Background: Owing to the differential propensity for bleeding and ischemic events with response to antiplatelet therapy, the safety and effectiveness of potent P2Y12 inhibitor ticagrelor in East Asian populations remain uncertain. Methods: In this multicenter trial, 800 Korean patients hospitalized for acute coronary syndromes with or without ST elevation and intended for invasive management were randomly assigned to receive, in a 1:1 ratio, ticagrelor (180 mg loading dose, 90 mg twice daily thereafter) or clopidogrel (600 mg loading dose, 75 mg daily thereafter). The primary safety outcome was clinically significant bleeding (a composite of major bleeding or minor bleeding according to PLATO (Platelet Inhibition and Patient Outcomes) criteria at 12 months. Results: At 12 months, the incidence of clinically significant bleeding was significantly higher in the ticagrelor group than in the clopidogrel group (11.7% [45/400] vs 5.3% [21/400]; hazard ratio [HR], 2.26; 95% confidence interval [CI], 1.34 to 3.79; P =0.002). The incidences of major bleeding (7.5% [29/400] vs 4.1% [16/400], P =0.04) and fatal bleeding (1% [4/400] vs 0%, P =0.04) were also higher in the ticagrelor group. The incidence of death from cardiovascular causes, myocardial infarction, or stroke was not significantly different between the ticagrelor group and the clopidogrel group (9.2% [36/400] vs 5.8% [23/400]; HR, 1.62; 95% CI, 0.96 to 2.74; P =0.07). Overall safety and effectiveness findings were similar with the use of several different analytic methods and in multiple subgroups. Conclusions: In Korean acute coronary syndrome patients intended to receive early invasive management, standard-dose ticagrelor as compared with clopidogrel was associated with a higher incidence of clinically significant bleeding. The numerically higher incidence of ischemic events should be interpreted with caution, given the present trial was underpowered to draw any conclusion regarding efficacy. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02094963.

Using a Langmuir probe and an energy-resolved ion mass spectrometer, gas-phase kinetics of fluorocarbon ions has been investigated as a function of the Ar percentage in a mixed CF4/Ar plasma. Spatially resolved electron energy distribution function, plasma potential, and ion density are measured in an inductively coupled plasma. As the Ar percentage increases, the average electron energy decreases while the electron density remains flat. The ion density also stays constant at low Ar percentages but increases over the Ar percentage larger than 63% mainly due to the increase of the Ar+ density. The plasma potential decreases as a result of the increase of Ar partial pressure, and this is confirmed by measuring the ion energy distributions of argon and fluorocarbon ions using the ion mass spectrometry. With the mass spectrometry, it is found that the most prominent ions, CF3+ and CF+, are formed predominantly by a process of dissociative ionization or radical ionization while CF2+ ions are formed dominantly by a process of charge transfer. As a practical application of this study for SiO2 etching, the densities of the fluorocarbon ions and radicals are correlated with the SiO2 etch rate and its selectivity to photoresist. Microtrench profile is also investigated as a function of Ar percentage and it is observed that the microtrench tends to be suppressed with the increase of the Ar percentage. This tendency is correlated with changes in the plasma chemistry as the Ar percentage increases.

Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. Methods To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. Results CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. Conclusions CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

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