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Abstract This study aimed to develop a machine learning (ML) model for predicting pulmonary embolism (PE) in patients with gastrointestinal cancers, a group at increased risk for PE. We conducted a retrospective, multicenter study analyzing patients who underwent computed tomographic pulmonary angiography (CTPA) between 2010 and 2020. The study utilized demographic and clinical data, including the Wells score and D-dimer levels, to train a random forest ML model. The model’s effectiveness was assessed using the area under the receiver operating curve (AUROC). In total, 446 patients from hospital A and 139 from hospital B were included. The training set consisted of 356 patients from hospital A, with internal validation on 90 and external validation on 139 patients from hospital B. The model achieved an AUROC of 0.736 in hospital A and 0.669 in hospital B. The ML model significantly reduced the number of patients recommended for CTPA compared to the conventional diagnostic strategy (hospital A; 100.0% vs. 91.1%, P

Purpose: Urothelial carcinoma has various molecular subtypes, each with different tumor characteristics. Although it is known that molecular changes occur during tumor progression, little is known about the specifics of these changes. In this study, we performed transcriptional analysis to understand the molecular changes during tumor progression. Materials and Methods: Formalin-fixed, paraffin-embedded tumor tissues were obtained from 12 patients with muscle-invasive bladder cancer (MIBC). The invasive and non-invasive papillary areas were identified in papillary urothelial carcinoma specimens. Immunohistochemistry (IHC) and mRNA sequencing were performed for each tumor area. Results: Patients with CK5/6-negative and CK20-positive non-invasive papillary areas were selected and classified into the IHC switch subgroup (CK5/6-positive and CK20-negative in the invasive area) and the IHC unchanged subgroup (CK5/6-negative and CK20-positive in the invasive area) according to the IHC results of the invasive area. We identified differences in the mRNA expression between the non-invasive papillary and invasive areas of the papillary MIBC tissue samples. In both the non-invasive papillary and invasive areas, the IHC switch subgroup showed basal subtype gene expression, while the IHC unchanged subgroup demonstrated luminal subtype gene expression. Conclusions: The non-invasive papillary area showed a gene expression pattern similar to that of the invasive area. Therefore, even if the non-invasive papillary area exhibits a luminal phenotype on IHC, it can have a basal subtype gene expression depending on the invasive area.

Background Dipeptidyl peptidase-4 (DPP4) inhibitors are frequently prescribed for patients with type 2 diabetes; however, their cost can pose a significant barrier for those with impaired kidney function. This study aimed to estimate the economic benefits of substituting non-renal dose-adjusted (NRDA) DPP4 inhibitors with renal dose-adjusted (RDA) DPP4 inhibitors in patients with both impaired kidney function and type 2 diabetes. Methods This retrospective cohort study was conducted from January 1, 2012 to December 31, 2018, using data obtained from common data models of five medical centers in Korea. Model 1 applied the prescription pattern of participants with preserved kidney function to those with impaired kidney function. In contrast, model 2 replaced all NRDA DPP4 inhibitors with RDA DPP4 inhibitors, adjusting the doses of RDA DPP4 inhibitors based on individual kidney function. The primary outcome was the cost difference between the two models. Results In total, 67,964,996 prescription records were analyzed. NRDA DPP4 inhibitors were more frequently prescribed to patients with impaired kidney function than in those with preserved kidney function (25.7%, 51.3%, 64.3%, and 71.6% in patients with estimated glomerular filtration rates [eGFRs] of ≥60,

Abstract Clostridioides difficile infection (CDI) is a major cause of antibiotic-associated diarrhea and colitis. It is recognized as one of the most significant hospital-acquired infections. Although CDI can develop severe complications and spores of Clostridioides difficile can be transmitted by the fecal-oral route, CDI is occasionally overlooked in clinical settings. Thus, it is necessary to monitor high CDI risk groups, particularly those undergoing antibiotic treatment, to prevent complications and spread. We developed and validated a deep learning-based model to predict the occurrence of CDI within 28 days after starting antibiotic treatment using longitudinal electronic health records. For each patient, timelines of vital signs and laboratory tests with a 35-day monitoring period and a patient information vector consisting of age, sex, comorbidities, and medications were constructed. Our model achieved the prediction performance with an area under the receiver operating characteristic curve of 0.952 (95% CI: 0.932–0.973) in internal validation and 0.972 (95% CI: 0.968–0.975) in external validation. Platelet count and body temperature emerged as the most important features. The risk score, the output value of the model, exhibited a consistent increase in the CDI group, while the risk score in the non-CDI group either maintained its initial value or decreased. Using our CDI prediction model, high-risk patients requiring symptom monitoring can be identified. This could help reduce the underdiagnosis of CDI, thereby decreasing transmission and preventing complications.

Abstract Background Selecting informative genes or eliminating uninformative ones before any downstream gene expression analysis is a standard task with great impact on the results. A carefully curated gene set significantly enhances the likelihood of identifying meaningful biomarkers. Method In contrast to the conventional forward gene search methods that focus on selecting highly informative genes, we propose a backward search method, DenoiseIt, that aims to remove potential outlier genes yielding a robust gene set with reduced noise. The gene set constructed by DenoiseIt is expected to capture biologically significant genes while pruning irrelevant ones to the greatest extent possible. Therefore, it also enhances the quality of downstream comparative gene expression analysis. DenoiseIt utilizes non-negative matrix factorization in conjunction with isolation forests to identify outlier rank features and remove their associated genes. Results DenoiseIt was applied to both bulk and single-cell RNA-seq data collected from TCGA and a COVID-19 cohort to show that it proficiently identified and removed genes exhibiting expression anomalies confined to specific samples rather than a known group. DenoiseIt also showed to reduce the level of technical noise while preserving a higher proportion of biologically relevant genes compared to existing methods. The DenoiseIt Software is publicly available on GitHub at https://github.com/cobi-git/DenoiseIt

Background: KRAS , TP53 , CDKN2A , and SMAD4 have been the main driver mutations in pancreatic ductal adenocarcinoma (PDAC). Studies on the clinical significance and treatment response to 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) regimen in terms of the presence of these mutations remain inconclusive. Objectives: This study aimed to compare the survival outcome and response to FOLFIRINOX chemotherapy based on the presence of four driver mutation genes. Design: A multi-center retrospective study conducted at two tertiary medical centers. Methods: This study analyzed PDAC patients who were treated with FOLFIRINOX chemotherapy as the initial treatment. Tumor specimens were analyzed by a targeted next-generation sequencing platform at two tertiary referral hospitals from January 2016 to March 2022. Patients’ demographics, survival outcomes, and chemotherapeutic response were investigated and compared according to the presence of driver mutations. Results: The analysis included 100 patients. KRAS mutation was identified in 92 (92.0%) patients, followed by TP53 , CDKN2A , and SMAD4 in 63 (63.0%), 18 (18.0%), and 17 (17.0%) patients, respectively. The TP53 wild-type group demonstrated longer overall survival (OS) than the TP53 mutated group (median OS: 29 vs 19 months, p = 0.03), and TP53 served as a prognostic factor for survival (hazard ratio = 1.74, 95% confidence interval: 1.00–3.00, p = 0.048). The difference in OS according to TP53 mutation was intensified in localized pancreatic adenocarcinoma (37 vs 19 months, p = 0.01). The TP53 wild-type group demonstrated a higher objective response rate to FOLFIRINOX chemotherapy than the TP53 mutation group in localized pancreatic adenocarcinoma (50.0% vs 17.6%, p = 0.024). Conclusion: PDAC patients with wild-type TP53 demonstrated longer OS than those with TP53 mutation, and this trend was intensified in patients with localized disease. This result may be due to an impaired response to FOLFIRINOX chemotherapy in patients with TP53 mutation.

Abstract Background Lumbar spinal stenosis (LSS) and spondylolisthesis (SPL) are characterized as degenerative spinal pathologies and share considerable similarities. However, opinions vary on whether to recommend exercise or restrict it for these diseases. Few studies have objectively compared the effects of daily physical activity on LSS and SPL because it is impossible to restrict activities ethnically and practically. We investigated the effect of restricting physical activity due to social distancing (SoD) on LSS and SPL, focusing on the aspect of healthcare burden changes during the pandemic period. Methods We included first-visit patients diagnosed exclusively with LSS and SPL in 2017 and followed them up for two years before and after the implementation of the SoD policy. As controls, patients who first visited in 2015 and were followed for four years without SoD were analyzed. The common data model was employed to analyze each patient’s diagnostic codes and treatments. Hospital visits and medical costs were analyzed by regression discontinuity in time to control for temporal effects on dependent variables. Results Among 33,484 patients, 2,615 with LSS and 446 with SPL were included. A significant decrease in hospital visits was observed in the LSS (difference, -3.94 times/month·100 patients; p = 0.023) and SPL (difference, -3.44 times/month·100 patients; p = 0.026) groups after SoD. This decrease was not observed in the data from the control group. Concerning medical costs, the LSS group showed a statistically significant reduction in median copayment (difference, -$45/month·patient; p

Abstract Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohibited owing to the risk of privacy invasion. Federated learning is a reasonable idea to train distributed multicenter data without direct access; however, a central server to merge and distribute models is needed, which is expensive and hardly approved due to various legal regulations. This paper proposes a continual learning framework for a multicenter study, which does not require a central server and can prevent catastrophic forgetting of previously trained knowledge. The proposed framework contains the continual learning method selection process, assuming that a single method is not omnipotent for all involved datasets in a real-world setting and that there could be a proper method to be selected for specific data. We utilized the fake data based on a generative adversarial network to evaluate methods prospectively, not ex post facto. We used four independent electrocardiogram datasets for a multicenter study and trained the arrhythmia detection model. Our proposed framework was evaluated against supervised and federated learning methods, as well as finetuning approaches that do not include any regulation to preserve previous knowledge. Even without a central server and access to the past data, our framework achieved stable performance (AUROC 0.897) across all involved datasets, achieving comparable performance to federated learning (AUROC 0.901).

Abstract BackgroundAcute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)–based time series data are rare. ObjectiveIn this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)–based model to hospital electronic health record (EHR)–based time series data in patients who took nephrotoxic drugs using a DRN. MethodsWe conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model’s contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. ResultsThis study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase. ConclusionsEarly surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.

The growing complexity and volume of modern software have led to an increase in source code vulnerabilities, posing significant security risks. In response, deep learning-based automated source code vulnerability detection methods, particularly those utilizing source code similarity analysis, have recently emerged as promising solutions. However, existing similarity-based source code vulnerability detection methods frequently fail to fully utilize information from the hierarchical structure of source code and are often computationally expensive, limiting their practicality in real-world scenarios. In this paper, we introduce XTransformer, a novel deep learning-based source code vulnerability detector tailored for comparing target source code against archived vulnerable codes across various levels of the source code’s hierarchical structure by leveraging extra cross-attention imposed on the transformer architecture. Additionally, we propose a specialized training strategy based on supervised contrastive learning to improve XTransformer’s ability to effectively learn and differentiate between vulnerable and non-vulnerable source codes. Comprehensive experiments demonstrate that XTransformer outperforms current state-of-the-art methods across different datasets and code lengths while significantly reducing the inference time compared to other similarity-based methods that utilize hierarchical information from source code.

The rapid evolution of software development, propelled by competitive demands and the continuous integration of new features, frequently leads to inadvertent security oversights. Traditional security practices, often reactive in nature, primarily focus on identifying known vulnerabilities, creating a significant shortfall in detecting emergent, zero-day threats. This paper introduces CODE-SMASH, a novel deep learning-based source code vulnerability detector that utilizes a Siamese neural network with a hierarchical architecture integrating BiGRU and attention mechanisms. Our experiments using real-world datasets, specifically the Chromium and Debian datasets, demonstrate CODE-SMASH’s superiority over existing methods. It achieves significant improvements in detection performance across all key metrics, including accuracy, precision, recall, and F1-score, with average improvements of approximately 8.3%, 11.6%, 27.75%, and 17.7%, respectively, compared to the best-performing existing methods in our experiments. Moreover, CODE-SMASH shows its superior capability in handling complex and lengthy code sequences, with performance improvements for long-length code (60 to 80 lines) in F1 scores of 4.53 percentage points on the Chromium dataset and 5.62 percentage points on the Debian dataset compared to the second-best model’s performance. We believe our research makes a significant contribution to the field of automated vulnerability detection by providing a high-precision solution to the growing challenges in software security. Furthermore, based on our findings, we anticipate that future research could enhance CODE-SMASH by expanding its generalizability to various programming languages and reducing computational demands to improve efficiency.

Today’s cyber-attacks have become increasingly sophisticated and diverse, targeting systems that hold sensitive information, creating the need for continuous cyber exercise and skill development for cyber professionals. Because cyber exercises require training activities and environments that can support a variety of situations, significant technological efforts have been made to build training environments. In line with technological trends, current cyber exercise simulations are being studied to create various cyber scenarios that can help build an intelligent cyber battlefield using big data and artificial intelligence (AI). This requires a large amount and different types of data for learning, as well as a technical system that can manage and update them periodically. The objective of this study is to develop network topology generation and traffic prediction technologies based on intelligent network traffic analysis and AI models for cyber exercise technology systems. To automate training network scenarios, a path generation technology based on graph theory was developed, and the network environment was analyzed based on the amount of transmission by building a software-defined network capable of analyzing and predicting network traffic. A comparison of AI models such as long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent units (GRU) to predict the amount of transmission showed good performance, with BiLSTM showing a better prediction error. The proposed methodology provides insights that can be used to adjust training scenarios during the network design and operation phases, which is expected to help manage the network, increase efficiency, and address security issues.

Terahertz (THz) wave imaging has potential features for medicine, such as cancer detection. Nevertheless, traditional lenses are heavy, bulky, low spatial resolution, and difficult to integrate. Recently, metasurface has emerged as a compelling approach for achieving lightweight, ultrathin, and easy integration. Although many THz metasurface lenses have been proposed for the wave-focusing applications, there are a few THz metalenses that are polarization insensitive and have a high spatial resolution for THz imaging. In this paper, an ultra-thin, planar, polarization-independent, and high numerical aperture metalens has been proposed using double split-ring resonator (DSRR) structures for high-resolution THz wave focusing. The combination method of the propagation phase (adjusting the diameter of the outer ring of the DSRR) and the geometric phase (changing the slit of the DSRR) is applied to build the unit cell library including eight DSRRs, which can cover the full phase from 0 to $2\pi $ and is independent with the polarization of the incident wave. By arranging the DSRRs into the concentric rings on a thin substrate, an ultrathin, polarization-insensitive, and high numerical aperture metalens is designed with a thickness of $0.55\lambda $ , a focal length of $660~\mu \text{m}$ ( $4\lambda$ ), radius of 4.678 mm ( $28.35~\lambda$ ), and numerical aperture of 0.99 that can work at 1.82 THz (wavelength ( $\lambda$ ) of $165~\mu \text{m}$ ). The designed metalens with a near-unity numerical aperture achieves a high spatial resolution of $89~\mu \text{m}$ ( $0.54\lambda$ ), which can produce high-quality images. It means that the proposed metalens can resolve the microscale features separated by a sub-wavelength distance ( $ < 90~\mu \text{m}$ ). Therefore, the suggested metalens can serve as an objective lens for the miniaturized microscopy, opening a new avenue for microscopic THz imaging and showing potential usage in tiny THz imaging systems for cancer detection applications.

Abstract In single-cell transcriptome analysis, numerous biomarkers related to COVID-19 severity, including cell subtypes, genes, and pathways, have been identified. Nevertheless, most studies have focused on severity groups based on clinical features, neglecting immunological heterogeneity within the same severity level. In this study, we employed sample-level clustering using cell-cell interaction scores to investigate patient heterogeneity and uncover novel subtypes. The clustering results were validated using external datasets, demonstrating superior reproducibility and purity compared to gene expression- or gene set enrichment-based clustering. Furthermore, the cell-cell interaction score-based clusters exhibited a strong correlation with the WHO ordinal severity score based on clinical characteristics. By characterizing the identified subtypes through known COVID-19 severity-associated biomarkers, we discovered a “Severe-like moderate” subtype. This subtype displayed clinical features akin to moderate cases; however, molecular features, such as gene expression and cell-cell interactions, resembled those of severe cases. Notably, all patients who progressed from moderate to severe belonged to this subtype, underscoring the significance of cell-cell interactions in COVID-19 patient heterogeneity and severity.

Abstract Background This study aimed to develop a novel combined immune score (CIS)-based model assessing prognosis in triple-negative breast cancer (TNBC). Methods The expression of eight immune markers (PD-1, PD-L1, PD-L2, IDO, TIM3, OX40, OX40L, and H7-H2) was assessed with immunohistochemistry on the tumor cells (TCs) and immune cells (ICs) of 227 TNBC cases, respectively, and subsequently associated with selected clinicopathological parameters and survival. Data retrieved from The Cancer Genome Atlas (TCGA) were further examined to validate our findings. Results All immune markers were often expressed in TCs and ICs, except for PD-1 which was not expressed in TCs. In ICs, the expression of all immune markers was positively correlated between one another, except between PD-L1 and OX40, also TIM3 and OX40. In ICs, PD-1, PD-L1, and OX40L positive expression was associated with a longer progression-free survival (PFS; p = 0.040, p = 0.020, and p = 0.020, respectively). In TCs, OX40 positive expression was associated with a shorter PFS (p = 0.025). Subsequently, the TNBC patients were classified into high and low combined immune score groups (CIS-H and CIS-L), based on the expression levels of a selection of biomarkers in TCs (TCIS-H or TCIS-L) and ICs (ICIS-H or ICIS-L). The TCIS-H group was significantly associated with a longer PFS (p

Abstract Both intradialytic hypotension (IDH) and hypertension (IDHTN) are associated with poor outcomes in hemodialysis patients, but a model predicting dual outcomes in real-time has never been developed. Herein, we developed an explainable deep learning model with a sequence-to-sequence-based attention network to predict both of these events simultaneously. We retrieved 302,774 hemodialysis sessions from the electronic health records of 11,110 patients, and these sessions were split into training (70%), validation (10%), and test (20%) datasets through patient randomization. The outcomes were defined when nadir systolic blood pressure (BP)

Abstract Hypertension is a chronic disease that requires long‐term follow‐up in many patients, however, optimal visit intervals are not well‐established. This study aimed to evaluate the incidences of major cardiovascular events (MACEs) according to visit intervals. We analyzed data from 9894 hypertensive patients in the Korean Hypertension Cohort, which enrolled and followed up 11,043 patients for over 10 years. Participants were classified into five groups based on their median visit intervals (MVIs) during the 4‐year period and MACEs were compared among the groups. The patients were divided into clinically relevant MVIs of one (1013; 10%), two (1299; 13%), three (2732; 28%), four (2355; 24%), and six months (2515; 25%). The median follow‐up period was 5 years (range: 1745 ± 293 days). The longer visit interval groups did not have an increased cumulative incidence of MACE (12.9%, 11.8%, 6.7%, 5.9%, and 4%, respectively). In the Cox proportional hazards model, those in the longer MVI group had a smaller hazard ratio (HR) for MACEs or all‐cause death: 1.77 (95% confidence interval [CI], 1.45–2.17), 1.7 (95% CI: 1.41–2.05), 0.90 (95% CI: 0.74–1.09) and 0.64 (95% CI: 0.52–0.79), respectively (Reference MVI group of 75–104 days). In conclusion, a follow‐up visits with a longer interval of 3–6 months was not associated with an increased risk of MACE or all‐cause death in hypertensive patients. Therefore, once medication adjustment is stabilized, a longer interval of 3–6 months is reasonable, reducing medical expenses without increasing the risk of cardiovascular outcomes.

Background The genetically predicted lipid-lowering effect of HMGCR or PCSK9 variant can be used to assess drug proxy effects on kidney function. Methods Mendelian randomization (MR) analysis-identified HMGCR and PCSK9 genetic variants were used to predict the low-density lipoprotein (LDL) cholesterol-lowering effects of medications targeting related molecules. Primary summary-level outcome data for log-estimated glomerular filtration rate (eGFR; creatinine) were provided by the CKDGen Consortium (n = 1,004,040 European) from a meta-analysis of CKDGen and UK Biobank data. We also conducted a separate investigation of summary-level data from CKDGen (n = 567,460, log-eGFR [creatinine]) and UK Biobank (n = 436,581, log-eGFR [cystatin C]) samples. Summary-level MRs using an inverse variance weighted method and pleiotropy-robust methods were performed. Results Summary-level MR analysis indicated that the LDL-lowering effect predicted genetically by HMGCR variants (50-mg/dL decrease) was significantly associated with a decrease in eGFR (–1.67%; 95% confidence interval [CI], –2.20% to –1.13%). Similar significance was found in results from the pleiotropy-robust MR methods when the CKDGen and UK Biobank data were analyzed separately. However, the LDL-lowering effect predicted genetically by PCSK9 variants was significantly associated with an increase in eGFR (+1.17%; 95% CI, 0.10%–2.25%). The results were similarly supported by the weighted median method and in each CKDGen and UK Biobank dataset, but the significance obtained by MR-Egger regression was attenuated. Conclusion Genetically predicted HMG-CoA reductase inhibition was associated with low eGFR, while genetically predicted PCSK9 inhibition was associated with high eGFR. Clinicians should consider that the direct effect of different types of lipid-lowering medication on kidney function can vary.

Objectives The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable time-series model. Results The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based time-series classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.

Abstract Interleukins (ILs), key cytokine family of inflammatory response, are closely associated with kidney function. However, the causal effect of various ILs on kidney function needs further investigation. Here we show two-sample summary-level Mendelian randomization (MR) analysis that examined the causality between serum IL levels and kidney function. Genetic variants with strong association with serum IL levels were obtained from a previous genome-wide association study meta-analysis. Summary-level data for estimated glomerular filtration rate (eGFR) were obtained from CKDGen database. As a main MR analysis, multiplicative random-effects inverse-variance weighted method was performed. Pleiotropy-robust MR analysis, including MR-Egger with bootstrapped error and weighted median methods, were also implemented. We tested the causal estimates from nine ILs on eGFR traits. Among the results, higher genetically predicted serum IL-1 receptor antagonist level was significantly associated with higher eGFR values in the meta-analysis of CKDGen and the UK Biobank data. In addition, the result was consistent towards eGFR decline phenotype of the outcome database. Otherwise, nonsignificant association was identified between other genetically predicted ILs and eGFR outcome. These findings support the clinical importance of IL-1 receptor antagonist-associated pathway in relation to kidney function in the general individuals, particularly highlighting the importance of IL-1 receptor antagonist.

Abstract Background The Khorana score (KS) has not been well studied in East Asian cancer patients, who have different genetic backgrounds for inherited thrombophilia, body metabolism, and cancer epidemiology. Methods By using the Common Data Model, we retrospectively collected deidentified data from 11,714 consecutive newly diagnosed cancer patients who underwent first-line chemotherapy from December 2015 to December 2021 at a single institution in Korea, and we applied the KS for cancer-associated thrombosis (CAT) prediction. Age at diagnosis, sex, and use of highly thrombogenic chemotherapeutics were additionally investigated as potential risk factors for CAT development. Results By 6 months after chemotherapy initiation, 207 patients (1.77%) experienced CAT. Only 0.4% had a body mass index (BMI) ≥ 35 kg/m2 and changing the cutoff to 25 kg/m2 improved the prediction of CAT. Age ≥ 65 years and the use of highly thrombogenic chemotherapeutics were independently associated with CAT development. KS values of 1 ~ 2 and ≥ 3 accounted for 52.3% and 7.6% of all patients, respectively, and the incidence of CAT in these groups was 2.16% and 4.16%, respectively, suggesting a lower incidence of CAT in the study population than in Westerners. The KS component regarding the site of cancer showed a good association with CAT development but needed some improvement. Conclusion The KS was partially validated to predict CAT in Korean cancer patients undergoing modern chemotherapy. Modifying the BMI cutoff, adding other risk variables, and refining the use of cancer-site data for CAT risk prediction may improve the performance of the KS for CAT prediction in East Asian patients.

Summary: Background: Diabetes is a major public health concern. We aimed to evaluate the long-term risk of incident type 2 diabetes in a non-diabetic population using a deep learning model (DLM) detecting prevalent type 2 diabetes using electrocardiogram (ECG). Methods: In this retrospective study, participants who underwent health checkups at two tertiary hospitals in Seoul, South Korea, between Jan 1, 2001 and Dec 31, 2022 were included. Type 2 diabetes was defined as glucose ≥126 mg/dL or glycated haemoglobin (HbA1c) ≥ 6.5%. For survival analysis on incident type 2 diabetes, we introduced an additional variable, diabetic ECG, which is determined by the DLM trained on ECG and corresponding prevalent diabetes. It was assumed that non-diabetic individuals with diabetic ECG had a higher risk of incident type 2 diabetes than those with non-diabetic ECG. The one-dimensional ResNet-based model was adopted for the DLM, and the Guided Grad-CAM was used to localise important regions of ECG. We divided the non-diabetic group into the diabetic ECG group (false positive) and the non-diabetic ECG (true negative) group according to the DLM decision, and performed a Cox proportional hazard model, considering the occurrence of type 2 diabetes more than six months after the visit. Findings: 190,581 individuals were included in the study with a median follow-up period of 11.84 years. The areas under the receiver operating characteristic curve for prevalent type 2 diabetes detection were 0.816 (0.807–0.825) and 0.762 (0.754–0.770) for the internal and external validations, respectively. The model primarily focused on the QRS duration and, occasionally, P or T waves. The diabetic ECG group exhibited an increased risk of incident type 2 diabetes compared with the non-diabetic ECG group, with hazard ratios of 2.15 (1.82–2.53) and 1.92 (1.74–2.11) for internal and external validation, respectively. Interpretation: In the non-diabetic group, those whose ECG was classified as diabetes by the DLM were at a higher risk of incident type 2 diabetes than those whose ECG was not. Additional clinical research on the relationship between the phenotype of ECG and diabetes to support the results and further investigation with tracked data and various ECG recording systems are suggested for future works. Funding: National Research Foundation of Korea.

We report here a novel anti-cancer therapy based on an avian-host-specific serotype Salmonella enterica serovar Gallinarum (S. Gallinarum) deficient in ppGpp synthesis. To monitor the tumor targeting, a bioluminescent ΔppGpp S. Gallinarum was constructed and injected intravenously into mice bearing syngeneic and human xenograft tumors. Strong bioluminescent signals were detected specifically in all grafted tumors at 2 days post-injection (dpi). The bacterial counts in normal and tumor tissue at 1 dpi revealed that ΔppGpp S. Gallinarum reached >108 CFU/g in tumor tissue and 106–107 CFU/g in endothelial organs; counts were much lower in other organs. At 16 dpi, ΔppGpp S. Gallinarum counts in tumor tissue decreased to ∼106 CFU/g, while those in the other organs became undetectable. A strong anti-cancer effect was observed after the injection of ΔppGpp S. Gallinarum into BALB/c mice grafted with CT26 colon cancer cells. This could be attributed to reduced virulence, which allowed the administration of at least a 10-fold greater dose (108 CFU) of ΔppGpp S. Gallinarum than other attenuated strains of S. enterica serovar Typhimurium (≤107 CFU). An advantage of the avian-specific S. Gallinarum as a cancer therapeutic should be a reduced capacity to cause infections or harm in humans.

Abstract Background Ageing traits and frailty are important health issues in modern medicine. Evidence supporting the causal effects of tobacco smoking on various ageing traits is required. Methods This study performed Mendelian randomization (MR) analysis instrumenting 377 genetic variants associated with being an ever‐smoker at a genome‐wide significance level to test the causal estimates from tobacco smoking. The outcome data were obtained from 337 138 white British ancestry participants from the UK Biobank. Leucocyte telomere length, appendicular lean mass index, subjective walking pace, handgrip strength, and wristband accelerometry‐determined physical activity degree were collected as ageing‐related outcomes. Summary‐level MR analysis was performed using the inverse variance‐weighted method and pleiotropy‐robust MR methods, including weighted median and MR‐Egger. Observational association between the outcome traits and phenotypically being an ever‐smoker was also investigated. Results Summary‐level MR analysis indicated that a higher genetic predisposition for tobacco smoking was significantly associated with shorter leucocyte telomere length (twofold increase in prevalence of smoking towards standardized Z‐score, −0.041 [−0.054, −0.028]), lower appendicular lean mass index (−0.007 [−0.010, −0.005]), slower walking pace (ordinal category, −0.047 [−0.054, −0.033]) and lower time spent on moderate‐to‐vigorous physical activity (hours per week, −0.39 [−0.56, −0.23]). The causal estimates were non‐significant towards handgrip strength phenotype (kg, 0.074 [−0.055, 0.204]). Pleiotropy‐robust MR results generally supported the main causal estimates. The observational findings also showed significant association between being an ever‐smoker and the ageing traits. Conclusions Genetically predicted and observational tobacco smoking status are significantly associated with poor ageing phenotypes. Healthcare providers may continue to reduce tobacco use, which may be helpful in reducing the burden of ageing and frailty.

Construction sites remain highly perilous work environments globally, exposing employees to numerous hazards that can result in severe injuries or fatalities. To resolve this several solutions based on quantitative approaches have been developed. However the wide adoption of preexisting solutions is hindered by lack of accuracy. To this aim the development of an efficient fuzzy inference system has become a de-facto necessity. In this paper, we propose an edge inference framework based on multi-layered fuzzy logic for safety of construction workers. The proposed system employs an edge computing-based framework where IoT devices collect, store, and manage data to offer safety services. Multi-layer fuzzy logic is applied to infer the worker safety index based on rules that consist of construction environment factors. The multi-layer fuzzy logic is fed with weather, building and worker data collected from IoT nodes as inputs. The safety risk assessment process involves analyzing various factors. Weather information, such as temperature, humidity, and rainfall data, is considered to assess the risk to safety. The condition of the building is evaluated by analyzing load, strain, and inclination data. Additionally, the safety risk to workers is analyzed by taking into account their heart rate and location information. The initial layer's outputs are utilized as inputs for the subsequent layer, where an integrated safety index is inferred. Ultimately, the safety index is generated as the final outcome. The system's results are conveyed through warnings and an error measurement on a safety scale ranging from 1 to 10. Furthermore, web service is developed to allow the construction management to check the worker safety condition of the construction site in real-time, while also monitoring the operational status of the IoT devices, allowing for the early detection of sensor malfunction and the subsequent guarantee of worker safety. Extensive evaluations conducted to test the performance of the developed framework verify its efficiency to provide improved risk assessment, real-time monitoring, and proactive safety actions, encouraging a safer and more productive work environment.

With the proliferation of network systems, the boundaries between cyber and physical environments are blurring, leading to an increased risk of sophisticated cyber-attacks equipped with advanced technologies. In particular, as advancements in artificial intelligence through learning models have led to automated attacks and attack scenarios, countries are implementing cyber training and constructing training systems to respond to cyber security threats. This cyber training is based on existing cyber-attacks and conducted in virtual spaces similar to reality, generating network traffic through simulators and focusing on training for attack response and cyber resilience. However, the exponential increase in the number of network-based devices and the amount of network traffic they generate is leading to a gradual increase in threats to cyber security. In this study, first investigated the existing port number-based network traffic classification technologies and payload-based network traffic classification technologies to identify their shortcomings in the current network environment. We then categorized existing studies into supervised, unsupervised, and reinforcement learning to analyze the technology of classifying network traffic based on learning models as well as classification methods, procedures, performance standards, evaluation methods, quality of service/quality of experience, etc. Based on the analysis, presented limitations for application to training networks according to the learning method and suggested recommendations for establishing future research directions. Therefore, refining learning model-based network traffic classification technology will contribute to the construction of automated cyber training grounds such as cyber-attack–defense scenarios, network traffic anomaly detection, and maximizing cumulative rewards.

Monitoring construction site safety through physical observations is inherently flawed due to the complex and dynamic nature of construction sites. To overcome these challenges and enhance worker safety management, decentralized model training-assisted edge intelligence emerges as a promising solution. However, despite the potential benefits, our investigation reveals that no research for worker safety prediction has been grounded in the Federated Learning (FL) approach. In this context, we present a novel approach to worker safety prediction, leveraging FL in outdoor construction environments while preserving the privacy and security of sensitive data. Our methodology involves deploying sensor-based IoT devices at construction sites to collect highly granular spatial and temporal weather, building, and worker data. This data is then collaboratively utilized for training Deep Neural Network (DNN) models on the edge nodes in a cross-silos manner. To implement our approach, we establish a test-bed utilizing the EdgeX framework and constrained devices such as Raspberry Pi 4Bs, acting as edge nodes. Following the collaborative training, the resultant global model is deployed on participating nodes for edge inference, ensuring optimal network resource utilization and data privacy. The experimental results demonstrate the efficacy of the proposed approach in improving the utilization of construction safety management systems and reducing the risk of accidents and fatalities in the future. The outcome is a system that exhibits enhanced speed and responsiveness, a crucial aspect for time-sensitive applications such as the prediction of worker safety.

Water drilling machines are used to drill boreholes in the ground to extract groundwater. The resources required for water drilling vary from region to region due to underground water table depth and ground soil layer. Water drilling on a hard underground soil layer requires different resources than a soft underground. The proposed study facilitates the drilling industry by selecting the region with a soft land layer and increasing the penetration rate. Furthermore, the number of days and water table depth prediction allows the drilling industry to estimate the depth of the water table and time resources to reach the water table at different locations. The classification techniques classify the region based on the soil land layer. Regression techniques are used for predicting water table depth and number of days. The experiments are performed on a borehole log dataset provided by a research organization. This study used Support Vector Machine, TabNet, and Deep Tabular models to predict the land soil layer and compare the results with our proposed Ensemble Weighted Voting Soil Layer Classifier (EWV-SLC). The performance of the classification model is evaluated using accuracy, Precision, Recall, and F1 Score. The experimental finding shows that the EWV-SLC model performs better in accuracy and F1 score than other machine learning techniques. The performance of the regression model is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE),Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). In a days and water table depth prediction phase Support, Vector Regressor, Deep Neural Network, and TabNet Regressor are used, and compare the results with our proposed Ensemble Number of Days (E-NOD) and Ensemble Water Table depth (E-WTD) Regressor model. E-NOD and E-WTD models achieved less MAE, RMSE, and MSE than other machine learning methods.

Abstract Auscultation, a cost-effective and non-invasive part of physical examination, is essential to diagnose pediatric respiratory disorders. Electronic stethoscopes allow transmission, storage, and analysis of lung sounds. We aimed to develop a machine learning model to classify pediatric respiratory sounds. Lung sounds were digitally recorded during routine physical examinations at a pediatric pulmonology outpatient clinic from July to November 2019 and labeled as normal, crackles, or wheezing. Ensemble support vector machine models were trained and evaluated for four classification tasks (normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing) using K-fold cross-validation (K = 10). Model performance on a prospective validation set (June to July 2021) was compared with those of pediatricians and non-pediatricians. Total 680 clips were used for training and internal validation. The model accuracies during internal validation for normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing were 83.68%, 83.67%, 80.94%, and 90.42%, respectively. The prospective validation (n = 90) accuracies were 82.22%, 67.74%, 67.80%, and 81.36%, respectively, which were comparable to pediatrician and non-pediatrician performance. An automated classification model of pediatric lung sounds is feasible and maybe utilized as a screening tool for respiratory disorders in this pandemic era.

Abstract Background Germline mutations of breast cancer susceptibility gene BRCA1 and BRCA2 (gBRCA1/2) are associated with elevated risk of breast cancer in young women in Asia. BRCA1 and BRCA2 proteins contribute to genomic stability through homologous recombination (HR)-mediated double-strand DNA break repair in cooperation with other HR-related proteins. In this study, we analyzed the targeted sequencing data of Korean breast cancer patients with gBRCA1/2 mutations to investigate the alterations in HR-related genes and their clinical implications. Materials and methods Data of the breast cancer patients with pathogenic gBRCA1/2 mutations and qualified targeted next-generation sequencing, SNUH FiRST cancer panel, were analyzed. Single nucleotide polymorphisms, small insertions, and deletions were analyzed with functional annotations using ANNOVAR. HR-related genes were defined as ABL1, ATM, ATR, BARD1, BRCA1, BRCA2, CDKN1A, CDKN2A, CHEK1, CHEK2, FANCA, FANCD2, FANCG, FANCI, FANCL, KDR, MUTYH, PALB2, POLE, POLQ, RAD50, RAD51, RAD51D, RAD54L, and TP53. Mismatch-repair genes were MLH1, MSH2, and MSH6. Clinical data were analyzed with cox proportional hazard models and survival analyses. Results Fifty-five Korean breast cancer patients with known gBRCA1/2 mutations and qualified targeted NGS data were analyzed. Ethnically distinct mutations in gBRCA1/2 genes were noted, with higher frequencies of Val1833Ser (14.8%), Glu1210Arg (11.1%), and Tyr130Ter (11.1%) in gBRCA1 and Arg2494Ter (25.0%) and Lys467Ter (14.3%) in gBRCA2. Considering subtypes, gBRCA1 mutations were associated with triple-negative breast cancers (TNBC), while gBRCA2 mutations were more likely hormone receptor-positive breast cancers. At least one missense mutation of HR-related genes was observed in 44 cases (80.0%). The most frequently co-mutated gene was TP53 (38.1%). In patients with gBRCA1/2 mutations, however, genetic variations of TP53 occurred in locations different from the known hotspots of those with sporadic breast cancers. The patients with both gBRCA1/2 and TP53 mutations were more likely to have TNBC, high Ki-67 values, and increased genetic mutations, especially of HR-related genes. Survival benefit was observed in the TP53 mutants of patients with gBRCA2 mutations, compared to those with TP53 wild types. Conclusion Our study showed genetic heterogeneity of breast cancer patients with gBRCA1 and gBRCA2 mutations in the Korean populations. Further studies on precision medicine are needed for tailored treatments of patients with genetic diversity among different ethnic groups.

Objectives The purpose of this study is to provide basic data for the development of a rapid and easy to measure alternative indicator for estimating the number of cyanobacteria cells in the field.Methods:Phycocyanin, algal cell number, and water quality items were measured at the left, middle, and right points of Maery Bridge near the Mulgeum and Maery algae monitoring network located downstream of the Nakdong River. The phycocyanin concentration(PC) was measured in the field using the LISST-HAB sensor, which is a fluorescence concentration analyzer, and comparative analysis was performed with the number of algal cells by algae species. Results and Discussion As a limiting factor for blue-green algae occurrence, the correlation coefficient (r) between T-P and Blue-green algae (BGA) was 0.58 (≥0.3). Phycocyanin and Blue-green algae showed a high significant correlation at 0.96 (≥0.3). Prob (F-statistics) of the blue-green algae cell count estimation formula using phycocyanin was less than 0.05, showing a significant result. The estimation formula was estimated BGA(cells/mL) = 326.95×PC+50,484. As a result of the analysis of the predicted and measured values of Blue-green algae, r2 was 0.988, indicating a high coefficient of determination and 92.5% accuracy. Conclusion The correlation analysis with the number of cyanobacteria showed a high significant correlation with phycocyanin of 0.96 (≥0.3), followed by T-P, a limiting factor for the occurrence of cyanobacteria, of 0.58 (≥0.3) indicating a significant correlation. In addition, the formula for estimating the number of blue-green algae cells using phycocyanin was found to be BGA (cells/mL)=326.95×PC+50,484 in which r2 was 0.927, and Prob (F-statistics) was less than 0.05. As a result of analyzing the predicted and measured values ​​of cyanobacteria cell numbers, r2 was 0.988, indicating a high coefficient of determination, with accuracy of 92.5%. Through this results, it reveals that the number of cyanobacteria cells can be estimated using the sensor of the fluorescence concentration analyzer at the target point by substituting the phycocyanin concentration into the blue-green algae cell number estimation formula.

Abstract Background Acute appendicitis often presents with vague abdominal pain, which fosters diagnostic challenges to clinicians regarding early detection and proper intervention. This is even more problematic with individuals with severe psychiatric disorders who have reduced sensitivity to pain due to long-term or excessive medication use or disturbed bodily sensation perceptions. This study aimed to determine whether psychiatric disorder, psychotropic prescription, and treatment compliance increase the risks of complicated acute appendicitis. Methods The diagnosis records of acute appendicitis from four university hospitals in Korea were investigated from 2002 to 2020. A total of 47,500 acute appendicitis-affected participants were divided into groups with complicated and uncomplicated appendicitis to determine whether any of the groups had more cases of psychiatric disorder diagnoses. Further, the ratio of complicated compared to uncomplicated appendicitis in the mentally ill group was calculated regarding psychotropic dose, prescription duration, and treatment compliance. Results After adjusting for age and sex, presence of psychotic disorder (odds ratio [OR]: 1.951; 95% confidence interval [CI]: 1.218–3.125), and bipolar disorder (OR: 2.323; 95% CI: 1.194–4.520) was associated with a higher risk of having complicated appendicitis compared with absence of psychiatric disorders. Patients who are taking high-daily-dose antipsychotics, regardless of prescription duration, show high complicated appendicitis risks; High-dose antipsychotics for

Abstract R-peak detection is an essential step in analyzing electrocardiograms (ECGs). Previous deep learning models reported their performance primarily in a single database, and some models did not perform at the highest levels when applied to a database different from the testing database. To achieve high performances in cross-database validations, we developed a novel deep learning model for R-peak detection using stationary wavelet transform (SWT) and separable convolution. Three databases (i.e., the MIT-BIH Arrhythmia [MIT-BIH], the Institute of Cardiological Technics [INCART], and the QT) were used in both the training and testing models, and the MIT-BIH ST Change (MIT-BIH-ST), European ST-T, TELE and MIT-BIH Noise Stress Test (MIT-BIH-NST) databases were further used for testing. The detail coefficient of level 4 decomposition by SWT and the first derivative from filtered ECGs were used for model inputs, and the interval of 150 ms centered at marked peaks was used for labels. Separable convolution with atrous spatial pyramidal pooling was selected as the model’s architecture, and noise-augmented waveforms of 5.69 s duration (2048 size in 360 Hz) were used in training. The model performance was evaluated using cross-database validation. The F1 scores of the peak detection model were 0.9994, 0.9985, and 0.9999 in the MIT-BIH, INCART, and QT databases, respectively. When the above three databases were pooled, the F1 scores were 0.9993 for fivefold cross-validation and 0.9991 for cross-database validation. The model performance remained high for MIT-BIH-ST, European ST-T, and TELE, with F1 scores of 0.9995, 0.9988, and 0.9790, respectively. The model performance when trained by severe noise augmentation increased for the MIT-BIH-NST database (F1 scores from 0.9504 to 0.9759) and decreased for the MIT-BIH database (F1 scores from 0.9994 to 0.9991). The present SWT and separable convolution-based model for R-peak detection yields a high performance even for cross-database validations.

Abstract In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can visualize fibrosis in superficial and deep locations. For standardized measurement, verification of deep learning (DL)-based recognition was performed. A cross-sectional, observational cohort trial was conducted. After narrowing window width of the absorptive values in CT images, SegNet-based semantic segmentation model of every pixel into 5 classes (air, skin, muscle/water, fat, and fibrosis) was trained (65%), validated (15%), and tested (20%). Then, 4 indices were formulated and compared with the standardized circumference difference ratio (SCDR) and bioelectrical impedance (BEI) results. In total, 2138 CT images of 27 chronic unilateral lymphedema patients were analyzed. Regarding fibrosis segmentation, the mean boundary F1 score and accuracy were 0.868 and 0.776, respectively. Among 19 subindices of the 4 indices, 73.7% were correlated with the BEI (partial correlation coefficient: 0.420–0.875), and 13.2% were correlated with the SCDR (0.406–0.460). The mean subindex of Index 2 $$\left( {\frac{{P_{Fibrosis\, in\, Affected} - P_{Fibrosis\, in\, Unaffected} }}{{P_{Limb\, in\, Unaffected} }}} \right)$$ P F i b r o s i s i n A f f e c t e d - P F i b r o s i s i n U n a f f e c t e d P L i m b i n U n a f f e c t e d presented the highest correlation. DL has potential applications in CT image-based lymphedema-induced fibrosis recognition. The subtraction-type formula might be the most promising estimation method.

ObjectiveAlthough pediatric epilepsy is an independent disease entity, it is often observed in pediatric neurodevelopmental disorders (NDDs) as a major or minor clinical feature, which might provide diagnostic clues. This study aimed to identify the clinical and genetic characteristics of patients with epilepsy in an NDD cohort and demonstrate the importance of genetic testing.MethodsWe retrospectively analyzed the detailed clinical differences of pediatric NDD patients with epilepsy according to their genetic etiology. Among 1,213 patients with NDDs, 477 were genetically diagnosed by exome sequencing, and 168 had epilepsy and causative variants in 129 genes. Causative genes were classified into two groups: (i) the “epilepsy-genes” group resulting in epilepsy as the main phenotype listed in OMIM, Epi25, and ClinGen (67 patients) and (ii) the “NDD-genes” group not included in the “epilepsy-genes” group (101 patients).ResultsPatients in the “epilepsy-genes” group started having seizures, often characterized by epilepsy syndrome, at a younger age. However, overall clinical features, including treatment responses and all neurologic manifestations, showed no significant differences between the two groups. Gene ontology analysis revealed the close interactions of epilepsy genes associated with ion channels and neurotransmitters.ConclusionWe demonstrated a similar clinical presentation of different gene groups regarding biological/molecular processes in a large NDDs cohort with epilepsy. Phenotype-driven genetic analysis should cover a broad scope, and further studies are required to elucidate integrated pathomechanisms.

Abstract Oxygen evolution reaction (OER) under acidic conditions becomes of significant importance for the practical use of a proton exchange membrane (PEM) water electrolyzer. In particular, maximizing the mass activity of iridium (Ir) is one of the maiden issues. Herein, the authors discover that the Ir‐doped calcium copper titanate (CaCu₃Ti₄O₁₂, CCTO) perovskite exhibits ultrahigh mass activity up to 1000 A gIr−1 for the acidic OER, which is 66 times higher than that of the benchmark catalyst, IrO2. By substituting Ti with Ir in CCTO, metal‐oxygen (M‐O) covalency can be significantly increased leading to the reduced energy barrier for charge transfer. Further, highly polarizable CCTO perovskite referred to as “colossal dielectric”, possesses low defect formation energy for oxygen vacancy inducing a high number of oxygen vacancies in Ir‐doped CCTO (Ir‐CCTO). Electron transfer occurs from the oxygen vacancies and Ti to the substituted Ir consequentially resulting in the electron‐rich Ir and ‐deficient Ti sites. Thus, favorable adsorptions of oxygen intermediates can take place at Ti sites while the Ir ensures efficient charge supplies during OER, taking a top position of the volcano plot. Simultaneously, the introduced Ir dopants form nanoclusters at the surface of Ir‐CCTO, which can boost catalytic activity for the acidic OER.

In this study, we developed a thermal storage medium comprising porous activated carbon filled with organic phase-change materials (PCMs) that utilizes the latent heat of phase-change to absorb heat during heating and release heat during cooling. For the activated carbon, we used both charcoal-based powdered activated carbon (250–350 mesh) and granular activated carbon. The organic phase-change materials used in the experiments were dodecane, tridecane, tetradecane, and pentadecane. Material properties such as thermal conductivity, latent heat, and melting temperature range were evaluated experimentally and theoretically, with the results observed to be consistent. The cyclic thermal performance of the proposed medium was also evaluated. Notably, filling the activated carbon with a mixture of organic PCMs resulted in the highest temperature-moderating effect. The procedure and results presented in this study are expected to aid in further improvement in the performance of thermal storage media containing PCM where stable temperatures are required, including building heating and cooling.

Background: Hypoxia is a well-recognized characteristic of the tumor microenvironment of solid cancers. This study aimed to analyze hypoxia-related genes shared by groups based on tumor location. Methods: A total of 9 hypoxia-related pathways from the Kyoto Encyclopedia of Genes and Genomes database or the Reactome database were selected, and 850 hypoxia-related genes were analyzed. Based on their anatomical locations, 14 tumor types were categorized into 6 groups. The group-specific genetic risk score was classified as high- or low-risk based on mRNA expression, and survival outcomes were evaluated. Results: The risk scores in the Female Reproductive group and the Lung group were internally and externally validated. In the Female Reproductive group, CDKN2A, FN1, and ITGA5 were identified as hub genes associated with poor prognosis, while IL2RB and LEF1 were associated with favorable prognosis. In the Lung group, ITGB1 and LDHA were associated with poor prognosis, and GLS2 was associated with favorable prognosis. Functional enrichment analysis showed that the Female Reproductive group was enriched in relation to cilia and skin, while the Lung group was enriched in relation to cytokines and defense. Conclusions: This analysis may lead to better understanding of the mechanisms of cancer progression and facilitate establishing new biomarkers for prognosis prediction.

Objectives. The BRAFV600E mutation is a major driver mutation in papillary thyroid cancer. The aim of this study was to elucidate the correlation between DNA methylation and gene expression changes induced by the BRAFV600E mutation in thyroid cells. Methods. We used Nthy/BRAF cell lines generated by transfection of Nthy/ori cells with the wild-type BRAF gene (Nthy/WT cells) and the V600E mutant-type BRAF gene (Nthy/V600E cells). We performed gene expression microarray and DNA methylation array analyses for Nthy/WT and Nthy/V600E cells. Two types of array data were integrated to identify inverse correlations between methylation and gene expression. The results were verified in silico using data from The Cancer Genome Atlas (TCGA) and in vivo through pyrosequencing and quantitative real-time polymerase chain reaction (qRT-PCR). Results. In the Nthy/V600E cells, 199,821 probes were significantly hypermethylated, and 697 genes showed a “hypermethylation-downregulation” pattern in Nthy/V600E. Tumor suppressor genes and apoptosis-related genes were included. In total, 66,446 probes were significantly hypomethylated, and 227 genes showed a “hypomethylation-upregulation” pattern in Nthy/V600E cells. Protooncogenes and developmental protein-coding genes were included. In the TCGA analysis, 491/697 (70.44%) genes showed a hypermethylation-downregulation pattern, and 153/227 (67.40%) genes showed a hypomethylation-upregulation pattern. Ten selected genes showed a similar methylation-gene expression pattern in pyrosequencing and qRT-PCR. Conclusion. Induction of the BRAFV600E mutation in thyroid cells led to frequent hypermethylation. Anticancer genes, such as those involved in tumor suppression or apoptosis, were downregulated by upstream hypermethylation, whereas carcinogenic genes, such as protooncogenes, were upregulated by hypomethylation. Our results suggest that the BRAFV600E mutation in thyroid cells modulates DNA methylation and results in cancer-related gene expression.

Mendelian randomization analyses of estimated glomerular filtration rate (eGFR) in the UK Biobank cohort provide insight into the causal effects of kidney function on 60 biochemical parameters.

Background Appropriate monitoring of intradialytic biosignals is essential to minimize adverse outcomes because intradialytic hypotension and arrhythmia are associated with cardiovascular risk in hemodialysis patients. However, a continuous monitoring system for intradialytic biosignals has not yet been developed. Methods This study investigated a cloud system that hosted a prospective, open-source registry to monitor and collect intradialytic biosignals, which was named the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry. This registry was based on real-time multimodal data acquisition, such as blood pressure, heart rate, electrocardiogram, and photoplethysmogram results. Results We analyzed session information from this system for the initial 8 months, including data for some cases with hemodynamic complications such as intradialytic hypotension and arrhythmia. Conclusion This biosignal registry provides valuable data that can be applied to conduct epidemiological surveys on hemodynamic complications during hemodialysis and develop artificial intelligence models that predict biosignal changes which can improve patient outcomes.

Abstract Background Previous observational studies suggested that a reduction in estimated glomerular filtration rate (eGFR) or a supranormal eGFR value was associated with adverse cardiovascular risks. However, a previous Mendelian randomization (MR) study under the linearity assumption reported null causal effects from eGFR on myocardial infarction (MI) risks. Further investigation of the nonlinear causal effect of kidney function assessed by eGFR on the risk of MI by nonlinear MR analysis is warranted. Methods In this MR study, genetic instruments for log-eGFR based on serum creatinine were developed from European samples included in the CKDGen genome-wide association study (GWAS) meta-analysis (N=567,460). Alternate instruments for log-eGFR based on cystatin C were developed from a GWAS of European individuals that included the CKDGen and UK Biobank data (N=460,826). Nonlinear MR analysis for the risk of MI was performed using the fractional polynomial method and the piecewise linear method on data from individuals of white British ancestry in the UK Biobank (N=321,024, with 12,205 MI cases). Results Nonlinear MR analysis demonstrated a U-shaped (quadratic P value

Abstract It is well known that wearing virtual reality (VR) and augmented reality (AR) devices for long periods can cause visual fatigue and motion sickness due to vergence-accommodation conflict (VAC). VAC is considered the main obstacle to the development of advanced three-dimensional VR and AR technology. In this paper, we present a novel AR high-density super-multiview (HDSMV) display technique capable of eliminating VAC in wide range. The designed binocular time-sequential AR HDSMV projection, which delivers 11 views to each eye pupil, is experimentally demonstrated, confirming that VAC is eliminated over a wide-range of viewer’s focus distance. It is believed that the proposed time-sequential AR HDSMV method will pave the way for the development of VAC-free AR technology.

Background Long-term outcomes of live kidney donors remain controversial, although this information is crucial for selecting potential donors. Thus, this study compared the long-term risk of all-cause mortality between live kidney donors and healthy control. Methods We performed a retrospective cohort study including donors from seven tertiary hospitals in South Korea. Persons who underwent voluntary health screening were included as controls. We created a matched control group considering age, sex, era, body mass index, baseline hypertension, diabetes, estimated glomerular filtration rate, and dipstick albuminuria. The study outcome was progression to end-stage kidney disease (ESKD), and all-cause mortality as identified in the linked claims database. Results We screened 1,878 kidney donors and 78,115 health screening examinees from 2003 to 2016. After matching, 1,701 persons remained in each group. The median age of the matched study subjects was 44 years, and 46.6% were male. Among the study subjects, 2.7% and 16.6% had underlying diabetes and hypertension, respectively. There were no ESKD events in the matched donor and control groups. There were 24 (1.4%) and 12 mortality cases (0.7%) in the matched donor and control groups, respectively. In the age-sex adjusted model, the risk for all-cause mortality was significantly higher in the donor group than in the control group. However, the significance was not retained after socioeconomic status was included as a covariate (adjusted hazard ratio, 1.82; 95% confidence interval, 0.87–3.80). Conclusion All-cause mortality was similar in live kidney donors and matched non-donor healthy controls with similar health status and socioeconomic status in the Korean population.

The spatial tumor shape is determined by the complex interactions between tumor cells and their microenvironment. Here, we investigated the role of a newly identified breast cancer‐related gene, calsequestrin 2 (CASQ2), in tumor–microenvironment interactions during tumor growth and metastasis. We analyzed gene expression and three‐dimensional tumor shape data from the breast cancer dataset of The Cancer Genome Atlas (TCGA) and identified CASQ2 as a potential regulator of tumor–microenvironment interaction. In TCGA breast cancer cases containing information of three‐dimensional tumor shapes, CASQ2 mRNA showed the highest correlation with the spatial tumor shapes. Furthermore, we investigated the expression pattern of CASQ2 in human breast cancer tissues. CASQ2 was not detected in breast cancer cell lines in vitro but was induced in the xenograft tumors and human breast cancer tissues. To evaluate the role of CASQ2, we established CASQ2‐overexpressing breast cancer cell lines for in vitro and in vivo experiments. CASQ2 overexpression in breast cancer cells resulted in a more aggressive phenotype and altered epithelial–mesenchymal transition (EMT) markers in vitro. CASQ2 overexpression induced cancer‐associated fibroblast characteristics along with increased hypoxia‐inducible factor 1α (HIF1α) expression in stromal fibroblasts. CASQ2 overexpression accelerated tumorigenesis, induced collagen structure remodeling, and increased distant metastasis in vivo. CASQ2 conferred more metaplastic features to triple‐negative breast cancer cells. Our data suggest that CASQ2 is a key regulator of breast cancer tumorigenesis and metastasis by modulating diverse aspects of tumor–microenvironment interactions.

Background Considering the growing prevalence of Western lifestyles and related chronic diseases occurring in South Korea, this study aimed to explore the progression of metabolic risk factors in living kidney donors. Methods This study enrolled living kidney donors from seven hospitals from 1982 to 2016. The controls were individuals that voluntarily received health check-ups from 1995 to 2016 that were matched with donors according to age, sex, diabetes status, baseline estimated glomerular filtration rate, and date of the medical record. Data on hyperuricemia, hypertension, hypercholesterolemia, and overweight/obesity were collected to determine metabolic risks. Logistic regressions with interaction terms between the medical record date and donor status were used to compare the trends in metabolic risks over time in the two groups. Results A total of 2,018 living kidney donors and matched non-donors were included. The median age was 44.0 years and 54.0% were women. The living kidney donors showed a lower absolute prevalence for all metabolic risk factors, except for those that were overweight/obese, than the non-donors. The proportion of subjects that were overweight/obese was consistently higher over time in the donor group. The changes over time in the prevalence of each metabolic risk were not significantly different between groups, except for a lower prevalence of metabolic risk factors ≥ 3 in donors. Conclusion Over time, metabolic risks in living kidney donors are generally the same as in non-donors, except for a lower prevalence of metabolic risk factors ≥3 in donors.

Acinetobacter baumannii, a major nosocomial pathogen, survives in diverse hospital environments, and its multidrug resistance is a major concern. The ppGpp-dependent stringent response mediates the reprogramming of genes with diverse functions in several bacteria. We investigated whether ppGpp is involved in A. baumannii’s pathogenesis by examining biofilm formation, surface motility, adhesion, invasion, and mouse infection studies. Transcriptome analysis of early stationary phase cultures revealed 498 differentially-expressed genes (≥ 2-fold change) in a ppGpp-deficient A. baumannii strain; 220 and 278 genes were up and downregulated, respectively. Csu operon expression, important in pilus biosynthesis during early biofilm formation, was significantly reduced in the ppGpp-deficient strain. Our findings suggest that ppGpp signaling influences A. baumannii biofilm formation, surface motility, adherence, and virulence. We showed the association between ppGpp and pathogenicity in A. baumannii for the first time; ppGpp may be a novel antivirulence target in A. baumannii.

Background An activating mutation (c.617A>C/p.Lys206Arg, L206R) in protein kinase cAMP-activated catalytic subunit alpha (PRKACA) has been reported in 35% to 65% of cases of cortisol-producing adenomas (CPAs). We aimed to compare the clinical characteristics and transcriptome analysis between PRKACA L206R mutants and wild-type CPAs in Korea. Methods We included 57 subjects with CPAs who underwent adrenalectomy at Seoul National University Hospital. Sanger sequencing for PRKACA was conducted in 57 CPA tumor tissues. RNA sequencing was performed in 13 fresh-frozen tumor tissues. Results The prevalence of the PRKACA L206R mutation was 51% (29/57). The mean age of the study subjects was 42±12 years, and 87.7% (50/57) of the patients were female. Subjects with PRKACA L206R mutant CPAs showed smaller adenoma size (3.3±0.7 cm vs. 3.8±1.2 cm, P=0.059) and lower dehydroepiandrosterone sulfate levels (218±180 ng/mL vs. 1,511±3,307 ng/mL, P=0.001) than those with PRKACA wild-type CPAs. Transcriptome profiling identified 244 differentially expressed genes (DEGs) between PRKACA L206R mutant (n=8) and wild-type CPAs (n=5), including five upregulated and 239 downregulated genes in PRKACA L206R mutant CPAs (|fold change| ≥2, P

Abstract Background Atrial fibrillation (AF) and brain volume loss are prevalent in older individuals. We aimed to assess the causal effect of atrial fibrillation on brain volume phenotypes by Mendelian randomization (MR) analysis. Methods The genetic instrument for AF was constructed from a previous genome-wide association study (GWAS) meta-analysis (15,993 AF patients and 113,719 controls of European ancestry). The outcome summary statistics for head-size-normalized white or gray matter volume measured by magnetic resonance imaging were provided by a previous GWAS of 33,224 white British participants in the UK Biobank. Two-sample MR by the inverse variance–weighted method was performed, supported by pleiotropy-robust MR sensitivity analysis. The causal estimates for the effect of AF on ischemic stroke were also investigated in a dataset that included the findings from the MEGASTROKE study (34,217 stroke patients and 406,111 controls of European ancestry). The direct effects of AF on brain volume phenotypes adjusted for the mediating effect of ischemic stroke were studied by multivariable MR. Results A higher genetic predisposition for AF was significantly associated with lower grey matter volume [beta −0.040, standard error (SE) 0.017, P=0.017], supported by pleiotropy-robust MR sensitivity analysis. Significant causal estimates were identified for the effect of AF on ischemic stroke (beta 0.188, SE 0.026, P=1.03E−12). The total effect of AF on lower brain grey matter volume was attenuated by adjusting for the effect of ischemic stroke (direct effects, beta −0.022, SE 0.033, P=0.528), suggesting that ischemic stroke is a mediator of the identified causal pathway. The causal estimates were nonsignificant for effects on brain white matter volume as an outcome. Conclusions This study identified that genetic predisposition for AF is significantly associated with lower gray matter volume but not white matter volume. The results indicated that the identified total effect of AF on gray matter volume may be mediated by ischemic stroke.