Upcycling green carbon black as a reinforcing agent for styrene-butadiene rubber materials.

This study reports the effects of recovered carbon black (produced in a clean and sustainable way) as a reinforcing agent on the physicochemical properties of a styrene-butadiene rubber (SBR) matrix. SBR-based composite materials are prepared with recovered green carbon black (GCB), and these are thoroughly compared to the composite materials containing conventional virgin carbon black (VCB) (produced by the incomplete combustion of petroleum products). The GCB-SBR composite materials generally show detectably inferior properties compared to the VCB-SBR composite under the same preparation conditions due to the limited functionality of the GCB filler. However, the introduction of a small amount of crosslinker, acrylate-functionalized POSS (polyhedral oligomeric silsesquioxane), into the GCB-SBR composite materials effectively enhances the overall physical properties, including the tensile strength, fracture elongation, and thermal stability. The degree of the crosslinking efficiency, thermal stability, and mechanical properties of the composite materials are optimized and thoroughly examined to demonstrate the possibility of replacing typical VCB with GCB, which can allow for upcycling the inexpensive and ecofriendly carbon black materials as effective reinforcing fillers.

Three-dimensional network of nitrogen-doped carbon matrix-encapsulated Si nanoparticles/carbon nanofibers hybrids for lithium-ion battery anodes with excellent capability.

Three-dimensionally structured silicon (Si)-carbon (C) nanocomposites have great potential as anodes in lithium-ion batteries (LIBs). Here, we report a Nitrogen-doped graphene/carbon-encapsulated Si nanoparticle/carbon nanofiber composite (NG/C@Si/CNF) prepared by methods of surface modification, electrostatic self-assembly, cross-linking with heat treatment, and further carbonization as a potential high-performance anode for LIBs. The N-doped C matrix wrapped around Si nanoparticles improved the electrical conductivity of the composites and buffered the volume change of Si nanoparticles during lithiation/delithiation. Uniformly dispersed CNF in composites acted as conductive networks for the fast transport of ions and electrons. The entire tightly connected organic material of NG/C@Si and CNF prevented the crushing and shedding of particles and maintained the integrity of the electrode structure. The NG/C@Si/CNF composite exhibited better rate capability and cycling performance compared with the other electrode materials. After 100 cycles, the electrode maintained a high reversible specific capacity of 1371.4 mAh/g.

Enhancing the Alkaline Hydrogen Evolution Reaction of Graphene Quantum Dots by Ethylenediamine Functionalization.

Developing metal-free photocatalyst for water splitting is one of the important rising research topics. Although graphene quantum dots (GQDs) have already been investigated as a water splitting photocatalyst several times, studies on modification and design are still needed for an efficient hydrogen evolution reaction (HER) rate particularly in alkaline solutions with an aim to realize overall water splitting. We have synthesized covalently functionalized GQDs with ethylenediamine (EDA) by an amide coupling reaction. It was found that EDA-functionalized GQDs generally exhibited much higher HER activity than bare GQDs. Importantly, the HER activity of EDA-functionalized GQDs increased in proportion to the pH and peaked at pH = 10, which is in stark contrast to the simple decreasing HER rate with the pH of bare GQDs. Through linear sweep voltammetry measurement and electrochemical impedance spectroscopy analysis, it was verified that covalently bonded EDA acts as water dissociation sites to enhance the photocatalytic HER in alkaline medium.

Development and validation of a deep-learning-based pediatric early warning system: A single-center study.

BACKGROUND: Early detection and prompt intervention for clinically deteriorating events are needed to improve clinical outcomes. There have been several attempts at this, including the introduction of rapid response teams (RRTs) with early warning scores. We developed a deep-learning-based pediatric early warning system (pDEWS) and validated its performance. METHODS: This single-center retrospective observational cohort study reviewed, 50,019 pediatric patients admitted to the general ward in a tertiary-care academic children's hospital from January 2012 to December 2018. They were split by admission date into a derivation and a validation cohort. We developed a pDEWS for the early prediction of cardiopulmonary arrest and unexpected ward-to-pediatric intensive care unit (PICU) transfer. Then, we validated this system by comparing modified pediatric early warning score (PEWS), random forest (RF); an ensemble model of multiple decision trees and logistic regression (LR); a statistical model that uses a logistic function. RESULTS: For predicting cardiopulmonary arrest, the pDEWS (area under the receiver operating characteristic curve (AUROC), 0.923) outperformed modified PEWS (AUROC, 0.769) and reduced the mean alarm count per day (MACPD) and number needed to examine (NNE) by 82.0% (from 46.7 to 8.4 MACPD) and 89.5% (from 0.303 to 0.807), respectively. Furthermore, for predicting unexpected ward-to-PICU transfer pDEWS also showed superior performance compared to existing methods. CONCLUSION: Our study showed that pDEWS was superior to the modified PEWS and prediction models using RF and LR. This study demonstrates that the integration of the pDEWS into RRTs could increase operational efficiency and improve clinical outcomes.

Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement.

OBJECTIVES: To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization. METHODS: Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics. RESULTS: The five readers reported 139-151 negative screening results without CAD and 126-142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (p < 0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004). CONCLUSIONS: Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases. KEY POINTS: • Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65. • The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%). • In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).

Synthesis and Electrochemical Performance of Electrostatic Self-Assembled Nano-Silicon@N-Doped Reduced Graphene Oxide/Carbon Nanofibers Composite as Anode Material for Lithium-Ion Batteries.

Silicon-carbon nanocomposite materials are widely adopted in the anode of lithium-ion batteries (LIB). However, the lithium ion (Li+) transportation is hampered due to the significant accumulation of silicon nanoparticles (Si) and the change in their volume, which leads to decreased battery performance. In an attempt to optimize the electrode structure, we report on a self-assembly synthesis of silicon nanoparticles@nitrogen-doped reduced graphene oxide/carbon nanofiber (Si@N-doped rGO/CNF) composites as potential high-performance anodes for LIB through electrostatic attraction. A large number of vacancies or defects on the graphite plane are generated by N atoms, thus providing transmission channels for Li+ and improving the conductivity of the electrode. CNF can maintain the stability of the electrode structure and prevent Si from falling off the electrode. The three-dimensional composite structure of Si, N-doped rGO, and CNF can effectively buffer the volume changes of Si, form a stable solid electrolyte interface (SEI), and shorten the transmission distance of Li+ and the electrons, while also providing high conductivity and mechanical stability to the electrode. The Si@N-doped rGO/CNF electrode outperforms the Si@N-doped rGO and Si/rGO/CNF electrodes in cycle performance and rate capability, with a reversible specific capacity reaching 1276.8 mAh/g after 100 cycles and a Coulomb efficiency of 99%.

A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards.

BACKGROUND: The recently developed deep learning (DL)-based early warning score (DEWS) has shown potential in predicting deteriorating patients. We aimed to validate DEWS in multiple centres and compare the prediction, alarming and timeliness performance with the modified early warning score (MEWS) to identify patients at risk for in-hospital cardiac arrest (IHCA). METHOD/RESEARCH DESIGN: This retrospective cohort study included adult patients admitted to the general wards of five hospitals during a 12-month period. The occurrence of IHCA within 24 h of vital sign observation was the outcome of interest. We assessed the discrimination using the area under the receiver operating characteristic curve (AUROC). RESULTS: The study population consists of 173,368 patients (224 IHCAs). The predictive performance of DEWS was superior to that of MEWS in both the internal (AUROC: 0.860 vs. 0.754, respectively) and external (AUROC: 0.905 vs. 0.785, respectively) validation cohorts. At the same specificity, DEWS had a higher sensitivity than MEWS, and at the same sensitivity, DEWS reduced the mean alarm count by nearly half of MEWS. Additionally, DEWS was able to predict more IHCA patients in the 24-0.5 h before the outcome, and DEWS was reasonably calibrated. CONCLUSION: Our study showed that DEWS was superior to MEWS in three key aspects (IHCA predictive, alarming, and timeliness performance). This study demonstrates the potential of DEWS as an effective, efficient screening tool in rapid response systems (RRSs) to identify high-risk patients.

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis.

There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.

Artificial Intelligence Algorithm for Screening Heart Failure with Reduced Ejection Fraction Using Electrocardiography.

Although heart failure with reduced ejection fraction (HFrEF) is a common clinical syndrome and can be modified by the administration of appropriate medical therapy, there is no adequate tool available to perform reliable, economical, early-stage screening. To meet this need, we developed an interpretable artificial intelligence (AI) algorithm for HFrEF screening using electrocardiography (ECG) and validated its performance. This retrospective cohort study included two hospitals. An AI algorithm based on a convolutional neural network was developed using 39,371 ECG results from 17,127 patients. The internal validation included 3,470 ECGs from 2,908 patients. Furthermore, we conducted external validation using 4,362 ECGs from 4,176 patients from another hospital to verify the applicability of the algorithm across different centers. The end-point was to detect HFrEF, defined as an ejection fraction <40%. We also visualized the regions in 12 lead ECG that affected HFrEF detection in the AI algorithm and compared this to the previously documented literature. During the internal and external validation, the areas under the curves of the AI algorithm using a 12 lead ECG for detecting HFrEF were 0.913 (95% confidence interval, 0.902-0.925) and 0.961 (0.951-0.971), respectively, and the areas under the curves of the AI algorithm using a single-lead ECG were 0.874 (0.859-0.890) and 0.929 (0.911-0.946), respectively. The deep learning-based AI algorithm performed HFrEF detection well using not only a 12 lead but also a single-lead ECG. These results suggest that HFrEF can be screened not only using a 12 lead ECG, as is typical of a conventional ECG machine, but also with a single-lead ECG performed by a wearable device employing the AI algorithm, thereby preventing irreversible disease progression and mortality.

Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.

OBJECTIVES: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size. METHODS: Four hundred eleven subsolid nodules (SSNs) (120 preinvasive lesions or MIAs and 291 IPAs) in 333 patients who underwent surgery between June 2010 and August 2016 were retrospectively included to develop the model (370 SSNs in 293 patients for training and 41 SSNs in 40 patients for tuning). Ninety SSNs of 2 cm or smaller (45 preinvasive lesions or MIAs and 45 IPAs) resected in 2018 formed a validation set. Six radiologists measured the solid portion of each nodule. Performances of the model and radiologists were assessed using receiver operating characteristics curve analysis. RESULTS: The deep learning model differentiated IPA from preinvasive lesions or MIA with areas under the curve (AUCs) of 0.914, 0.956, and 0.833 for the training, tuning, and validation sets, respectively. The mean AUC of the radiologists was 0.835 in the validation set, without significant differences between radiologists and the model (p = 0.97). The sensitivity, specificity, and accuracy of the model were 71% (32/45), 87% (39/45), and 79% (71/90), respectively, whereas the corresponding values of the radiologists were 75.2% (203/270), 76.7% (207/270), and 75.9% (410/540) with a 5-mm threshold for the solid portion size. CONCLUSIONS: The performance of the model for differentiating IPA from preinvasive lesions or MIA was comparable to that of the radiologists' measurements of solid portion size. KEY POINTS: • A deep learning-based model differentiated IPA from preinvasive lesions or MIA with AUCs of 0.914 and 0.956 for the training and tuning sets, respectively. • In the validation set including subsolid nodules of 2 cm or smaller, the model showed an AUC of 0.833, being on par with the performance of the solid portion size measurements made by the radiologists (AUC, 0.835; p = 0.97). • SSNs with a solid portion measuring > 10 mm on CT showed a high probability of being IPA (positive predictive value, 93.5-100.0%).

Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction.

Background Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to investigate whether deep learning-based super-resolution algorithms for reducing CT section thickness can improve performance. Materials and Methods CT images obtained with 1-, 3-, and 5-mm-thick sections were obtained in patients who underwent surgery between March 2018 and December 2018. Patients with resected synchronous SSNs and those without SSNs (negative controls) were retrospectively evaluated. The SSNs, which ranged from 6 to 30 mm, were labeled ground-truth lesions. A deep learning-based CAD system was applied to SSN detection on CT images of each section thickness and those converted from 3- and 5-mm section thickness into 1-mm section thickness by using the super-resolution algorithm. The CAD performance on each section thickness was evaluated and compared by using the jackknife alternative free response receiver operating characteristic figure of merit. Results A total of 308 patients (mean age ± standard deviation, 62 years ± 10; 183 women) with 424 SSNs (310 part-solid and 114 nonsolid nodules) and 182 patients without SSNs (mean age, 65 years ± 10; 97 men) were evaluated. The figures of merit differed across the three section thicknesses (0.92, 0.90, and 0.89 for 1, 3, and 5 mm, respectively; P = .04) and between 1- and 5-mm sections (P = .04). The figures of merit varied for nonsolid nodules (0.78, 0.72, and 0.66 for 1, 3, and 5 mm, respectively; P < .001) but not for part-solid nodules (range, 0.93-0.94; P = .76). The super-resolution algorithm improved CAD sensitivity on 3- and 5-mm-thick sections (P = .02 for 3 mm, P < .001 for 5 mm). Conclusion Computer-aided detection (CAD) of subsolid nodules performed better at 1-mm section thickness CT than at 3- and 5-mm section thickness CT, particularly with nonsolid nodules. Application of a super-resolution algorithm improved the sensitivity of CAD at 3- and 5-mm section thickness CT. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Goo in this issue.

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies.

PURPOSE: Gastric cancer remains the leading cause of cancer-related deaths in Northeast Asia. Population-based endoscopic screenings in the region have yielded successful results in early detection of gastric tumors. Endoscopic screening rates are continuously increasing, and there is a need for an automatic computerized diagnostic system to reduce the diagnostic burden. In this study, we developed an algorithm to classify gastric epithelial tumors automatically and assessed its performance in a large series of gastric biopsies and its benefits as an assistance tool. EXPERIMENTAL DESIGN: Using 2,434 whole-slide images, we developed an algorithm based on convolutional neural networks to classify a gastric biopsy image into one of three categories: negative for dysplasia (NFD), tubular adenoma, or carcinoma. The performance of the algorithm was evaluated by using 7,440 biopsy specimens collected prospectively. The impact of algorithm-assisted diagnosis was assessed by six pathologists using 150 gastric biopsy cases. RESULTS: Diagnostic performance evaluated by the AUROC curve in the prospective study was 0.9790 for two-tier classification: negative (NFD) versus positive (all cases except NFD). When limited to epithelial tumors, the sensitivity and specificity were 1.000 and 0.9749. Algorithm-assisted digital image viewer (DV) resulted in 47% reduction in review time per image compared with DV only and 58% decrease to microscopy. CONCLUSIONS: Our algorithm has demonstrated high accuracy in classifying epithelial tumors and its benefits as an assistance tool, which can serve as a potential screening aid system in diagnosing gastric biopsy specimens.

Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record.

OBJECTIVES: A deep learning-based early warning system is proposed to predict sepsis prior to its onset. DESIGN: A novel algorithm was devised to detect sepsis 6 hours prior to its onset based on electronic medical records. SETTING: Retrospective cohorts from three separate hospitals are used in this study. Sepsis onset was defined based on Sepsis-3. Algorithms are evaluated based on the score function used in the Physionet Challenge 2019. PATIENTS: Over 60,000 ICU patients with 40 clinical variables (vital signs, laboratory results) for each hour of a patient's ICU stay were used. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The proposed algorithm predicted the onset of sepsis in the preceding n hours (where n = 4, 6, 8, or 12). Furthermore, the proposed method compared how many sepsis patients can be predicted in a short time with other methods. To interpret a given result in a clinical perspective, the relationship between input variables and the probability of the proposed method were presented. The proposed method achieved superior results (area under the receiver operating characteristic curve, area under the precision-recall curve, and score) and predicted more sepsis patients in advance. In official phase, the proposed method showed the utility score of -0.101, area under the receiver operating characteristic curve 0.782, area under the precision-recall curve 0.041, accuracy 0.786, and F-measure 0.046. CONCLUSIONS: Using Physionet Challenge 2019, the proposed method can accurately and early predict the onset of sepsis. The proposed method can be a practical early warning system in the environment of real hospitals.

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services.

BACKGROUND: In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. METHODS: We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. RESULTS: The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]). CONCLUSIONS: The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation. DESIGN: Retrospective cohort study. SETTING: This study was conducted at a hospital in which deep learning-based early warning system was implemented. PATIENTS: We reviewed the records of adult patients who were admitted to the general ward of our hospital from April 2018 to March 2019. INTERVENTIONS: The study population included 8,039 adult patients. A total 83 events of deterioration occurred during the study period. The outcome was events of deterioration, defined as cardiac arrest and unexpected ICU admission. We defined a true alarm as an alarm occurring within 0.5-24 hours before a deteriorating event. MEASUREMENTS AND MAIN RESULTS: We used the area under the receiver operating characteristic curve, area under the precision-recall curve, number needed to examine, and mean alarm count per day as comparative measures. The deep learning-based early warning system (area under the receiver operating characteristic curve, 0.865; area under the precision-recall curve, 0.066) outperformed the modified early warning score (area under the receiver operating characteristic curve, 0.682; area under the precision-recall curve, 0.010) and reduced the number needed to examine and mean alarm count per day by 69.2% and 59.6%, respectively. At the same specificity, deep learning-based early warning system had up to 257% higher sensitivity than conventional methods. CONCLUSIONS: The developed artificial intelligence based on deep-learning, deep learning-based early warning system, accurately predicted deterioration of patients in a general ward and outperformed conventional methods. This study showed the potential and effectiveness of artificial intelligence in an rapid response system, which can be applied together with electronic health records. This will be a useful method to identify patients with deterioration and help with precise decision-making in daily practice.

Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer.

OBJECTIVE: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve the reproducibility of RFs obtained from images with different slice thicknesses. MATERIALS AND METHODS: CT images with 1-, 3-, and 5-mm slice thicknesses obtained from 100 pathologically proven lung cancers between July 2017 and December 2017 were evaluated. CNN-based SR algorithms using residual learning were developed to convert thick-slice images into 1-mm slices. Lung cancers were semi-automatically segmented and a total of 702 RFs (tumor intensity, texture, and wavelet features) were extracted from 1-, 3-, and 5-mm slices, as well as the 1-mm slices generated from the 3- and 5-mm images. The stabilities of the RFs were evaluated using concordance correlation coefficients (CCCs). RESULTS: The mean CCCs for the comparisons of original 1 mm vs. 3 mm, 1 mm vs. 5 mm, and 3 mm vs. 5 mm images were 0.41, 0.27, and 0.65, respectively (p < 0.001 for all comparisons). Tumor intensity features showed the best reproducibility while wavelets showed the lowest reproducibility. The majority of RFs failed to achieve reproducibility (CCC ≥ 0.85; 3.6%, 1.0%, and 21.5%, respectively). After applying the CNN-based SR algorithms, the reproducibility significantly improved in all three pairings (mean CCCs: 0.58, 0.45, and 0.72; p < 0.001 for all comparisons). The reproducible RFs also increased (36.3%, 17.4%, and 36.9%, respectively). CONCLUSION: The reproducibility of RFs in lung cancer is significantly influenced by CT slice thickness, which can be improved by the CNN-based SR algorithms.

Are there effects of lunar cycle on pediatric febrile seizure?: A single-center retrospective study (2005-2018).

BACKGROUND: Several studies have reported an association between seizure and the lunar cycle; however, results are conflicting. Thus, we investigated whether emergency department (ED) visits due to febrile seizure (FS) or FS plus were affected by lunar cycle. METHODS: We reviewed the medical records of patients who were admitted to the ED with a main diagnosis of FS or FS plus from January 1, 2005 to August 31, 2018 (13 years 8 months), a period of 4991 days with 169 lunar cycles. During that period, we collected weather data such as mean temperature, average atmospheric pressure (AP), and humidity according to lunar phase (new moon, first quarter, full moon, and third or last quarter). RESULTS: A total of 1979 patients were identified. We found male predominant with a mean age of 2.62 ±â€¯2.09 years. Acute pharyngotonsillitis was the most common cause of fever, generalized tonic-clonic seizure was the most common type of seizure, and the mean peak body temperature was 38.77 ±â€¯0.81 °C. The lunar cycle did not affect the onset or frequency of FS after adjustment; however, several factors, including season, O3 and NO2 concentrations, and holidays, were associated with FS. CONCLUSION: We did not find an association between lunar cycle and FS or FS plus. However, several factors, including season, O3, NO2, and holidays were associated with FS or FS plus.

Validation of deep-learning-based triage and acuity score using a large national dataset.

AIM: Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Score (DTAS) identifies high-risk patients more accurately than existing triage and acuity scores using a large national dataset. METHODS: We conducted a retrospective observational cohort study using data from the Korean National Emergency Department Information System (NEDIS), which collected data on visits in real time from 151 EDs. The NEDIS data was split into derivation data (January 2014-June 2016) and validation data (July-December 2016). We also used data from the Sejong General Hospital (SGH) for external validation (January-December 2017). We predicted in-hospital mortality, critical care, and hospitalization using initial information of ED patients (age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs and mental status as predictor variables). RESULTS: A total of 11,656,559 patients were included in this study. The primary outcome was in-hospital mortality. The Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC) of DTAS were 0.935 and 0.264. It significantly outperformed Korean triage and acuity score (AUROC:0.785, AUPRC:0.192), modified early warning score (AUROC:0.810, AUPRC:0.116), logistic regression (AUROC:0.903, AUPRC:0.209), and random forest (AUROC:0.910, AUPRC:0.179). CONCLUSION: Deep-learning-based Triage and Acuity Score predicted in-hospital mortality, critical care, and hospitalization more accurately than existing triages and acuity, and it was validated using a large, multicenter dataset.

Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning.

With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare system is currently fraught with many challenges. Rule-based methods (e.g., Modified Early Warning Score) and machine learning (e.g., random forest) were proposed as a solution but not effective. In this article, we introduce the DeepEWS (Deep learning based Early Warning Score), which is based on a novel deep learning algorithm. Relative to the standard of care and current solutions in the marketplace, there is high accuracy, and in the clinical setting even when we consider the number of alarms, the accuracy levels are superior.

Severely modified lipoprotein properties without a change in cholesteryl ester transfer protein activity in patients with acute renal failure secondary to Hantaan virus infection.

Patients with hemorrhagic fever with renal syndrome (HFRS) often exhibit altered serum lipid and lipoprotein profile during the oliguric phase of the disease. Serum lipid and lipoprotein profiles were assessed during the oliguric and recovery phases in six male patients with HFRS. In the oliguric phase of HFRS, the apolipoprotein (apo) C-III content in high-density lipoproteins (HDL) was elevated, whereas the apoA-I content was lowered. The level of expression and activity of antioxidant enzymes were severely reduced during the oliguric phase, while the cholesteryl ester transfer protein activity and protein level were unchanged between the phases. In the oliguric phase, electromobility of HDL2 and HDL3 was faster than in the recovery phase. Low-density lipoprotein (LDL) particle size was smaller and the distribution was less homogeneous. Patients with HFRS in the oliguric phase had severely modified lipoproteins in composition and metabolism.