Apurinic/apyrimidinic endonuclease 1 (APE1) is an enzyme responsible for the initial step in the base excision repair pathway and is known to be a potential drug target for treating cancers, because its expression is associated with resistance to DNA-damaging anticancer agents. Although several inhibitors already have been identified, the identification of novel kinds of potential inhibitors of APE1 could provide a seed for the development of improved anticancer drugs. For this purpose, we first classified known inhibitors of APE1. According to the classification, we constructed two distinct pharmacophore models. We screened more than 3 million lead-like compounds using the pharmacophores. Hits that fulfilled the features of the pharmacophore models were identified. In addition to the pharmacophore screen, we carried out molecular docking to prioritize hits. Based on these processes, we ultimately identified 1,338 potential inhibitors of APE1 with predicted binding affinities to the enzyme.
Antineoplastic Agents ; Classification ; DNA Repair ; Molecular Docking Simulation

More effective production of human insulin is important, because insulin is the main medication that is used to treat multiple types of diabetes and because many people are suffering from diabetes. The current system of insulin production is based on recombinant DNA technology, and the expression vector is composed of a preproinsulin sequence that is a fused form of an artificial leader peptide and the native proinsulin. It has been reported that the sequence of the leader peptide affects the production of insulin. To analyze how the leader peptide affects the maturation of insulin structurally, we adapted several in silico simulations using 13 artificial proinsulin sequences. Three-dimensional structures of models were predicted and compared. Although their sequences had few differences, the predicted structures were somewhat different. The structures were refined by molecular dynamics simulation, and the energy of each model was estimated. Then, protein-protein docking between the models and trypsin was carried out to compare how efficiently the protease could access the cleavage sites of the proinsulin models. The results showed some concordance with experimental results that have been reported; so, we expect our analysis will be used to predict the optimized sequence of artificial proinsulin for more effective production.
Computer Simulation ; DNA, Recombinant ; Humans* ; Insulin ; Molecular Dynamics Simulation* ; Proinsulin ; Protein Sorting Signals ; Trypsin

BACKGROUND: Injury severity scoring systems that quantify and predict trauma outcomes have not been established in Korea. This study was designed to determine the best system for use in the Korean trauma population. METHODS: We collected and analyzed the data from trauma patients admitted to our institution from January 2010 to December 2014. Injury Severity Score (ISS), Revised Trauma Score (RTS), and Trauma and Injury Severity Score (TRISS) were calculated based on the data from the enrolled patients. Area under the receiver operating characteristic (ROC) curve (AUC) for the prediction ability of each scoring system was obtained, and a pairwise comparison of ROC curves was performed. Additionally, the cut-off values were estimated to predict mortality, and the corresponding accuracy, positive predictive value, and negative predictive value were obtained. RESULTS: A total of 7,120 trauma patients (6,668 blunt and 452 penetrating injuries) were enrolled in this study. The AUCs of ISS, RTS, and TRISS were 0.866, 0.894, and 0.942, respectively, and the prediction ability of the TRISS was significantly better than the others (p < 0.001, respectively). The cut-off value of the TRISS was 0.9082, with a sensitivity of 81.9% and specificity of 92.0%; mortality was predicted with an accuracy of 91.2%; its positive predictive value was the highest at 46.8%. CONCLUSIONS: The results of our study were based on the data from one institution and suggest that the TRISS is the best prediction model of trauma outcomes in the current Korean population. Further study is needed with more data from multiple centers in Korea.
Area Under Curve ; Humans ; Injury Severity Score ; Korea ; Mortality ; ROC Curve ; Sensitivity and Specificity ; Trauma Centers

BACKGROUND: Injury severity scoring systems that quantify and predict trauma outcomes have not been established in Korea. This study was designed to determine the best system for use in the Korean trauma population. METHODS: We collected and analyzed the data from trauma patients admitted to our institution from January 2010 to December 2014. Injury Severity Score (ISS), Revised Trauma Score (RTS), and Trauma and Injury Severity Score (TRISS) were calculated based on the data from the enrolled patients. Area under the receiver operating characteristic (ROC) curve (AUC) for the prediction ability of each scoring system was obtained, and a pairwise comparison of ROC curves was performed. Additionally, the cut-off values were estimated to predict mortality, and the corresponding accuracy, positive predictive value, and negative predictive value were obtained. RESULTS: A total of 7,120 trauma patients (6,668 blunt and 452 penetrating injuries) were enrolled in this study. The AUCs of ISS, RTS, and TRISS were 0.866, 0.894, and 0.942, respectively, and the prediction ability of the TRISS was significantly better than the others (p < 0.001, respectively). The cut-off value of the TRISS was 0.9082, with a sensitivity of 81.9% and specificity of 92.0%; mortality was predicted with an accuracy of 91.2%; its positive predictive value was the highest at 46.8%. CONCLUSIONS: The results of our study were based on the data from one institution and suggest that the TRISS is the best prediction model of trauma outcomes in the current Korean population. Further study is needed with more data from multiple centers in Korea.
Area Under Curve ; Humans ; Injury Severity Score ; Korea ; Mortality ; ROC Curve ; Sensitivity and Specificity ; Trauma Centers

Background: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. Methods: We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. Results: A total of 1,207 patients were included in the study. Among them, 631, 139, and 153were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI],0.9352–0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612– 0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860–1.0000); sensitivity, 0.9594 (95% CI, 0.9245–0.9943); specificity, 0.9714 (95% CI, 0.9162–1.0000); PPV, 0.9916 (95% CI, 0.9752–1.0000); NPV, 0.8718 (95% CI, 0.7669–0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825–0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845–0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087–0.9867); sensitivity, 0.9595 (95% CI, 0.9145–1.0000); specificity, 0.6500 (95% CI, 0.5022–0.7978); PPV, 0.8353 (95% CI, 0.7564–0.9142); NPV, 0.8966 (95% CI, 0.7857–1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. Conclusion We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

Background: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. Methods: We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. Results: A total of 1,207 patients were included in the study. Among them, 631, 139, and 153were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI],0.9352–0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612– 0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860–1.0000); sensitivity, 0.9594 (95% CI, 0.9245–0.9943); specificity, 0.9714 (95% CI, 0.9162–1.0000); PPV, 0.9916 (95% CI, 0.9752–1.0000); NPV, 0.8718 (95% CI, 0.7669–0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825–0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845–0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087–0.9867); sensitivity, 0.9595 (95% CI, 0.9145–1.0000); specificity, 0.6500 (95% CI, 0.5022–0.7978); PPV, 0.8353 (95% CI, 0.7564–0.9142); NPV, 0.8966 (95% CI, 0.7857–1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. Conclusion We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

Objective: We investigated the effects of a quick Sequential Organ Failure Assessment (qSOFA)–negative result (qSOFA score <2 points) at triage on the compliance with sepsis bundles among patients with sepsis who presented to the emergency department (ED). Methods: Prospective sepsis registry data from 11 urban tertiary hospital EDs between October 2015 and April 2018 were retrospectively reviewed. Patients who met the Third International Consensus Definitions for Sepsis and Septic Shock criteria were included. Primary exposure was defined as a qSOFA score ≥2 points at ED triage. The primary outcome was defined as 3-hour bundle compliance, including lactate measurement, blood culture, broad-spectrum antibiotics administration, and 30 mL/kg crystalloid administration. Multivariate logistic regression analysis to predict 3-hour bundle compliance was performed. Results: Among the 2,250 patients enrolled in the registry, 2,087 fulfilled the sepsis criteria. Only 31.4% (656/2,087) of the sepsis patients had qSOFA scores ≥2 points at triage. Patients with qSOFA scores <2 points had lower lactate levels, lower SOFA scores, and a lower 28-day mortality rate. Rates of compliance with lactate measurement (adjusted odds ratio [aOR], 0.47; 95% confidence interval [CI], 0.29–0.75), antibiotics administration (aOR, 0.64; 95% CI, 0.52–0.78), and 30 mL/kg crystalloid administration (aOR, 0.62; 95% CI, 0.49–0.77) within 3 hours from triage were significantly lower in patients with qSOFA scores <2 points. However, the rate of compliance with blood culture within 3 hours from triage (aOR, 1.66; 95% CI, 1.33–2.08) was higher in patients with qSOFA scores <2 points. Conclusion A qSOFA-negative result at ED triage is associated with low compliance with lactate measurement, broad-spectrum antibiotics administration, and 30 mL/kg crystalloid administration within 3 hours in sepsis patients.

Background: The preventable trauma death rate survey is a basic tool for the quality management of trauma treatment because it is a method that can intuitively evaluate the level of national trauma treatment. We conducted this study as a national biennial follow-up survey project and report the results of the review of the 2019 trauma death data in Korea. Methods: From January 1, 2019 to December 31, 2019, of a total of 8,482 trauma deaths throughout the country, 1,692 were sampled from 279 emergency medical institutions in Korea. All cases were evaluated for preventability of death and opportunities for improvement using a multidisciplinary panel review approach. Results: The preventable trauma death rate was estimated to be 15.7%. Of these, 3.1% were judged definitive preventable deaths, and 12.7% were potentially preventable deaths. The odds ratio for preventable traumatic death was 2.56 times higher in transferred patients compared to that of patients who visited the final hospital directly. The group that died 1 hour after the accident had a statistically significantly higher probability of preventable death than that of the group that died within 1 hour after the accident. Conclusion The preventable trauma death rate for trauma deaths in 2019 was 15.7%, which was 4.2%p lower than that in 2017. To improve the quality of trauma treatment, the transfer of severe trauma patients to trauma centers should be more focused.

OBJECTIVE: Chest pain is one of the most common complaints in the emergency department (ED). Cardiac computed tomography angiography (CCTA) is a frequently used tool for the early triage of patients with low- to intermediate-risk acute chest pain. We present a study protocol for a multicenter prospective randomized controlled clinical trial testing the hypothesis that a low-dose CCTA protocol using prospective electrocardiogram (ECG)-triggering and limited-scan range can provide sufficient diagnostic safety for early triage of patients with acute chest pain. METHODS: The trial will include 681 younger adult (aged 20 to 55) patients visiting EDs of three academic hospitals for acute chest pain or equivalent symptoms who require further evaluation to rule out acute coronary syndrome. Participants will be randomly allocated to either low-dose or conventional CCTA protocol at a 2:1 ratio. The low-dose group will undergo CCTA with prospective ECG-triggering and restricted scan range from sub-carina to heart base. The conventional protocol group will undergo CCTA with retrospective ECG-gating covering the entire chest. Patient disposition is determined based on computed tomography findings and clinical progression and all patients are followed for a month. The primary objective is to prove that the chance of experiencing any hard event within 30 days after a negative low-dose CCTA is less than 1%. The secondary objectives are comparisons of the amount of radiation exposure, ED length of stay and overall cost. RESULTS AND CONCLUSION: Our low-dose protocol is readily applicable to current multi-detector computed tomography devices. If this study proves its safety and efficacy, dose-reduction without purchasing of expensive newer devices would be possible.
Acute Coronary Syndrome ; Adult ; Angiography* ; Chest Pain ; Coronary Angiography ; Electrocardiography ; Emergencies* ; Emergency Service, Hospital ; Heart ; Humans ; Length of Stay ; Prospective Studies ; Radiation Exposure ; Retrospective Studies ; Thorax ; Triage

Objective: High-quality intensive care, including targeted temperature management (TTM) for patients with postcardiac arrest syndrome, is a key element for improving outcomes after out-of-hospital cardiac arrest (OHCA). We aimed to assess the status of postcardiac arrest syndrome care, including TTM and 6-month survival with neurologically favorable outcomes, after adult OHCA patients were treated with TTM, using data from the Korean Hypothermia Network prospective registry. Methods: We used the Korean Hypothermia Network prospective registry, a web-based multicenter registry that includes data from 22 participating hospitals throughout the Republic of Korea. Adult comatose OHCA survivors treated with TTM between October 2015 and December 2018 were included. The primary outcome was neurological outcome at 6 months. Results: Of the 1,354 registered OHCA survivors treated with TTM, 550 (40.6%) survived 6 months, and 413 (30.5%) had good neurological outcomes. We identified 839 (62.0%) patients with preClinsumed cardiac etiology. A total of 937 (69.2%) collapses were witnessed, shockable rhythms were demonstrated in 482 (35.6%) patients, and 421 (31.1%) patients arrived at the emergency department with prehospital return of spontaneous circulation. The most common target temperature was 33°C, and the most common target duration was 24 hours. Conclusion The survival and good neurologic outcome rates of this prospective registry show great improvements compared with those of an earlier registry. While the optimal target temperature and duration are still unknown, the most common target temperature was 33°C, and the most common target duration was 24 hours.