.43, 1.62, and 1.47, respectively). In women, group 4 had a lower prevalence of dyslipidemia than group 1 (OR 0.59, 95%CI 0.44–0.80), and group 2 had a higher prevalence of MetS than group 1 (OR 1.24, 95%CI 1.03–1.50). The combination of skipping breakfast and late sleep timing was associated with the higher prevalence of MetS particularly in men. Moreover, the relationship between breakfast and sleep timing on CVD risk factors differed by sex and age group.

Heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2/B1) is an N6-methyladenosine (m6A) RNA modification regulator and a key determinant of pre- mRNA processing, mRNA metabolism and transportation in cells. Currently, m6A reader proteins such as hnRNPA2/B1 and YTHDF2 has functional roles in mice embryo. However, the role of hnRNPA2/B1 in porcine embryogenic development are unclear. Here, we investigated the developmental competence and mRNA expression levels in porcine parthenogenetic embryos after hnRNPA2/B1 knock-down. HhnRNPA2/B1 was localized in the nucleus during subsequent embryonic development since zygote stage. After hnRNPA2/B1 knock-down using double stranded RNA injection, blastocyst formation rate decreased than that in the control group. Moreover, hnRNPA2/B1 knock-down embryos show developmental delay after compaction. In blastocyste stage, total cell number was decreased. Interestingly, gene expression patterns revealed that transcription of Pou5f1, Sox2, TRFP2C, Cdx2 and PARD6B decreased without changing the junction protein, ZO1, OCLN, and CDH1. Thus, hnRNPA2/B1 is necessary for porcine early embryo development by regulating gene expression through epigenetic RNA modification.

Abstract Background High-quality end-of-life (EOL) care requires both comfort care and the maintenance of dignity. However, delivering EOL in the emergency department (ED) is often challenging. Therefore, we aimed to investigate characteristics of EOL care for dying patients in the ED. Methods We conducted a retrospective cohort study of patients who died of disease in the ED at a tertiary hospital in Korea between January 2018 and December 2020. We examined medical care within the last 24 h of life and advance care planning (ACP) status. Results Of all 222 disease-related mortalities, 140 (63.1%) were men, while 141 (63.5%) had cancer. The median age was 74 years. As for critical care, 61 (27.5%) patients received cardiopulmonary resuscitation, while 80 (36.0%) received mechanical ventilation. The absence of serious illness (p = 0.011) and the lack of an advance statement (p

BackgroundCausal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care. ObjectiveThis paper provides a practical review and tutorial on scalable causal structure learning models with examples of real-world data to help health care audiences understand and apply them. MethodsWe reviewed traditional (combinatorial and score-based) methods for causal structure discovery and machine learning–based schemes. Various traditional approaches have been studied to tackle this problem, the most important among these being the Peter Spirtes and Clark Glymour algorithms. This was followed by analyzing the literature on score-based methods, which are computationally faster. Owing to the continuous constraint on acyclicity, there are new deep learning approaches to the problem in addition to traditional and score-based methods. Such methods can also offer scalability, particularly when there is a large amount of data involving multiple variables. Using our own evaluation metrics and experiments on linear, nonlinear, and benchmark Sachs data, we aimed to highlight the various advantages and disadvantages associated with these methods for the health care community. We also highlighted recent developments in biomedicine where causal structure learning can be applied to discover structures such as gene networks, brain connectivity networks, and those in cancer epidemiology. ResultsWe also compared the performance of traditional and machine learning–based algorithms for causal discovery over some benchmark data sets. Directed Acyclic Graph-Graph Neural Network has the lowest structural hamming distance (19) and false positive rate (0.13) based on the Sachs data set, whereas Greedy Equivalence Search and Max-Min Hill Climbing have the best false discovery rate (0.68) and true positive rate (0.56), respectively. ConclusionsMachine learning–based approaches, including deep learning, have many advantages over traditional approaches, such as scalability, including a greater number of variables, and potentially being applied in a wide range of biomedical applications, such as genetics, if sufficient data are available. Furthermore, these models are more flexible than traditional models and are poised to positively affect many applications in the future.

Summary: Developing drugs for treating Alzheimer’s disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

Abstract Background To compare obstetric and neonatal outcomes in twin pregnancies with or without gestational diabetes mellitus (GDM) before and after changes in GDM diagnostic criteria. Methods This was a retrospective cohort study of 1,764 twin pregnancies including 130 women with GDM (GDM group) and 1,634 women without GDM (non-GDM group). Patients with pregestational diabetes, unknown GDM status, and fetal death at

Defect-free joints of NiTi/austenitic stainless steel wires have extraordinary combined properties of shape memory, superelasticity, and biocompatibility. However, problems arise from the dissimilar welding of NiTi to stainless steel, including the formation of brittle intermetallic compounds and the loss of shape memory and superelastic properties. To overcome the welding challenges of these alloys, phases formed in the weld zone must be identified. Therefore, we used CALPHAD quaternary phase analysis to predict the formation of intermetallic compounds and other probable phases in the weld zone. The calculated results were compared with existing ternary phase diagrams containing Fe, Ni, Ti, and Cr. According to these calculations and experimental observations using electron backscatter diffraction, energy-dispersive X-ray spectroscopy, transmission electron microscopy, and micro X-ray diffraction techniques, brittle Fe2Ti is the predominant phase in the final solidified structure, even in the equilibrium state at the NiTi-weld interface. In addition, a ductile Ni3Ti phase, together with the γ-(Fe,Ni) and B2-(Fe,Ni)Ti phases, is feasible in the weld zone. Moreover, Cr appears as a solid solution with Fe and Ti (BCC_A2 and β(Cr,Ti)) at low Cr concentrations. At higher concentrations, Cr can potentially appear as a σ phase and a C14 Laves phase, which can cause a stress concentration at the NiTi-weld interface, degrading the mechanical properties of dissimilar joints. Therefore, a suitable modification process is required to improve the mechanical properties of NiTi to stainless steel joints. Thus, any adopted strategy should preclude brittle Fe2Ti and facilitate the formation of more ductile phases such as γ-(Fe,Ni).

Various approaches have been applied to red tide monitoring in Korea since reliable information on phytoplankton communities is crucial. In this study, we employed a high-performance liquid chromatography (HPLC) method to analyze two types of red tide, Mesodinium rubrum and Margalefidinium polykrikoides (also known as Cochlodinium polykrikoides), along the southern coasts of Korea. During the M. rubrum red tide on 8 August 2022, an unusual dominance of cryptophytes was observed, being the most dominant phytoplankton group. A significant positive correlation was found between alloxanthin concentrations, a marker pigment of cryptophytes, and M. rubrum cell numbers (p < 0.01, r = 0.830), indicating that HPLC-derived alloxanthin concentrations can serve as a valuable indicator for identifying red tides caused by M. rubrum and estimating cell numbers. However, it is crucial to consider the temporal dynamics of the prey–predator relationship between cryptophytes and M. rubrum. Further investigation is required to understand the environmental conditions that promote cryptophyte predominance and their role in M. rubrum red tide development. In the second field campaign on 29 August 2022, we observed a significant correlation between the concentration of peridinin, a marker pigment for dinoflagellates, and M. polykrikoides cell numbers (p < 0.01, r = 0.663), suggesting that peridinin can serve as a reliable indicator of M. polykrikoides red tides. In conclusion, HPLC-derived pigments, namely alloxanthin and peridinin, can be used to effectively monitor red tides caused by M. rubrum and M. polykrikoides, respectively. However, to overcome certain methodological limitations of HPLC, future studies should explore additional markers or analytical techniques capable of differentiating M. polykrikoides from other coexisting dinoflagellate species. Furthermore, the broad applicability of our method requires thorough investigation in diverse ecosystems to fully comprehend its scope and limitations. Future research should focus on evaluating the method’s efficacy in different contexts, accounting for the distinct traits of the ecosystems under consideration.

Respecting the preference for a place of care is essential for advance care planning in patients with advanced cancer. This retrospective study included adult patients with cancer referred to an inpatient palliative care consultation team at a tertiary acute care hospital in South Korea between April 2019 and December 2020. Patients’ preference for place of care and demographic and clinical factors were recorded, and the actual discharge locations were categorized as home or non-home. Patients discharged home but with unintended hospital visits within 2 months were also investigated. Of the 891 patients referred to the palliative care consultation team, 210 (23.6%) preferred to be discharged home. Among them, 113 (53.8%) were discharged home. No significant differences were found between patients who preferred home discharge and those who did not. Home discharge was higher among female patients (p = 0.04) and lower in those with poor oral intake (p < 0.001) or dyspnea (p = 0.02). Of the 113 patients discharged home, 37 (32.8%) had unintended hospital visits within 2 months. Approximately one-quarter of hospitalized patients with advanced cancer preferred to be discharged home, but only half of them received the home discharge. To meet patients’ preferences for end-of-life care, individual care planning considering relevant factors is necessary.

We investigated the spatio-temporal variations in three key biomolecular compounds (carbohydrates, proteins, and lipids) in particulate organic matter (POM) in the Yellow Sea (YS), South Sea of Korea (SS), East China Sea (ECS), and East Sea (Sea of Japan; ES) in order to estimate the regional annual calorie production rates based on the seasonal data obtained in each region. Carbohydrates were found to be most dominant, followed by lipids across all seas. The euphotic-integral calorie contents of POM during the study period were determined as 53.5 ± 12.6 Kcal m−2 in the YS; 54.2 ± 7.5 Kcal m−2 in the SS; 35.7 ± 9.2 Kcal m−2 in the ECS; and 58.7 ± 6.2 Kcal m−2 in the ES. Utilizing seasonal carbon conversion factors and primary production rates, we estimated the annual calorie productions, which were highest in the ES (1705 Kcal m−2 y−1) and lowest in the ECS (403 Kcal m−2 y−1). This can be attributed to the significantly higher energy efficiency of phytoplankton and faster turnover rate of calorie content. However, caution must be taken when comparing these estimates regionally, considering potential variations in developmental phases among the four regions during the sampling period in 2018. The calorie production rates in this study provide valuable insights into the physiological condition of phytoplankton within specific regions.

Abstract Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

Although numerous mental health applications (apps) are available to the public, the process behind their development is unclear. Many researchers doubt the validity of the mental health content in existing apps and have criticized the lack of evidence-based content regarding the targeted mental health issues, such as trauma, anxiety, and depression. The purpose of this study is to systematically describe the entire process of designing, developing, and evaluating a mental health app in South Korea. Using an instructional systems model, we describe the entire mental health app design and development process through five phases: analysis, design, development, implementation, and evaluation (ADDIE). Using those phases, we conducted expert reviews and usability tests and examined whether the mental health app helped to improve the emotional and psychological issues of users. Overall, participants were highly satisfied with the mental health app, specifically for low- and medium-risk conditions. Our mental health app demonstrated its potential to cultivate mental health literacy and reach a large Korean audience. Further implications are discussed.

Objective: To investigate the association between maternal pre-pregnancy body mass index (BMI) and the risk for gestational diabetes mellitus (GDM) in women with twin pregnancy in South Korea. Materials and methods: We performed a single-center, retrospective cohort study involving 1028 women with twin pregnancy from January 2006 to December 2018 in South Korea. Pregnancies with monoamnionic twins, twin–twin transfusion syndrome, fetal death in utero before 24 weeks, pre-gestational diabetes mellitus, and unknown BMI or GDM status were excluded. Subjects were grouped into four groups based on pre-pregnancy BMI: underweight (

The effect of friction stir welding (FSW) parameters on the microstructure, mechanical and electrical properties of 2024 aluminum alloy and copper joints was investigated. After obtaining the processing window, the optimum values of ultimate tensile strength≈ 142 MPa, elongation≈ 5 %, and electrical resistivity≈ 33 nΩm were obtained by FSW at a rotational speed (W) ≈ 948 rpm and a traverse speed (V) ≈ 85 mm/min. W/V ratio, which stands for the amount of heat input during FSW, had a considerable effect on the joints' properties. At W/V>(W/V)optimum, larger amounts of Al4Cu9 and Al2Cu brittle intermetallic compounds (IMCs), and at W/V

The spatial and temporal environmental conditions in the southern coastal waters of Korea (SCWK) as a complex area, which is influenced by both ocean currents and anthropogenic nutrient inputs, are highly variable. The impacts of environmental factors on the distribution of phytoplankton community structure in the SCWK remain unclear. Based on high-performance liquid chromatography (HPLC) analysis of a variety of photosynthetic pigments, spatial and temporal variations in phytoplankton community compositions were investigated in the SCWK. Cluster analysis, correlation analysis, and generalized additive models (GAMs) were used to explore the major phytoplankton groups and their response patterns to temperature, salinity, depth, and macronutrients. The results indicated that diatoms were the absolute dominant groups in the SCWK for all seasons, especially in cold seasons. Diatoms mainly concentrated at 1% light depths, while high chlorophyll-a concentrations (>3 µg/L) occurred at 100% and 30% light depths with good light conditions. Both clustering and correlation analyses showed that diatoms had a strong positive correlation with macronutrients such as dissolved inorganic nitrogen (DIN), dissolved inorganic phosphate (DIP), and dissolved silica (DSi). The temperature and salinity discrepancy between surface and bottom layers in summer caused a strong water stratification resulting in blocking nutrient-rich bottom water upwelling and leading cyanobacteria to become the dominant groups at 100% and 30% light depths in summer. Consistently, the cyanobacteria were highly associated with high temperature and low salinity in the correlation analysis and GAM results. Compared with diatom-predominant sites, cryptophytes and diatom-dominated sites are characterized by higher average excess nitrate (ExN). Further research on the responses of small-sized prokaryotic phytoplankton especially cyanobacteria to environmental variations and their primary production contributions would warrant a better understanding of the SCWK ecosystem.

The biochemical compositions of marine particulate organic matter (POM) can provide significant information to understanding the physiological conditions of phytoplankton and food quality for their potential consumers. We investigated the seasonal variations in biomolecular and amino acid (AA) compositions of the bulk POM in the southwestern East/Japan Sea from four different sampling months (February, April, August, and October) in 2018. In terms of the biomolecular composition of the POM, overall carbohydrates (CHO) were predominant among three biomolecules accounting for 48.6% followed by lipids (LIP; 35.5%) and proteins (PRT; 15.9%) in the East/Japan Sea. However, markedly seasonal differences in the biomolecular composition of POM were found from February to October, which could be due to seasonally different conditions favorable to phytoplankton growth. Dominant AA constituents to trace POM lability were glycine (GLY), alanine (ALA), and glutamic acid (GLU), suggesting that our POM was the mixtures of decomposing and fresher materials. Furthermore, the significantly negative correlation between the proportion of total essential amino acids (EAAs) and PRT composition (r = -0.627, p< 0.01) was probably reflected by nutrient availability to phytoplankton partitioning EAAs or non-essential AAs (NEAAs). The different biomolecular compounds under un- or favorable growth conditions for phytoplankton could determine the nutritional quality of POM as potential prey as well as degradation status of POM. Therefore, the biochemical compositions of phytoplankton-originated POM hold important ecological implications in various marine ecosystems under ongoing climate changes.