PurposeThe prediction of breast cancer recurrence is a crucial factor for successful treatment and follow-up planning. The principal objective of this study was to construct a novel prognostic model based on support vector machine (SVM) for the prediction of breast cancer recurrence within 5 years after breast cancer surgery in the Korean population, and to compare the predictive performance of the model with the previously established models.MethodsData on 679 patients, who underwent breast cancer surgery between 1994 and 2002, were collected retrospectively from a Korean tertiary teaching hospital. The following variables were selected as independent variables for the prognostic model, by using the established medical knowledge and univariate analysis: histological grade, tumor size, number of metastatic lymph node, estrogen receptor, lymphovascular invasion, local invasion of tumor, and number of tumors. Three prediction algorithms, with each using SVM, artificial neural network and Cox-proportional hazard regression model, were constructed and compared with one another. The resultant and most effective model based on SVM was compared with previously established prognostic models, which included Adjuvant! Online, Nottingham prognostic index (NPI), and St. Gallen guidelines.ResultsThe SVM-based prediction model, named 'breast cancer recurrence prediction based on SVM (BCRSVM),' proposed herein outperformed other prognostic models (area under the curve=0.85, 0.71, 0.70, respectively for the BCRSVM, Adjuvant! Online, and NPI). The BCRSVM evidenced substantially high sensitivity (0.89), specificity (0.73), positive predictive values (0.75), and negative predictive values (0.89).ConclusionAs the selected prognostic factors can be easily obtained in clinical practice, the proposed model might prove useful in the prediction of breast cancer recurrence. The prediction model is freely available in the website (http://ami.ajou.ac.kr/bcr/).

Tungsten oxide−zirconia catalysts were prepared by drying powdered Zr(OH)4 with ammonium metatungstate aqueous solution, followed by calcining in air at high temperature. Characterization of prepared catalysts was performed by using Fourier transform infrared (FTIR), Raman, and X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and differential scanning calorimetry (DSC) and by measuring surface area. The addition of tungsten oxide up to 20 wt % to zirconia shifted the phase transition of ZrO2 from amorphous to tetragonal to higher temperature due to the interaction between tungsten oxide and zirconia, and the specific surface area and acidity of catalysts increased in proportion to the tungsten oxide content. Since the ZrO2 stabilizes the tungsten oxide species, for the samples equal to or less than 5 wt %, tungsten oxide was well dispersed on the surface of zirconia, but for the samples containing 13 wt % or above 13 wt %, the triclinic phase of WO3 was observed at any calcination temperature. Upon the addition of only small amount of tungsten oxide (1 wt % WO3) to ZrO2, both the acidity and the acid strength of catalyst increased remarkably, showing the presence of Brönsted and Lewis acid sites on the surface of WO3/ZrO2. The high acid strength and high acidity were responsible for the WO bond nature of complex formed by the interaction between WO3 and ZrO2.

Purpose Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool. Methods We developed an algorithm referred to as the Comparison on Extreme Laboratory Test results, which takes an extreme representative value pair according to the types of laboratory abnormalities on the basis of each patient's medication point. We used 10 years' EMR data from a tertiary teaching hospital, containing 32 033 710 prescriptions and 115 241 147 laboratory tests for 530 829 individual patients. Ten drugs were selected randomly for analysis, and 51 laboratory values were matched. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were calculated. Results The mean number of detected laboratory abnormality signals for each drug was 27 (±7.5). The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 64 -100%, 22 -76%, 22 -75%, and 54 -100%, respectively. Conclusions The results of this study demonstrated that the Comparison on Extreme Laboratory Test results algorithm described herein was extremely effective in detecting the signals characteristic of adverse drug reactions. This algorithm can be regarded as a useful signal detection tool, which can be routinely applied to EMR data.

ObjectivesA distributed research network (DRN) has the advantages of improved statistical power, and it can reveal more significant relationships by increasing sample size. However, differences in data structure constitute a major barrier to integrating data among DRN partners. We describe our experience converting Electronic Health Records (EHR) to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM).MethodsWe transformed the EHR of a hospital into Observational Medical Outcomes Partnership (OMOP) CDM ver. 4.0 used in OHDSI. All EHR codes were mapped and converted into the standard vocabulary of the CDM. All data required by the CDM were extracted, transformed, and loaded (ETL) into the CDM structure. To validate and improve the quality of the transformed dataset, the open-source data characterization program ACHILLES was run on the converted data.ResultsPatient, drug, condition, procedure, and visit data from 2.07 million patients who visited the subject hospital from July 1994 to November 2014 were transformed into the CDM. The transformed dataset was named the AUSOM. ACHILLES revealed 36 errors and 13 warnings in the AUSOM. We reviewed and corrected 28 errors. The summarized results of the AUSOM processed with ACHILLES are available at http://ami.ajou.ac.kr:8080/.ConclusionsWe successfully converted our EHRs to a CDM and were able to participate as a data partner in an international DRN. Converting local records in this manner will provide various opportunities for researchers and data holders.

Although solution-processable high-k inorganic dielectrics have been implemented as a gate insulator for high-performance, low-cost transition metal oxide field-effect transistors (FETs), the high-temperature annealing (>300 °C) required to achieve acceptable insulating properties still limits the facile realization of flexible electronics. This study reports that the addition of a 2-dimetylamino-1-propanol (DMAPO) catalyst to a perhydropolysilazane (PHPS) solution enables a significant reduction of the curing temperature for the resulting SiO2 dielectrics to as low as 180 °C. The hydrolysis and condensation of the as-spun PHPS film under humidity conditions were enhanced greatly by the presence of DMAPO, even at extremely low curing temperatures, which allowed a smooth surface (roughness of 0.31 nm) and acceptable leakage characteristics (1.8 × 10(-6) A/cm(2) at an electric field of 1MV/cm) of the resulting SiO2 dielectric films. Although the resulting indium zinc oxide (IZO) FETs exhibited an apparent high mobility of 261.6 cm(2)/(V s), they suffered from a low on/off current (ION/OFF) ratio and large hysteresis due to the hygroscopic property of silazane-derived SiO2 film. The ION/OFF value and hysteresis instability of IZO FETs was improved by capping the high-k LaZrOx dielectric on a solution-processed SiO2 film via sol-gel processing at a low temperature of 180 °C while maintaining a high mobility of 24.8 cm(2)/(V s). This superior performance of the IZO FETs with a spin-coated LaZrOx/SiO2 bilayer gate insulator can be attributed to the efficient intercalation of the 5s orbital of In(3+) ion in the IZO channel, the good interface matching of IZO/LaZrOx and the carrier blocking ability of PHPS-derived SiO2 dielectric film. Therefore, the solution-processable LaZrOx/SiO2 stack can be a promising candidate as a gate dielectric for low-temperature, high-performance, and low-cost flexible metal oxide FETs.