Evaluation of Two Commercial Kits for Qualitative Detection of SARS-CoV-2 Antigens in Nasopharyngeal Specimens.

BACKGROUND: Two rapid antigen tests (RATs) for COVID-19 targeting the nucleocapsid protein of SARS-CoV-2 were compared with real-time RT-PCR as the reference method. METHODS: Ninety-six nasopharyngeal swab samples, comprising 56 positive and 40 negative samples confirmed through rRT-PCR were collected and retested to determine the reliability of the two nasopharyngeal RATs. RESULTS: The overall sensitivity and specificity of both RATs were 64.3% (95% confidence interval 50.4 - 76.6%) and 100% (95% confidence interval 91.2 - 100%), respectively. Cohen's kappa coefficient of agreement of both RATs to rRT-PCR was 0.600 (95% confidence interval 0.457 - 0.743) (p < 0.001), showing almost perfect agreement when the Ct values were less than 25 in rRT-PCR. A significant difference in Ct values between true positives and false negatives was observed (Mann-Whitney-Wilcoxon test; p < 0.001). CONCLUSIONS: Compared to rRT-PCR, RATs have fewer false negatives. In suspected COVID-19 cases, negative RAT results should be retested using either RAT or rRT-PCR.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Integrated approach for the isotope trophic position of black-tailed gull (Larus crassirostris) eggs over a decade: Combining stable isotopes of amino acids and fatty acids composition.

Recently, compound-specific isotope analysis (CSIA) using the amino acid nitrogen stable isotope ratio (δ15NAAs) has been widely used for accurate estimation of trophic position (TP). In addition, a quantitative fatty acid signature analysis (QFASA) offers insights into diet sources. In this study, we used these techniques to estimate the TP for seabirds that rely on diverse food sources across multiple ecosystems. This allows for the proper combination of factors used in TP calculation which are different for each ecosystem. The approach involved the application of a multi-mixing trophic discrimination factor (TDF) and mixing ß which is a Δδ15N between trophic and source amino acid of primary producer. Since the black-tailed gulls (BTGs) are income-breeding seabirds, which rely on energy sources obtained around their breeding sites, they and their eggs could be useful bioindicators for environmental monitoring. However, the ecological properties of BTGs such as habitats, diets, and TP are not well known due to their large migration range for wintering or breeding and their feeding habits on both aquatic and terrestrial prey. In this study, the eggs were used for estimating TP and for predicting TP of mother birds to overcome difficulties such as capturing birds and collecting non-invasive tissue samples. Eggs, sampled over a decade from three Korean islands, showed spatial differences in diet origin. Considering both the food chain and physiology of BTG, the TP of eggs was estimated to be 3.3-4.0. Notably, the TP was significantly higher at site H (3.8 ± 0.1) than at site B (3.5 ± 0.2), which indicated a higher contribution of marine diet as confirmed by QFASA. Using a reproductive shift of δ15NAAs, the TP of the mother birds was predicted to be 3.6-4.3, positioning them as the top predator in the food web. The advanced integration of multiple approaches provides valuable insights into bird ecology.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

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

Novel edible films fabricated with HG-type pectin extracted from different types of hybrid citrus peels: Effects of pectin composition on film properties.

This study investigated the valorization of novel HG-type hybrid citrus pectins derived from three cultivars: Setoka (ST), Kanpei (KP), and Shiranui (SH), and their application as packaging materials. The physicochemical properties of these pectins and their corresponding films were evaluated and compared to commercial citrus pectin. Significant variations were observed in pectin yield (18.15-24.12 %) and other physicochemical characteristics, such as degree of esterification (DE), degree of methoxylation (DM), and monosaccharide composition, among the different cultivars. All hybrid citrus pectins were classified as high-methoxy pectin types (66.67-72.89 %) with typical structural configurations like commercial citrus pectin. However, hybrid citrus pectin films exhibited superior physical properties, including higher mechanical strength, flexibility, and lower water solubility than commercial citrus pectin film, while maintaining similar transparency and moisture content. Additionally, the films displayed smooth and uniform surface morphology, confirming their excellent film-forming properties. Correlation analysis revealed that DE positively influenced mechanical properties (r = 1.0). Furthermore, the monosaccharide composition of pectins showed strong relationships (r = 0.8-1.0) with the film's mechanical and barrier properties. These findings highlight the potential of hybrid citrus pectin as potential packaging material, and the knowledge of the structure-function relationship obtained in this study could be useful for the tailored modification of citrus pectin-based packages.

New insight into biomagnification factor of mercury based on food web structure using stable isotopes of amino acids.

Although many attempts have been carried out to elaborate trophic magnification factor (TMF) and biomagnification factor (BMF), such as normalizing the concentration of pollutants and averaging diet sources, the uncertainty of the indexes still need to be improved to assess the bioaccumulation of pollutants. This study first suggests an improved BMF (i.e., BMF') applied to mercury bioaccumulation in freshwater fish from four sites before and after rainfall. The diet source and TP of each fish were identified using nitrogen stable isotope of amino acids (δ15NAAs) combined with bulk carbon stable isotope (δ13C). The BMF' was calculated normalizing with TP and diet contributions derived from MixSIAR. The BMF' values (1.3-27.2 and 1.2-27.8), which are representative of the entire food web, were generally higher than TMF (1.5-13.9 and 1.5-14.5) for both total mercury and methyl mercury, respectively. The BMF' implying actual mercury transfer pathway is more reliable index than relatively underestimated TMF for risk assessment. The ecological approach for BMF calculations provides novel insight into the behavior and trophic transfer of pollutants like mercury.

Fiber-based quantum-dot pulse oximetry for wearable health monitoring with high wavelength selectivity and photoplethysmogram sensitivity.

Increasing demand for real-time healthcare monitoring is leading to advances in thin and flexible optoelectronic device-based wearable pulse oximetry. Most previous studies have used OLEDs for this purpose, but did not consider the side effects of broad full-width half-maximum (FWHM) characteristics and single substrates. In this study, we performed SpO2 measurement using a fiber-based quantum-dot pulse oximetry (FQPO) system capable of mass production with a transferable encapsulation technique, and a narrow FWHM of about 30 nm. Based on analyses we determined that uniform angular narrow FWHM-based light sources are important for accurate SpO2 measurements through multi-layer structures and human skin tissues. The FQPO was shown to have improved photoplethysmogram (PPG) signal sensitivity with no waveguide-mode noise signal, as is typically generated when using a single substrate (30-50%). We successfully demonstrate improved SpO2 measurement accuracy as well as all-in-one clothing-type pulse oximetry with FQPO.

Meaningless gestures or pathway to healing and reconciliation? Comparing the perspectives on political apologies in victim and non-victim communities in El Salvador, the Republic of Korea and the United Kingdom.

Political apologies have been theorized to play an important role in healing and reconciliation processes in post-conflict settings. Whether they actually fulfil this function, however, remains unclear as the voices and perspectives of victim communities have largely been underrepresented in research. To address this, we examined the role of apologies that were offered for the El Mozote massacre (El Salvador), the Jeju 4.3 massacres (Republic of Korea) and Bloody Sunday (United Kingdom), according to members of these communities and the broader public. Although we anticipated that victim community members should find the apology more valuable and meaningful and should, therefore, be more positive about its role in healing and reconciliation processes, we found that this varies across countries. This variation could be explained by people's trust in the country's institutions. Across the samples, we found that the apology was seen as a relatively important gesture. For the apology to be perceived as impactful, however, it had to be seen as a meaningful (i.e. sincere) gesture. Our findings suggest that apologies have a role to play in the aftermath of human rights violations, but that it is essential to take the broader context into account.

Heterogeneous graph construction and HinSAGE learning from electronic medical records.

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

Ultrasmall polymer-coated cerium oxide nanoparticles as a traumatic brain injury therapy.

No medication has been approved for secondary injuries after traumatic brain injury (TBI). While free radicals are considered a major mediator of secondary injury, conventional antioxidants only have modest clinical efficacy. Here, we synthesized CX201 consisting of core cerium oxide nanoparticles coated with 6-aminocaproic acid and polyvinylpyrrolidone in aqueous phase. CX201 with 3.49 ± 1.11 nm of core and 6.49 ± 0.56 nm of hydrodynamic diameter showed multi-enzymatic antioxidant function. Owing to its excellent physiological stability and cell viability, CX201 had a neuroprotective effect in vitro. In a TBI animal model, an investigator-blinded randomized experiment showed a single intravenously injected CX201 significantly improved functional recovery compared to the control. CX201 reduced lipid peroxidation and inflammatory cell recruitment at the damaged brain. These suggest ultrasmall CX201 can efficiently reduce secondary brain injuries after TBI. Given the absence of current therapies, CX201 may be proposed as a novel therapeutic strategy for TBI.

Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern.

INTRODUCTION: This study aimed to evaluate a 3-dimensional (3D) U-Net-based convolutional neural networks model for the fully automatic segmentation of regional pharyngeal volume of interests (VOIs) in cone-beam computed tomography scans to compare the accuracy of the model performance across different skeletal patterns presenting with various pharyngeal dimensions. METHODS: Two-hundred sixteen cone-beam computed tomography scans of adult patients were randomly divided into training (n = 100), validation (n = 16), and test (n = 100) datasets. We trained the 3D U-Net model for fully automatic segmentation of pharyngeal VOIs and their measurements: nasopharyngeal, velopharyngeal, glossopharyngeal, and hypopharyngeal sections as well as total pharyngeal airway space (PAS). The test datasets were subdivided according to the sagittal and vertical skeletal patterns. The segmentation performance was assessed by dice similarity coefficient, volumetric similarity, precision, and recall values, compared with the ground truth created by 1 expert's manual processing using semiautomatic software. RESULTS: The proposed model achieved highly accurate performance, showing a mean dice similarity coefficient of 0.928 ± 0.023, the volumetric similarity of 0.928 ± 0.023, precision of 0.925 ± 0.030, and recall of 0.921 ± 0.029 for total PAS segmentation. The performance showed region-specific differences, revealing lower accuracy in the glossopharyngeal and hypopharyngeal sections than in the upper sections (P <0.001). However, the accuracy of model performance at each pharyngeal VOI showed no significant difference according to sagittal or vertical skeletal patterns. CONCLUSIONS: The 3D-convolutional neural network performance for region-specific PAS analysis is promising to substitute for laborious and time-consuming manual analysis in every skeletal and pharyngeal pattern.

Craniofacial treatment protocol for a pediatric patient with obstructive sleep apnea and skeletal Class III malocclusion: A 13-year follow-up.

This report aimed to describe the long-term effects of craniofacial growth modification treatment on sleep and breathing functions in a 7-year-old girl diagnosed with skeletal Class III malocclusion and sleep-disordered breathing. Based on the flowchart of orthodontic intervention protocol that we proposed for phenotype-based patient selection and skeletal target-based treatment selection for pediatric patients with sleep-disordered breathing, a 2-phase treatment targeting the nasomaxillary complex was performed. Posttreatment 3-dimensional changes in the skeletal structure and upper airway were evaluated in association with functional assessment using a validated pediatric sleep questionnaire and home sleep test. Esthetic improvement and obstructive sleep apnea cure were achieved without skeletal surgery. The 2-year retention records showed stable occlusion and improved facial profile with normal breathing and sleep.

RIDAB: Electronic medical record-integrated real world data platform for predicting and summarizing interactions in biomedical research from heterogeneous data resources.

BACKGROUND AND OBJECTIVE: With the advent of bioinformatics, biological databases have been constructed to computerize data. Biological systems can be described as interactions and relationships between elements constituting the systems, and they are organized in various biomedical open databases. These open databases have been used in approaches to predict functional interactions such as protein-protein interactions (PPI), drug-drug interactions (DDI) and disease-disease relationships (DDR). However, just combining interaction data has limited effectiveness in predicting the complex relationships occurring in a whole context. Each contributing source contains information on each element in a specific field of knowledge but there is a lack of inter-disciplinary insight in combining them. METHODS: In this study, we propose the RWD Integrated platform for Discovering Associations in Biomedical research (RIDAB) to predict interactions between biomedical entities. RIDAB is established as a graph network to construct a platform that predicts the interactions of target entities. Biomedical open database is combined with EMRs each representing a biomedical network and a real-world data. To integrate databases from different domains to build the platform, mapping of the vocabularies was required. In addition, the appropriate structure of the network and the graph embedding method to be used were needed to be selected to fit the tasks. RESULTS: The feasibility of the platform was evaluated using node similarity and link prediction for drug repositioning task, a commonly used task for biomedical network. In addition, we compared the US Food and Drug Administration (FDA)-approved repositioned drugs with the predicted result. By integrating EMR database with biomedical networks, the platform showed increased f1 score in predicting repositioned drugs, from 45.62% to 57.26%, compared to platforms based on biomedical networks alone. CONCLUSIONS: This study demonstrates that the elements of biomedical research findings can be reflected by integrating EMR data with open-source biomedical networks. In addition, showed the feasibility of using the established platform to represent the integration of biomedical networks and reflected the relationship between real world networks.

Association between social support and postpartum depression.

Postpartum depression is common; however, little is known about its relationship to social support and postpartum depression. This study examined the association between them among South Korean women within one year of childbirth. This study was based on the 2016 Korean Study of Women's Health-Related Issues (K-Stori), a cross-sectional survey employing nationally-representative random sampling. Participants were 1,654 postpartum women within a year of giving birth. Chi-square test and logistic regression analysis were conducted to analyze the associations between social support (and other covariates) and postpartum depression. Among participants, 266 (16.1%) had postpartum depression. Depending on the level of social support, 6.0%, 53.9%, and 40.1% of them had low, moderate, and high social support, respectively. Women with moderate or low social support were more likely to have postpartum depression (OR = 1.78, 95% CI = 1.26-2.53; OR = 2.76, 95% CI = 1.56-4.89). This trend was observed in participants with multiparity, pregnancy loss, obese body image, and employed women. Social support was associated with a decreased likelihood of postpartum depression, indicating the importance of social support, especially for women experiencing multiparity, pregnancy loss, negative body image, as well as for employed women.